biology

Article Exploring the Role of Stress in Hepatocellular Carcinoma through mining of the Human Atlas

Nataša Pavlovi´cand Femke Heindryckx *

Department of Medical Cell Biology, Uppsala University, 751 23 Uppsala, Sweden; [email protected] * Correspondence: [email protected]

Simple Summary: Hepatocellular carcinoma is a highly deadly primary liver cancer. It is usually diagnosed at a late stage, when therapeutic options are scarce, and the lack of predictive biomarkers poses a challenge for early detection. A known hallmark of hepatocellular carcinoma is the accu- mulation of misfolded in the endoplasmic reticulum (ER), known as ER-stress. Growing experimental evidence suggests that ER-stress is involved in liver cancer initiation and progression. However, it remains unclear if ER-stress markers can be used as therapeutic targets or biomarkers for patients with liver cancer. In this study, we evaluated the prognostic value of proteins involved in managing ER-stress in liver cancer by mining a publicly available patient-derived database, the Human Protein Atlas. We thereby identified 44 ER-stress-associated proteins as prognostic markers in liver cancer. Furthermore, we discussed the expression of these markers in relation to disease stage, age, sex, ethnicity, and tissue localization.

  Abstract: Endoplasmic reticulum (ER) stress and actors of unfolded protein response (UPR) have

Citation: Pavlovi´c,N.; Heindryckx, F. emerged as key hallmarks of hepatocarcinogenesis. Numerous reports have shown that the main Exploring the Role of Endoplasmic actors in the UPR pathways are upregulated in HCC and contribute to the different facets of tumor Reticulum Stress in Hepatocellular initiation and disease progression. Furthermore, ER-stress inducers and inhibitors have shown Carcinoma through the Mining of the success in preclinical HCC models. Despite the mounting evidence of the UPR’s involvement in Human Protein Atlas. Biology 2021, HCC pathogenesis, it remains unclear how ER-stress components can be used safely and effectively 10, 640. https://doi.org/10.3390/ as therapeutic targets or predictive biomarkers for HCC patients. In an effort to add a clinical biology10070640 context to these findings and explore the translational potential of ER-stress in HCC, we performed a systematic overview of UPR-associated proteins as predictive biomarkers in HCC by mining the Academic Editor: Natalia Osna Human Protein Atlas database. Aside from evaluating the prognostic value of these markers in HCC, we discussed their expression in relation to patient age, sex, ethnicity, disease stage, and tissue Received: 16 June 2021 localization. We thereby identified 44 UPR-associated proteins as unfavorable prognostic markers in Accepted: 7 July 2021 HCC. The expression of these markers was found to be higher in tumors compared to the stroma of Published: 9 July 2021 the hepatic HCC patient tissues.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Keywords: endoplasmic reticulum stress; unfolded protein response; hepatocellular carcinoma published maps and institutional affil- iations.

1. Introduction Hepatocellular carcinoma (HCC) is a the most common type of primary liver cancer Copyright: © 2021 by the authors. and usually develops in a background of chronic liver disease and inflammation. With Licensee MDPI, Basel, Switzerland. global HCC incidence and mortality consistently rising, advanced screening programs, This article is an open access article novel predictive biomarkers, and new therapeutic options for advanced HCC are desper- distributed under the terms and ately needed [1]. Further research on the molecular pathogenesis of HCC progression is conditions of the Creative Commons therefore encouraged, as this could eventually lead to the discovery of new biomarkers for Attribution (CC BY) license (https:// improved HCC surveillance and prevention, as well as new therapeutic targets for treating creativecommons.org/licenses/by/ patients with liver cancer. 4.0/).

Biology 2021, 10, 640. https://doi.org/10.3390/biology10070640 https://www.mdpi.com/journal/biology Biology 2021, 10, 640 2 of 15

The unfolded protein response (UPR) is a conserved cellular survival strategy which is activated in response to the accumulation of unfolded or misfolded proteins in the endo- plasmic reticulum (ER) lumen, an event known as ER-stress. Factors such as pathogens, mutations, nutrient deprivation, hypoxia, and an increased metabolic rate lead to an in- crease in the cell’s protein secretory load. This impairs the ER’s capacity to accurately monitor and synthesis, which induces UPR signaling through three major ER transducers—IRE1α, PERK, and ATF6—with the goal of reestablishing ER homeostasis. However, sustained ER-stress and prolonged UPR signaling can activate pro-apoptotic signaling pathways [2,3]. Chronic ER-stress and increased UPR activity have long been reported as the hall- marks of solid tumors, including HCC. Upregulated UPR signaling has been noted in the majority of human HCCs, irrespective of grade or stage [4]. Our group recently reported that inhibiting the IRE1α-mediated ER-stress pathway significantly reduced tumor burden in a chemically induced fibrotic HCC mouse model [5]. This pathway was also found to be crucial for liver cirrhosis progression in vivo [6]. The PERK- and ATF6-mediated pathways have also been widely implicated in HCC progression by increasing HCC chemoresistance and promoting tumor invasiveness and adaptation to hostile microenvironmental con- ditions [7,8]. However, despite mounting reports on the involvement of ER-stress in the different facets of HCC pathogenesis and the success of ER-stress inhibitors and inducers in pre-clinical HCC models [9], the translational potential of targeting ER-stress actors has yet to be fully assessed and utilized. Therefore, in the present study, our aim was to perform a systematic overview of UPR-associated proteins as prognostic markers in HCC through mining the publicly available Human Protein Atlas database [10,11]. Here, we showed that the expression of 44 UPR-associated proteins significantly correlated with poor prognosis in patients with HCC, marking them as clinically relevant biomarkers in the treatment of liver cancer. Furthermore, we presented and discussed the expression of these markers in relation to disease stage, age, sex, ethnicity, and tissue localization in an attempt to better understand how the detrimental effect ER-stress imposes on hepatocarcinogenesis could be effectively targeted and utilized in HCC treatment.

2. Materials and Methods 2.1. Selection of UPR A -set of 129 proteins involved in the UPR was generated from online databases: the Harmonizome [12,13] and STRING [14,15] (Figures1 and2, Supplementary Table S1). The publicly available database from the Human Protein Atlas was then used to deter- mine which of these genes were prognostic markers in patients with liver cancer [10,11] (Figure3). Biology 2021, 10, x FOR PEER REVIEW 3 of 17 Biology 2021, 10, 640 3 of 15

BiologyFigure 2021 1., 10The, x FOR method PEER usedREVIEW for selecting UPR-associated genes, determining their prognostic value in HCC, and comparing4 of 17 Figure 1. The method used for selecting UPR-associated genes, determining their prognostic value and presenting their expression to different patient variables. in HCC, and comparing and presenting their expression to different patient variables.

FigureFigure 2. 2.An An interconnected interconnected gene gene mapmap of the entire UPR-associat UPR-associateded gene-set gene-set derived derived from from STRING STRING and and the theHarmonizome. Harmonizome.

Biology 2021, 10, x FOR PEER REVIEW 5 of 17 Biology 2021, 10, 640 4 of 15

Figure 3. An interconnected gene map of UPR-associated genes with prognostic value in HCC. Figure 3. An interconnected gene map of UPR-associated genes with prognostic value in HCC.

2.2.2.2. The The Human Human Protein Protein Atlas Atlas Data Data Mining Mining PatientPatient survival survival was was correlated correlated to tothe the ex expressionpression levels levels of ofthe the selected selected 129 129 UPR- UPR- associatedassociated markers markers by usingusing publicly publicly available available data data from from the Humanthe Human Protein Protein Atlas. PatientsAtlas. Patientswere classified were classified into two into expression two expression groups based groups on based the fragments on the fragments per kilobase per of kilobase exon per ofmillion exon per reads million (FPKM) reads value (FPKM) of each value gene. of Theeach expressions gene. The ofexpressions UPR-associated of UPR-associated markers in the markersdifferent in tumorthe different stages tumor of HCC stages were of taken HCC from were the taken Human from Protein the Human Atlas databaseProtein Atlas using databasethe RNA using sequence the dataRNA from sequence 365 patients data fr derivedom 365 from patients the Cancer derived Genome from Projectthe Cancer [16]. Genome Project [16]. 2.3. Evaluating the Tumor/Stroma Expression of Unfavorable HCC Prognostic Markers 2.3. EvaluatingImages ofthe healthy Tumor/Stroma livers and Expressi HCCon biopsies of Unfavorable stained HCC with Prognostic antibodies Markers against the UPR- associatedImages of unfavorable healthy livers HCC and prognostic HCC biopsies markers stained were with derived antibodies from the against Human the ProteinUPR- Atlas. Five replicates stained with the antibody with the strongest staining intensity were associated unfavorable HCC prognostic markers were derived from the Human Protein selected for each marker. The staining intensity was then scored on a scale from 0 to 3, with Atlas. Five replicates stained with the antibody with the strongest staining intensity were 0 corresponding to no staining detected, 1 to weak staining, 2 to moderate staining, and selected for each marker. The staining intensity was then scored on a scale from 0 to 3, 3 to the strongest staining intensity on healthy, stromal, and tumoral tissues. The biopsy with 0 corresponding to no staining detected, 1 to weak staining, 2 to moderate staining, staining was scored by the authors independently, after which the average scores were and 3 to the strongest staining intensity on healthy, stromal, and tumoral tissues. The generated. biopsy staining was scored by the authors independently, after which the average scores were generated.

Biology 2021, 10, 640 5 of 15

2.4. Statistical Analysis The dataset derived from the Human Protein Atlas was imported into GraphPad Prism 8. The statistical significance between different experimental groups (age, sex, racial ethnicity, and HCC stage) was determined through a two-way ANOVA using the Dunn– Šidák correction for multiple comparisons. A volcano plot was generated after performing a multiple t-test on the different stages of HCC in order to visualize the ER-stress genes with the highest fold-change and statistically significant differences. The correlation between the biomarker expression level and patient survival was examined using GraphPad Prism 8. Based on the data from the Human Protein Atlas, the FPKM value of the appropriate gene was used as a cut-off to determine high or low expression. The FPKM/cut-off value corresponded to the best expression cut-off level, meaning that the FPKM value that would yield the maximum difference in regard to survival between the two groups at the lowest log-rank p-value. Statistical significance between the groups of the Kaplan–Meier curves was determined with a log-rank test. p-values of <0.05 were considered statistically significant.

3. Results 3.1. The Predictive Value UPR-Associated Protein Expression in HCC A systematic overview of UPR-associated proteins as prognostic markers for HCC was performed using the publicly available Human Protein Atlas database (Figure1). This search identified 44 out of 129 selected ER-stress genes as unfavorable prognostic markers in HCC and 1 as a favorable prognostic marker (Figures2–4, Supplementary Tables S1 and S2). We compared the mRNA expression of these 45 markers in the different stages of HCC, which revealed that the majority of UPR-associated genes had the highest expression in disease stages 2 and 3 (Figure4A,B). Notably, the expression of markers RACK1, ATF4, GANAB, PDIA6, SSR2, EIF2S3, CHOP, TRIB3, and CALU, known to be strongly associated with HCC invasiveness and chemoresistance [17–20], was shown to significantly increase with the progression of HCC to stages 2 and 3 (Figure4D–L). The correlation between the expression of these biomarkers and patient survival is shown in Figure5. It should, however, be pointed out that the decreased expression of prognostic markers in stage 4 could be explained by there being less available patient-derived data from this HCC stage. Biology 2021, 10, x FOR PEER REVIEW 7 of 17 Biology 2021, 10, 640 6 of 15

DDX11 A Stage I B FKBP14 Stage II RACK1 DDX11 Stage III FKBP14 ATF4 EXTL3 Stage IV ASNS GANAB DCP2 ERP29 JNK1 DNAJB6 TPP1 GOSR2 PDIA6 EXOSC2 EXOSC9 SSR2 EXOSC3 PPP2R5B EIF2S3 ZBTB17 CHOP PSEN1 DNAJC21 TRIB3 DNAJC7 TRAF3 SRPRB CXXC1 DNAJC1 EIF2S1 ERO1A CALU PIK3R1 DNAJC5 PPP1CB 100 GSK3A EIF2A PPP1CC GADD34 SSR1 KDELR3 SEC31A SEC31A SSR1 KDELR3 EIF2A PPP1CB GADD34 CALU PPP1CC DNAJC1 SRPRB GSK3A TRIB3 CHOP DNAJC5 EIF2S3 PIK3R1 SSR2 PDIA6 ERO1A TPP1 ERP29 EIF2S1 GANAB CXXC1 ATF4 RACK1 TRAF3 0 50 100 150 200 DNAJC7 DNAJC21 Volcano Plot 50 C 1.0 PSEN1

DNAJC7 ZBTB17 PPP2R5B 2 ZBTB17 0.8 EXOSC3 GSK3A EXOSC9

SSR1 EXOSC2 0.6 GOSR2 PPP1CC CXXC1 DNAJB6 PDIA6

SRPRB 1 EXOSC9 ASNS JNK1 0.4 GOSR2 EIF2S3 TRAF3 DCP2 SSR2 ERO1A -log10(adjusted value)P

EXOSC3 CHOP ASNS PPP1CB ATF4 DNAJC5 PPP2R5B EXOSC2 0.2 EXTL3 CALU EIF2A DDX11FKBP14 RACK1 TRIB3 DDX11 EXTL3 DNAJB6 GANAB ERP29 EIF2S1 TPP1 DNAJC1 JNK1 PIK3R1 FKBP14 FKBP14 GADD34 DNAJC21 0 KDELR3 SEC31A PSEN1 DCP2 DDX11 0.0 -25 -20 -15 -10 -5 0 5 FKBP14 Difference DDX11

Stage I Stage II Stage III Stage IV

RACK1 ATF4 GANAB PDIA6 SSR2 EIF2S3 CHOP TRIB3 CALU DEFGHI JKL 0.0030 0.0004 0.0018 <0.0001 <0.0001 0.0071 0.0130 0.0007 0.0014 0.0222 80 0.0115 0.0002 200 150 <0.0001 0.0007 100 80 <0.0001 50 0.0218 <0.0001 40 40 30

80 60 40 150 60 30 30 100 20 60 30 100 40 40 20 20 40 20 50 10 50 20 20 10 10 20 10 (FPKM) expression Gene Gene expression (FPKM) Gene expression Geneexpression (FPKM) Gene expression (FPKM) expression Gene Gene expression (FPKM) expression Gene Gene expression (FPKM) expression Gene Gene expression (FPKM) expression Gene Gene expression (FPKM) expression Gene Gene expression (FPKM) expression Gene 0 0 0 0 0 0 0 0 0 1 4 1 2 3 4 1 3 3 1 4 1 4 e e e e 2 e ge 3 g g g g e g age a a a a a age g a age 2 age 1 age 2 age 3 age 4 a age age 2 t t tage 3 tage age 4 St Stage 2 St St St St St St Stage Stage 2 Stage Stage 4 tage 2 tage 3 t tage 3 S S S S tage 3 Stage 1 S S Stage 4 St St St S Stage 1 St St Stage 4 Stage St S Stage Stage 1 Stage 2 S St

Figure 4. The mRNA expression of unfavorable UPR-associated markers per HCC stage shown in a bar chart (A), a heatmap (BFigure), and a4. volcanoThe mRNA plot (Cexpression). Markers of with unfavorable statistically UPR-associated significant expression markers levels per betweenHCC stage the differentshown in stages a bar are chart shown (A), as a individualheatmap ( barB), and charts a volcano (D–L). p plot-values (C). of Markers <0.05 were with considered statistically statistically significant significant. expression levels between the different stages are shown as individual bar charts (D–L). p-values of <0.05 were considered statistically significant.

Biology 2021, 10, x FOR PEER REVIEW 8 of 17 Biology 2021, 10, 640 7 of 15

A Percentage 5-year survival DERACK1 ATF4 FGANAB off ut- -value High expression Low expression C P Low expression Low expression Low expression 100 100 100 High expression RACK1 29 57 143,61 0,000003 High expression High expression ATF4 37 53 110,59 0,00041 GANAB 33 57 80,91 0,000013 ERP29 37 54 70,28 0,0002 TPP1 30 54 60,18 0,00011 50 50 50 PDIA6 34 52 67,93 0,000036 SSR2 42 63 25,82 0,00029 EIF2S3 37 52 40,2 0,0003 Probability of Survival Probability Probability of Survival EIF2AK1 37 52 34,16 0,0007 Probabilityof Survival 0 0 EIF2S2 31 56 30,47 0,000032 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 CHOP 43 66 11,68 0,00066 Time TRIB3 33 56 23,18 0,000067 Time (days) Time (days) SRPRB 29 53 28,3 0,000031 DNAJC1 25 55 23,83 0,00002 GHPDIA6 SSR2 IEIF2S3 CALU 34 52 24,34 0,00018 Low expression PPP1CB 36 57 18,98 0,0000065 Low expression Low expression 100 100 100 High expression EIF2A 34 54 18,27 0,000024 High expression High expression SSR1 43 53 15,18 0,00049 SEC31A 36 52 19,45 0,00033 KDELR3 34 67 6,64 0,00012 GADD34 34 54 13,09 0,00019 50 50 50 PPP1CC 39 53 13,38 0,0000027 GSK3A 33 52 13,94 0,00011 DNAJC5 36 54 11,76 0,0000088 Probability of Survival Probability of Survival Probability of PIK3R1 57 34 5,36 0,00081 Probability of Survival 0 ERO1 alpha 36 56 8,44 0,00076 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 EIF2S1 25 53 9,78 <0,000001 0 1000 2000 3000 4000 days CXXC1 32 52 9,58 0,0000042 Time (days) Time (days) TRAF2 35 51 10,53 0,00031 DNAJC7 39 56 5,42 0,00033 TRIB3 CALU DNAJC21 37 51 5,9 0,000024 JKCHOP L Low expression PSEN1 29 54 5,76 0,00002 Low expression Low expression 25 52 100 High expression 100 ZBTB17 5,39 0,0000024 100 High expression High expression PPP2R5B 31 52 5,1 0,000032 EXOSC3 32 53 4,53 0,00007 EXOSC9 43 55 3,13 0,0001 EXOSC2 38 52 4,46 0,000014 50 50 GOSR2 41 51 3,97 0,00038 50 DNAJB6 29 54 3,96 0,000011

JNK1 34 51 3,06 0,00087 Probability of Survival Probability DCP2 37 53 2,6 0,00048 Probability of Survival Probability of Survival 23 56 2,59 0 0 ASNS 0,0000094 0 40 52 0 1000 2000 3000 4000 0 1000 2000 3000 4000 EXTL3 2,34 0,000011 0 1000 2000 3000 4000 FKBP14 38 51 1,95 0,00069 Time (days) Time Days DDX11 39 52 1,77 0,00024

30% 40% 50% 60%

BC High expression 80 80 <0.0001 Low expression

60 60

40 40

20 20 5 year survival (Percentage) 5-year survival (Percentage)survival 5-year 0 0 High expression Low expression 1 B 2 1 1 U 1 A 3 C 5 1 7 1 1 7 B 9 2 2 1 3 1 K A A6 K OP IB3 L 2A 34 1 5 K P2 NS L C TF4N PP1 A 2S2H PRB A F D 1C JC 2S1 JC C S P14 A T SSR F C TR C EI SSR D A K3R1 alphaF XXC A JN D A B RA ERP29 PDI EIF2S3F2 EI GSK3A EI C TRAF2 AJC2PSEN EXT K DDX1 GA SR DNAJC PPP1CB SEC31KDELR PPP DN PI DN N ZBTB GOSRDNAJB6 F EI GA D PPP2REXOSC3EXOSCEXOSC ERO1

Figure 5. Five-year HCC patient survival data correlated to the expression of unfavorable UPR-associated markers (A–C). TheFigure selected 5. Five-year markers HCC are shown patient as survival individual data Kaplan–Meier correlated to curvesthe expression (D–L). of unfavorable UPR-associated markers (A–C). The selected markers are shown as individual Kaplan–Meier curves (D–L).

3.2.3.2. The The Expression Expression of of UPR-Associated UPR-Associated Markers Markers in in Relation Relation to to Sex, Sex, Age, Age, and and Ethnicity Ethnicity in in HCC HCC CancerCancer progression progression and and treatment treatment efficacy efficacy are knownare known to be to impacted be impacted by different by different host characteristics,host characteristics, including including patient sexpatient and age sex [21 and]. Furthermore, age [21]. Furthermore, the etiological the backgrounds etiological ofbackgrounds HCC vary significantlyof HCC vary between significantly the Westernbetween worldthe Western and East world Asia and [22 East]. Therefore, Asia [22]. throughTherefore, mining through the Humanmining the Protein Human Atlas Protein database, Atlas we database, aimed to we evaluate aimed to the evaluate potential the correlationpotential betweencorrelation the expressionbetween the of the expressi UPR-associatedon of the unfavorable UPR-associated prognostic unfavorable markers inprognostic HCC with markers patient sex,in HCC age, andwith ethnicity. patient sex, Overall, age, noand significant ethnicity. differences Overall, no were significant noted whendifferences comparing were the noted expression when comparing of UPR markers the expression between of sexes UPR in markers HCC patients, between with sexes the in exceptionHCC patients, of the with markers the EIF2S3,exception SSR2, of the and markers ATF4, where EIF2S3, a higherSSR2, and expression ATF4, where was observed a higher inexpression female patients was (Figureobserved6A,B). in Similarly,female patien no notablets (Figure differences 6A,B). wereSimilarly, observed no whennotable comparingdifferences the were UPR observed marker when expressions comparing between the UPR three marker different expressions age groups between (<40, 41 three to 65,different and >66; age Figure groups7A,B), (<40, with 41 theto 65, exception and >66; of theFigure markers 7A,B), RACK1, with the ATF4, exception and CHOP of the (Figuremarkers7C,D,K). RACK1, Finally, ATF4, whenand CHOP comparing (Figure the 7C,D mRNA,K). Finally, expression when of comparing the UPR the markers mRNA inexpression HCC between of the three UPR ethnic markers categories in HCC (Asian,between Black three or ethnic African categories American, (Asian, and Black White; or FigureAfrican8), American,differences and in expression White; Figure were 8), observed differences for markers in expression RACK1, were ATF4, observed and SSR2, for withmarkers the common RACK1, trend ATF4, of theand expression SSR2, with being the elevatedcommon in trend the Asian of the group expression compared being to theelevated Black orin Africanthe Asian American group compared and White to groupsthe Black (Figure or African8C,D,G). American and White groups (Figure 8C,D,G).

BiologyBiology2021, 202110, 640, 10, x FOR PEER REVIEW 9 of 17 8 of 15

AB Female vs Male RACK1 ATF4 DDX11 Female GANAB FKBP14 Male ERP29 EXTL3 TPP1 ASNS PDIA6 DCP2 SSR2 JNK1 EIF2S3 DNAJB6 CHOP GOSR2 TRIB3 EXOSC2 SRPRB EXOSC9 100 EXOSC3 DNAJC1 PPP2R5B CALU ZBTB17 PPP1CB PSEN1 EIF2A DNAJC21 SSR1 DNAJC7 SEC31A TRAF3 KDELR3 CXXC1 GADD34 EIF2S1 PPP1CC ERO1A GSK3A PIK3R1 DNAJC5 DNAJC5 PIK3R1 GSK3A ERO1A PPP1CC EIF2S1 GADD34 CXXC1 KDELR3 TRAF3 SEC31A DNAJC7 50 SSR1 DNAJC21 EIF2A PSEN1 PPP1CB CALU ZBTB17 DNAJC1 PPP2R5B SRPRB EXOSC3 TRIB3 EXOSC9 CHOP 0.0430

EXOSC2 0.0058 EIF2S3 GOSR2 SSR2 DNAJB6 PDIA6 JNK1 TPP1 DCP2 ERP29

ASNS GANAB 0.0013 EXTL3 ATF4 FKBP14 RACK1 DDX11 0 50 100 150 Biology 2021, 10, x FOR PEER REVIEWFemale Male 10 of 17

FigureFigure 6. The 6. The mRNA mRNA expression expression of of unfavorable unfavorable UPR-associated UPR-associated markers per per sex sex shown shown in in a aheatmap heatmap (A ()A and) and a bar a bar chart chart (B). (B).

AB DDX11 <40 RACK1 FKBP14 ATF4 41 - 65 EXTL3 GANAB > 66 ASNS ERP29 DCP2 TPP1 PDIA6 JNK1 SSR2 DNAJB6 EIF2S3 GOSR2 CHOP EXOSC2 TRIB3 EXOSC9 SRPRB EXOSC3 DNAJC1 PPP2R5B 100 CALU ZBTB17 PPP1CB PSEN1 EIF2A DNAJC21 SSR1 DNAJC7 SEC31A TRAF3 KDELR3 CXXC1 GADD34 EIF2S1 PPP1CC ERO1A GSK3A PIK3R1 DNAJC5 DNAJC5 PIK3R1 GSK3A ERO1A PPP1CC EIF2S1 GADD34 CXXC1 KDELR3 TRAF3 SEC31A DNAJC7 50 SSR1 DNAJC21 EIF2A PSEN1 PPP1CB ZBTB17 CALU PPP2R5B DNAJC1 EXOSC3 SRPRB EXOSC9 TRIB3 EXOSC2 CHOP GOSR2 EIF2S3 DNAJB6 SSR2 JNK1 PDIA6 DCP2 TPP1 ASNS ERP29 EXTL3 GANAB FKBP14 ATF4 DDX11 RACK1 40 41 to 65 66 0 50 100 150 200 Gene expression (FPKM)

RACK1 ATF4 GANAB ERP29 TPP1 PDIA6 SSR2 EIF2S3 CHOP CDE0.0003 FGHIJ K 0.0191 0.0111

150 150 100 50 60 0.0041 0.0390 80 60 80 50

80 40 40 60 60 100 100 40 40 60 30 30 40 40 40 20 20 50 50 20 20 20 20 20 10 10 Gene expressionGene (FPKM) Gene expression Gene (FPKM) expression Gene expression(FPKM) Gene expression (FPKM) Gene Geneexpression (FPKM) Gene expressionGene (FPKM) 0 0 0 0 (FPKM)expression Gene 0 (FPKM)expression Gene 0 (FPKM)expression Gene 0 0 0 0 6 0 6 5 0 6 0 5 0 6 6 6 0 4 6 4 6 40 6 66 4 6 4 6 66 4 65 6 40 6 40 6 4 66 to 65 to 65 to 65 to 65 to 1 1 to 1 1 1 to 1 to 65 4 41 4 4 4 4 41 to 65 41 4

FigureFigure 7. The 7.mRNA The mRNA expression expression of unfavorableof unfavorable UPR-associated UPR-associated markers markers per per age age group group shown shown in a inheatmap a heatmap (A) and (A )a andbar a bar chart (chartB). The (B). selected The selected markers markers are are shown show asn as individual individual barbar chartscharts ( (CC––KK).).

BiologyBiology 20212021,, 1010,, 640x FOR PEER REVIEW 119 of 1517

A B DDX11 Asian RACK1 FKBP14 Black or African American ATF4 EXTL3 White GANAB ASNS ERP29 DCP2 TPP1 JNK1 PDIA6 DNAJB6 SSR2 GOSR2 EIF2S3 EXOSC2 CHOP EXOSC9 TRIB3 EXOSC3 SRPRB PPP2R5B DNAJC1 ZBTB17 CALU PSEN1 PPP1CB DNAJC21 EIF2A 100 DNAJC7 SSR1 TRAF3 SEC31A CXXC1 KDELR3 EIF2S1 GADD34 ERO1A PPP1CC PIK3R1 GSK3A DNAJC5 DNAJC5 PIK3R1 GSK3A ERO1A PPP1CC EIF2S1 GADD34 CXXC1 KDELR3 TRAF3 SEC31A DNAJC7 SSR1 EIF2A DNAJC21 50 PSEN1 PPP1CB ZBTB17 CALU PPP2R5B DNAJC1 EXOSC3 SRPRB EXOSC9 TRIB3 EXOSC2 CHOP GOSR2 EIF2S3 DNAJB6 SSR2 JNK1 PDIA6 DCP2 TPP1 ASNS ERP29 EXTL3 GANAB FKBP14 ATF4 DDX11 RACK1

Asian Black or African American White 0 50 100 150 200 Gene Expression (FPKM)

CDERACK1 ATF4 GANAB FGHIJKPDIA6 SSR2 EIF2S3 CHOP TRIB3 CALU <0.0001 0.0482 80 60 30 60 30 200 0.0003 150 <0.0001 100 50

80 40 150 60 100 40 20 40 20 60 30 100 40 40 20 50 20 10 20 10 50 20 20 10 Gene expression(FPKM) Gene expression(FPKM) Gene expression(FPKM) Gene expression(FPKM) Gene expression(FPKM) Gene expression(FPKM) Gene expression(FPKM) Gene expression(FPKM) Gene expression(FPKM) 0 0 0 0 0 0 0 0 0 n e e e e n n n e it it ia ite h hit h s h ican sia ican sian rican rican rican Asian r White A Whit Asia W Asian Whit A W Asian e W Asian e White Asian e White A W e er merican m m American A A America Am A Am Am n n n n an an an an a ca c ric ri fric fric rica f f A Af A A Afri African American A r r r r r r Afric o o o or k o ck o ck or Africa ck ck ac ack a a lack o la lack or l Bl Bl B B B Black Bla Bl B

Figure 8. The mRNA expression of unfavorable UPR-associated markers per ethnic group shown in a heatmap (A) and a barFigure chart 8. (TheB). ThemRNA selected expression markers of shownunfavorable as individual UPR-associat bar chartsed markers (C–K). per ethnic group shown in a heatmap (A) and a bar chart (B). The selected markers shown as individual bar charts (C–K).

3.3. Tumor/Stroma ExpressionExpression ofof UPR-AssociatedUPR-Associated UnfavorableUnfavorable PrognosticPrognostic MarkersMarkers inin HCCHCC Metabolic and oncogenic abnormalities in both tumortumor cellscells andand thethe surroundingsurrounding stromal microenvironment are are known known inducers inducers of of ER-stress ER-stress [23]. [23 ].HCC HCC is characterized is characterized by byabundant abundant stroma stroma and anddevelops develops in a inbackground a background of sustained of sustained inflammation inflammation and fibrosis. and fi- brosis.Therefore, Therefore, tumor–stroma tumor–stroma interactions interactions are a key are aregulator key regulator of HCC of HCC pathogenesis pathogenesis and and response to treatment [1]. In order to determine the tumor–stroma distribution of response to treatment [1]. In order to determine the tumor–stroma distribution of the the UPR-associated biomarker expression in the patient HCC tissue, we scored the im- UPR-associated biomarker expression in the patient HCC tissue, we scored the munohistochemical staining intensity on HPA-derived images of healthy, stromal, and immunohistochemical staining intensity on HPA-derived images of healthy, stromal, and tumoral liver tissue (Figure9)[ 24–39]. Notably, we observed a trend of increased UPR tumoral liver tissue (Figure 9) [24–39]. Notably, we observed a trend of increased UPR marker protein expression in the tumorous tissue compared to stromal compartments, marker protein expression in the tumorous tissue compared to stromal compartments, where the expression was overall downregulated to the baseline levels of the healthy where the expression was overall downregulated to the baseline levels of the healthy tissue (Figure9A). Furthermore, upregulated protein expression in the tumorous tissue tissue (Figure 9A). Furthermore, upregulated protein expression in the tumorous tissue was observed for the actors of the eIF2α-ATF4- CHOP pathway, as well as markers such was observed for the actors of the eIF2α-ATF4- CHOP pathway, as well as markers such as RACK1, PDIA6, and ERO1α, all of which have been proposed as potential therapeutic as RACK1, PDIA6, and ERO1α, all of which have been proposed as potential therapeutic targets in HCC [17,20,40,41]. targets in HCC [17,20,40,41].

Biology 2021, 10, x 640 FOR PEER REVIEW 1210 of of 17 15

Healthy Biopsy Tumor Stroma Healthy Biopsy Tumor Stroma AB F RACK1 EIF2S3

C G CHOP ATF4

D H TRIB3 GANAB

E I SSR1 ERO1-alpha

Figure 9.9. TheThe histological histological scoring scoring of healthy,of healthy, stromal, stromal, and tumoraland tumoral patient patient tissue stainedtissue againststained unfavorableagainst unfavorable UPR-associated UPR- associatedmarkers (A markers). The selected (A). The markers selected are markers shown are as individual shown as individual images of healthyimages tissueof healthy and HCCtissue biopsy and HCC (B– Ibiopsy). (B–I).

4. Discussion 4. Discussion The actors of the UPR have been widely demonstrated to influence different facets of The actors of the UPR have been widely demonstrated to influence different facets of HCCHCC progression. progression. While While numerous numerous preclinical preclinical models models have have proposed proposed ER-stress ER-stress inhibitors andand inducersinducers asas a a promising promising therapeutic therapeutic strategy strategy for patientsfor patients with liverwith cancer,liver cancer, the transla- the translationaltional potential potential of targeting of targeting UPR signaling UPR issignaling still unclear. is still In this unclear. study, weIn investigatedthis study, we the investigatedprognostic value the prognostic of UPR-associated value of proteins UPR-associated in liver cancer proteins through in liver mining cancer the through Human miningProtein Atlasthe Human database. Protein Furthermore, Atlas database. in an effort Furthermore, to potentially in identifyan effort a subpopulationto potentially identifyof liver cancera subpopulation patients that of could liver benefit cancer from patients ER-stress-targeted that could benefit therapy, from the expressionER-stress- targetedof these markerstherapy, wasthe expression evaluated inof relationthese mark to patienters was sex, evaluated age, ethnicity, in relation and to disease patient stage. sex, age,The UPR-associatedethnicity, and proteindisease expressionstage. The was UPR- alsoassociated discussed protein in regard expression to its hepatic was tissue also discussedlocalization. in regard to its hepatic tissue localization. ThroughThrough miningmining the the Human Human Protein Protein Atlas, Atlas, 44 UPR-associated 44 UPR-associated proteins proteins were identified were identifiedas unfavorable as unfavorable prognostic prognostic markers in markers HCC. While in HCC. the majority While the of themajority UPRmarkers of the UPR that markerswere found that to were be prognostic found to be could prognostic not be co categorizeduld not be undercategorized one specific under UPRone specific branch, UPR sev- branch,eral observations several observations could be made could regarding be made theirregarding upregulation, their upregulation, correlating correlating with poor HCCwith poorprognosis. HCC Forprognosis. instance, For unfavorable instance, prognosticunfavorable markers prognostic belonging marker to thes belonging PERK-mediated to the PERK-mediatedUPR pathway, such UPR as pathway, eIF2α, ATF4, such GADD34, as eIF2α, andATF4, CHOP, GADD34, have been and widelyCHOP, reportedhave been as widelykey regulators reported of as HCC key chemosensitivity regulators of HCC and chemosensitivity apoptosis in targeted and HCCapoptosis therapy. in targeted Notably, HCCATF4, therapy. CHOP, Notably, and eIF2 ATF4, subunit CHOP, gamma and (EIF2S3) eIF2 subunit expression gamma were (EIF2S3) found expression to significantly were foundincrease to withsignificantly the advancement increase with of HCC the to adva laterncement stages, inof lineHCC with to later the possible stages, rolein line of thesewith themarkers possible in HCC role of chemoresistance these markers [in18 H,42CC–44 chemoresistance]. Moreover, CHOP [18,42–44]. was shown Moreover, to be strongly CHOP wasimplicated shown in hepaticto be inflammation,strongly implicated fibrosis, andin hepatocarcinogenesis,hepatic inflammation, while fibrosis, CHOP-null and hepatocarcinogenesis,mice were found to be while resistant CHOP-null to chemically mice we inducedre found HCC to [be45 ].resistant Another to unfavorable chemically inducedmarker mediated HCC [45]. by Another the PERK unfavorable ER-stress branch, markerER mediated resident by oxidase the PERK 1-alpha ER-stress (ERO1- branch,α), has ERbeen resident found oxidase to play a1-alpha key role (ERO1- in HCCα), invasion, has been metastasis,found to play and a angiogenesiskey role in HCC by triggeringinvasion, metastasis,the S1PR1/STAT3/VEGF-A and angiogenesis signalingby triggering pathway, the S1PR1/STAT3/VEGF-A both in vitro and in vivo signaling [41]. pathway, both inReceptor vitro and of activatedin vivo [41]. protein 1 (RACK1) was shown to play a crucial role in the activationReceptor of of activated IRE1α signaling protein inkinase response 1 (RACK1) to the sorafenib was shown treatment to play ofa crucial HCC cells, role asin theRACK1 activation overexpression of IRE1α ledsignaling to the increasedin response to the sorafenib of treatment IRE1α and of enhanced HCC cells, XBP1 as RACK1splicing, overexpression thereby protecting led HCCto the cells increased from sorafenib-induced phosphorylation apoptosisof IRE1α [and17]. Moreover,enhanced XBP1our analysis splicing, of RACK1thereby expression protecting between HCC thecells different from sorafenib-induced stages of HCC showed apoptosis a significant [17]. Moreover,increase between our analysis stage 1of and RACK1 stages expression 2 and 3, adding between to thethe relevancedifferent stages of potentially of HCC targeting showed aRACK1 significant in advanced increase HCC. between stage 1 and stages 2 and 3, adding to the relevance of potentiallySignal targeting sequence RACK1 receptor in subunit advanced 2 (SSR2) HCC. was found to be overexpressed in patient HCCSignal tissue, sequence with increased receptor expression subunit 2 significantly(SSR2) was found correlating to be withoverexpressed disease progression. in patient HCCHong tissue, et al. with recently increased reported expression that SSR2was significantly involved correlating in the proliferation with disease andprogression. invasion

Biology 2021, 10, 640 11 of 15

of HCC cells, as SSR2 knockdown was shown to suppress the epithelial mesenchymal transition (EMT) of HCC cells [19]. Several homologs of the Hsp40/DnaJ (DnaJB6, DnaJC1, DnaJC5, DnaJC7, and DnaJC21) have been identified as unfavorable prognostic markers in liver cancer. Wang et al. recently demonstrated a novel oncogenic function of DnaJC5 in pro- moting HCC cell proliferation by regulating the SKP2-mediated degradation of tumor suppressor p27 [46]. Furthermore, other members of the DnaJ protein family have been assigned roles in other liver pathologies, such as steatosis development for DnajC7 [47] and liver cirrhosis for DnajC21 [48]. Several members of the DnaJ protein family have been determined to act as co-chaperones of proteins through different mechanisms, such as binding immunoglobulin protein (BiP). For instance, DnaJC1 has been shown to regulate translation in a BiP-dependent manner by binding its cytosolic domain to the 80S ribosome, thereby ensuring BiP interaction with nascent polypeptide chains entering the ER [49]. The upregulation of DnaJ proteins in HCC therefore aligns with a number of studies showing an increased expression of BiP in HCC patients [7,50]. Notably, our analysis identified one UPR-associated protein, phosphoinositide-3- kinase regulatory subunit 1 (PIK3r1), as a favorable prognostic marker in liver cancer. Winnay et al. reviously reported on an ER-stress-dependent interaction between PIK3r1, a key regulator of insulin action, and nuclear XBP1 accumulation, followed by UPR gene induction. This study demonstrated that PIK3r1 deletion in the liver attenuates adap- tive UPR signaling and promotes inflammation [51]. We could therefore speculate that increased PIK3r1 expression is involved in a pro-adaptive response to HCC-induced ER-stress, whereby it suppresses inflammation and exerts an anti-carcinogenic function. However, given its function in metabolomic homeostasis, the role of PIK3r1 in HCC- associated ER-stress could be largely dependent on disease etiology, and this warrants further investigation. Overall, the UPR-associated proteins that were identified as unfavorable prognostic markers in HCC in our study are in line with previous reports that confirm their contribu- tion to ER-stress-mediated hepatocarcinogenesis and their potential as therapeutic targets in HCC. These findings therefore strengthen the evidence of their involvement in HCC progression and their translational potential for patients with liver cancer. When comparing the expression of the UPR-associated unfavorable prognostic mark- ers in relation to patient sex, no notable differences were observed. This finding is in contrast to reports on the sexual dimorphism of ER-stress susceptibility in different in vivo models. Male prepubertal rats were shown to have a lower predisposition to hepatic ER-stress, while an increase in UPR marker expression in males was noted during the transition from a prepubertal to an adult age, which was strongly linked to the notable increase in testosterone levels in this phase [52]. Similarly, Hodeify et al. reported on sex differences in ER-stress-induced acute kidney injury and observed a significantly higher susceptibility in male mice compared to females [53]. Similarly, we did not observe significant differences when we compared the UPR marker expressions between the three different age groups, with the exception of the markers RACK1, ATF4, and CHOP. While an age-related decline in RACK1 expression has been noted in several studies on age-associated immunosenescence [54,55], the increased expression of ATF4 in the liver was found to correlate with a longer life span in different strains of slow-aging mice [56]. This finding aligns with the diverse pro-adaptive ATF4- mediated UPR pathways that could play a role in controlling the aging rate and UPR maintenance. Contrary to our findings, an age-related upregulation of CHOP has been reported by previous studies, where it was also noted that increased CHOP expression sensitized aging hepatocytes to oxidative injury, ER-stress, and autophagy [57–59]. Overall, our observation of a lack of age-related increase in ER-stress marker expression in HCC patients was contrary to reports of age-related deterioration of the UPR machinery and an increase in protein misfolding, which is known to exacerbate existing diseases, including different types of cancer [60]. Biology 2021, 10, 640 12 of 15

When we compared the mRNA expression of the UPR markers in HCC between three ethnic categories (Asian, Black or African American, and White), differences in expression were observed for the markers RACK1, ATF4, and SSR2. While a common feature of HCC is that it arises in the background of chronic liver disease and cirrhosis, dominant risk factors for HCC development differ between different ethnicities. In East Asia (with the exception of Japan) and Southeast Asia, these risk factors are the hepatitis B virus HBV and aflatoxin exposure, while in Europe and North America they include the hepatitis C virus and the metabolic syndrome [22]. These distinct HCC etiologies could in part explain the differences in UPR-marker expression we observed between different ethnic groups, but also highlight the importance of etiologic factors when evaluating biomarkers and therapeutic targets in HCC. Finally, a notable trend of increased UPR marker protein expression in the tumorous hepatic tissue was observed compared to stromal compartments, where the expression was overall downregulated to the baseline levels of the healthy tissue. For instance, key ER-stress markers associated with HCC chemoresistance, such as ATF4, ERO1-α, EIF2S3, and CHOP, were found to have higher expression in tumoral tissue compared to the stromal compartment of the liver biopsies. These ER-stress regulators have also been proposed as therapeutic targets for inducing HCC apoptosis and sensitizing HCC cells to chemotherapy. Chronic UPR activation and ER-stress occurrence in tumor cells is known to be a result of an increased metabolic rate and a mutation-driven protein synthesis need [61]. However, ER-stress in stromal cells has also been reported as a key contributor to hepatocarcinogenesis. In our previous paper, we showed that ER-stress markers belonging to the IRE1α branch were mainly present in the hepatic stellate cell population in the livers of mice with HCC. This increased expression was shown to correlate with stellate cell activation, pro-fibrotic signaling, and ultimately tumor progression in HCC. Furthermore, we reported that the expression of IRE1α signaling pathway components was upregulated in fibrotic HCC patient tissue compared to non-fibrous HCC tissue, while this increased expression significantly correlated with poor survival in patients with liver cancer [5]. Moreover, we recently reported that ER-stress occurrence in macrophages was correlated with an anti-tumoral macrophage phenotype and the increased macrophage clearance of HCC cells [62]. These findings emphasized the contribution of ER-stress and UPR activation in stromal tissue to HCC progression, as well as the therapeutic potential of targeting stromal cells undergoing ER-stress in liver cancer. This insight on the tumor/stroma distribution of UPR marker expression in HCC could prove relevant when assessing the use and development of nanotherapies targeted at these markers. Delivering the ER-stress inducers or inhibitors specifically to the cell-of- interest could be of great importance, as systemically targeting UPR pathways could have long-term undesired side-effects for patients.

5. Conclusions Broad experimental evidence from preclinical and patient studies highlights ER-stress as an important contributor to the hepatocarcinogenic process. While it is important to shed light on the molecular implications of ER-stress markers for the progression of HCC, there is a great need to explore the translational potential and the clinical relevance of these findings. By investigating the predictive value of ER-stress markers in HCC, we identified 44 proteins as unfavorable for patients with liver cancer. Through quantifying the distribution of the expression of these markers in patient HCC tissue as well as discussing the current literature on how these markers are involved in HCC pathogenesis, we provide insight into how the therapeutic value of targeting ER-stress in HCC can be utilized for the advancement of liver cancer prevention and treatment.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/biology10070640/s1, Table S1: Full gene-set, Table S2: UPR prognostic markers with protein functions. Biology 2021, 10, 640 13 of 15

Author Contributions: Conceptualization, N.P. and F.H.; methodology, N.P. and F.H.; software, F.H.; validation, N.P. and F.H.; formal analysis, F.H.; investigation, N.P. and F.H.; resources, F.H.; data curation, N.P. and F.H.; writing—original draft preparation, N.P.; writing—review and editing, N.P. and F.H.; visualization, F.H.; supervision, F.H.; project administration, N.P. and F.H.; funding acquisition, F.H. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded through grants obtained from the Swedish Cancer Foundation (Cancerfonden, 20 1076PjF and 20 0175F) and the Swedish Society for Medical Research (SSMF, S17-0092). These funding sources were not involved in the study design; the collection, analysis, and interpretation of data; the writing of the report; and in the decision to submit the article for publication. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data used in this study (transcriptomics data from 365 patients, healthy and HCC liver biopsies) were derived from The Cancer Genome Project (https://www.cancer. gov/tcga, accessed on 14 January 2021) and the Human Protein Atlas (https://www.proteinatlas.org, accessed on 14 January 2021) publicly available databases which are referred to in the manuscript according to the database guidelines. Conflicts of Interest: The funders had no role in the design of the study; in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to publish the results.

References 1. Heindryckx, F.; Gerwins, P. Targeting the tumor stroma in hepatocellular carcinoma. World J. Hepatol. 2015, 7, 165–176. [CrossRef] [PubMed] 2. Walter, P.; Ron, D. The Unfolded Protein Response: From Stress Pathway to Homeostatic Regulation. Science 2011, 334, 1081–1086. [CrossRef] 3. Hetz, C. The unfolded protein response: Controlling cell fate decisions under ER stress and beyond. Nat. Rev. Mol. Cell Biol. 2012, 13, 89–102. [CrossRef][PubMed] 4. Al-Rawashdeh, F.Y.; Scriven, P.; Cameron, I.C.; Vergani, P.V.; Wyld, L. Unfolded protein response activation contributes to chemoresistance in hepatocellular carcinoma. Eur. J. Gastroenterol. Hepatol. 2010, 22, 1099–1105. [CrossRef] 5. Pavlovic, N.; Calitz, C.; Thanapirom, K.; Mazza, G.; Rombouts, K.; Gerwins, P.; Heindryckx, F. Inhibiting IRE1alpha-endonuclease activity decreases tumor burden in a mouse model for hepatocellular carcinoma. eLife 2020, 9, e55865. [CrossRef] 6. Heindryckx, F.; Binet, F.; Ponticos, M.; Rombouts, K.; Lau, J.; Kreuger, J.; Gerwins, P. Endoplasmic reticulum stress enhances fibrosis through IRE1 alpha-mediated degradation of miR-150 and XBP-1 splicing. EMBO Mol. Med. 2016, 8, 729–744. [CrossRef] 7. Shuda, M.; Kondoh, N.; Imazeki, N.; Tanaka, K.; Okada, T.; Mori, K.; Hada, A.; Arai, M.; Wakatsuki, T.; Matsubara, O.; et al. Activation of the ATF6, XBP1 and grp78 genes in human hepatocellular carcinoma: A possible involvement of the ER stress pathway in hepatocarcinogenesis. J. Hepatol. 2003, 38, 605–614. [CrossRef] 8. Bobrovnikova-Marjon, E.; Grigoriadou, C.; Pytel, D.; Zhang, F.; Ye, J.; Koumenis, C.; Cavener, D.; Diehl, J.A. PERK promotes cancer cell proliferation and tumor growth by limiting oxidative DNA damage. Oncogene 2010, 29, 3881–3895. [CrossRef] 9. Maiers, J.L.; Malhi, H. Endoplasmic Reticulum Stress in Metabolic Liver Diseases and Hepatic Fibrosis. Semin. Liver Dis. 2019, 39, 235–248. [CrossRef] 10. The Human Protein Atlas. 2021. Available online: https://www.proteinatlas.org (accessed on 14 January 2021). 11. Uhlen, M.; Zhang, C.; Lee, S.; Sjostedt, E.; Fagerberg, L.; Bidkhori, G.; Benfeitas, R.; Arif, M.; Liu, Z.; Edfors, F.; et al. A pathology atlas of the human cancer transcriptome. Science 2017, 357, 660. [CrossRef][PubMed] 12. The Harmonizome. 2021. Available online: https://maayanlab.cloud/Harmonizome/ (accessed on 14 January 2021). 13. Rouillard, A.D.; Gundersen, G.W.; Fernandez, N.F.; Wang, Z.; Monteiro, C.D.; McDermott, M.G.; Ma’ayan, A. The harmonizome: A collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016, 2016, baw100. [CrossRef] 14. String. 2021. Available online: https://string-db.org (accessed on 14 January 2021). 15. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [CrossRef][PubMed] 16. Cancer Genome Atlas Research Network. Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell 2017, 169, 1327–1341.e23. [CrossRef][PubMed] 17. Zhou, T.; Lv, X.; Guo, X.; Ruan, B.; Liu, D.; Ding, R.; Gao, Y.; Ding, J.; Dou, K.; Chen, Y. RACK1 modulates apoptosis induced by sorafenib in HCC cells by interfering with the IRE1/XBP1 axis. Oncol. Rep. 2015, 33, 3006–3014. [CrossRef][PubMed] Biology 2021, 10, 640 14 of 15

18. Li, D.; Wang, W.J.; Wang, Y.Z.; Wang, Y.B.; Li, Y.L. Lobaplatin promotes (125)I-induced apoptosis and inhibition of proliferation in hepatocellular carcinoma by upregulating PERK-eIF2alpha-ATF4-CHOP pathway. Cell Death Dis. 2019, 10, 744. [CrossRef] [PubMed] 19. Hong, X.; Luo, H.; Zhu, G.; Guan, X.; Jia, Y.; Yu, H.; Lv, X.; Yu, T.; Lan, H.; Zhang, Q.; et al. SSR2 overexpression associates with tumorigenesis and metastasis of Hepatocellular Carcinoma through modulating EMT. J. Cancer 2020, 11, 5578–5587. [CrossRef] [PubMed] 20. Negroni, L.; Taouji, S.; Arma, D.; Pallares-Lupon, N.; Leong, K.; Beausang, L.A.; Latterich, M.; Bosse, R.; Balabaud, C.; Schmitter, J.M.; et al. Integrative quantitative proteomics unveils proteostasis imbalance in human hepatocellular carcinoma developed on nonfibrotic livers. Mol. Cell. Proteom. 2014, 13, 3473–3483. [CrossRef] 21. Pal, S.K.; Hurria, A. Impact of age, sex, and comorbidity on cancer therapy and disease progression. J. Clin. Oncol. 2010, 28, 4086–4093. [CrossRef] 22. Jayaraman, T.; Lee, Y.Y.; Chan, W.K.; Mahadeva, S. Epidemiological differences of common liver conditions between Asia and the West. JGH Open 2020, 4, 332–339. [CrossRef] 23. Chen, X.; Cubillos-Ruiz, J.R. Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat. Rev. Cancer 2021, 21, 71–88. [CrossRef] 24. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000204628-RACK1/tissue/liver (accessed on 14 January 2021). 25. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000204628-RACK1/pathology/liver+ cancer (accessed on 14 January 2021). 26. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000128272-ATF4/tissue/liver (accessed on 14 January 2021). 27. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000128272-ATF4/pathology/liver+ cancer (accessed on 14 January 2021). 28. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000089597-GANAB/tissue/liver (accessed on 14 January 2021). 29. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000089597-GANAB/pathology/liver+ cancer (accessed on 14 January 2021). 30. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000197930-ERO1A/tissue/liver (accessed on 14 January 2021). 31. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000197930-ERO1A/pathology/liver+ cancer (accessed on 14 January 2021). 32. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000130741-EIF2S3/tissue/liver (accessed on 14 January 2021). 33. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000130741-EIF2S3/pathology/liver+ cancer (accessed on 14 January 2021). 34. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000175197-DDIT3/tissue/liver (accessed on 14 January 2021). 35. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000175197-DDIT3/pathology/liver+ cancer (accessed on 14 January 2021). 36. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000101255-TRIB3/tissue/liver (accessed on 14 January 2021). 37. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000101255-TRIB3/pathology/liver+ cancer (accessed on 14 January 2021). 38. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000124783-SSR1/tissue/liver (accessed on 14 January 2021). 39. The Human Protein Atlas. 2020. Available online: https://www.proteinatlas.org/ENSG00000124783-SSR1/pathology/liver+ cancer (accessed on 14 January 2021). 40. Li, J.; Zhuo, J.Y.; Zhou, W.; Hong, J.W.; Chen, R.G.; Xie, H.Y.; Zhou, L.; Zheng, S.S.; Jiang, D.H. Endoplasmic reticulum stress triggers delanzomib-induced apoptosis in HCC cells through the PERK/eIF2alpha/ATF4/CHOP pathway. Am. J. Transl. Res. 2020, 12, 2875–2889. 41. Yang, S.; Yang, C.; Yu, F.; Ding, W.; Hu, Y.; Cheng, F.; Zhang, F.; Guan, B.; Wang, X.; Lu, L.; et al. Endoplasmic reticulum resident oxidase ERO1-Lalpha promotes hepatocellular carcinoma metastasis and angiogenesis through the S1PR1/STAT3/VEGF-A pathway. Cell Death Dis. 2018, 9, 1105. [CrossRef] 42. Zhou, B.; Lu, Q.Q.; Liu, J.T.; Fan, L.L.; Wang, Y.; Wei, W.; Wang, H.; Sun, G.P. Melatonin Increases the Sensitivity of Hepatocellular Carcinoma to Sorafenib through the PERK-ATF4-Beclin1 Pathway. Int. J. Biol. Sci. 2019, 15, 1905–1920. [CrossRef] 43. Chen, Y.J.; Su, J.H.; Tsao, C.Y.; Hung, C.T.; Chao, H.H.; Lin, J.J.; Liao, M.H.; Yang, Z.Y.; Huang, H.H.; Tsai, F.J.; et al. Sinulari- olide Induced Hepatocellular Carcinoma Apoptosis through Activation of Mitochondrial-Related Apoptotic and PERK/eIF2 alpha/ATF4/CHOP Pathway. Molecules 2013, 18, 10146–10161. [CrossRef] Biology 2021, 10, 640 15 of 15

44. Yu, C.L.; Yang, S.F.; Hung, T.W.; Lin, C.L.; Hsieh, Y.H.; Chiou, H.L. Inhibition of eIF2alpha dephosphorylation accelerates pterostilbene-induced cell death in human hepatocellular carcinoma cells in an ER stress and autophagy-dependent manner. Cell Death Dis. 2019, 10, 418. [CrossRef][PubMed] 45. DeZwaan-McCabe, D.; Riordan, J.D.; Arensdorf, A.M.; Icardi, M.S.; Dupuy, A.J.; Rutkowski, D.T. The stress-regulated transcription factor CHOP promotes hepatic inflammatory gene expression, fibrosis, and oncogenesis. PLoS Genet. 2013, 9, e1003937. [CrossRef] 46. Wang, H.; Luo, J.; Tian, X.; Xu, L.; Zhai, Z.; Cheng, M.; Chen, L.; Luo, S. DNAJC5 promotes hepatocellular carcinoma cells proliferation though regulating SKP2 mediated p27 degradation. Biochim. Biophys. Acta Mol. Cell Res. 2021, 1868, 118994. [CrossRef][PubMed] 47. Ohno, M.; Kanayama, T.; Moore, R.; Ray, M.; Negishi, M. The roles of co- CCRP/DNAJC7 in Cyp2b10 gene activation and steatosis development in mouse livers. PLoS ONE 2014, 9, e115663. [CrossRef] 48. D’Amours, G.; Lopes, F.; Gauthier, J.; Saillour, V.; Nassif, C.; Wynn, R.; Alos, N.; Leblanc, T.; Capri, Y.; Nizard, S.; et al. Refining the phenotype associated with biallelic DNAJC21 mutations. Clin. Genet. 2018, 94, 252–258. [CrossRef][PubMed] 49. Shen, Y.; Meunier, L.; Hendershot, L.M. Identification and characterization of a novel endoplasmic reticulum (ER) DnaJ homologue, which stimulates ATPase activity of BiP in vitro and is induced by ER stress. J. Biol. Chem. 2002, 277, 15947–15956. [CrossRef] [PubMed] 50. Chava, S.; Lee, C.; Aydin, Y.; Chandra, P.K.; Dash, A.; Chedid, M.; Thung, S.N.; Moroz, K.; Wu, T.; Nayak, N.C.; et al. Chaperone- mediated autophagy compensates for impaired macroautophagy in the cirrhotic liver to promote hepatocellular carcinoma. Oncotarget 2017, 8, 40019–40036. [CrossRef][PubMed] 51. Winnay, J.N.; Boucher, J.; Mori, M.A.; Ueki, K.; Kahn, C.R. A regulatory subunit of phosphoinositide 3-kinase increases the nuclear accumulation of X-box-binding protein-1 to modulate the unfolded protein response. Nat. Med. 2010, 16, 438–445. [CrossRef] 52. Rossetti, C.L.; de Oliveira Costa, H.M.; Barthem, C.S.; da Silva, M.H.; de Carvalho, D.P.; da-Silva, W.S. Sexual dimorphism of liver endoplasmic reticulum stress susceptibility in prepubertal rats and the effect of sex steroid supplementation. Exp. Physiol. 2019, 104, 677–690. [CrossRef] 53. Hodeify, R.; Megyesi, J.; Tarcsafalvi, A.; Mustafa, H.I.; Hti Lar Seng, N.S.; Price, P.M. Gender differences control the susceptibility to ER stress-induced acute kidney injury. Am. J. Physiol. Renal Physiol. 2013, 304, F875–F882. [CrossRef][PubMed] 54. Racchi, M.; Buoso, E.; Ronfani, M.; Serafini, M.M.; Galasso, M.; Lanni, C.; Corsini, E. Role of Hormones in the Regulation of RACK1 Expression as a Signaling Checkpoint in Immunosenescence. Int. J. Mol. Sci. 2017, 18, 1453. [CrossRef] 55. Corsini, E.; Racchi, M.; Sinforiani, E.; Lucchi, L.; Viviani, B.; Rovati, G.E.; Govoni, S.; Galli, C.L.; Marinovich, M. Age-related decline in RACK-1 expression in human leukocytes is correlated to plasma levels of dehydroepiandrosterone. J. Leukoc. Biol. 2005, 77, 247–256. [CrossRef] 56. Li, W.; Li, X.; Miller, R.A. ATF4 activity: A common feature shared by many kinds of slow-aging mice. Aging Cell 2014, 13, 1012–1018. [CrossRef] 57. Ikeyama, S.; Wang, X.T.; Li, J.; Podlutsky, A.; Martindale, J.L.; Kokkonen, G.; van Huizen, R.; Gorospe, M.; Holbrook, N.J. Expression of the pro-apoptotic gene gadd153/chop is elevated in liver with aging and sensitizes cells to oxidant injury. J. Biol. Chem. 2003, 278, 16726–16731. [CrossRef] 58. Li, J.; Holbrook, N.J. Elevated gadd153/chop expression and enhanced c-Jun N-terminal protein kinase activation sensitizes aged cells to ER stress. Exp. Gerontol. 2004, 39, 735–744. [CrossRef][PubMed] 59. Enkhbold, C.; Morine, Y.; Utsunomiya, T.; Imura, S.; Ikemoto, T.; Arakawa, Y.; Saito, Y.; Yamada, S.; Ishikawa, D.; Shimada, M. Dysfunction of liver regeneration in aged liver after partial hepatectomy. J. Gastroenterol. Hepatol. 2015, 30, 1217–1224. [CrossRef] [PubMed] 60. Brown, M.K.; Naidoo, N. The endoplasmic reticulum stress response in aging and age-related diseases. Front. Physiol. 2012, 3, 263. [CrossRef][PubMed] 61. Verfaillie, T.; Garg, A.D.; Agostinis, P. Targeting ER stress induced apoptosis and inflammation in cancer. Cancer Lett. 2013, 332, 249–264. [CrossRef][PubMed] 62. Nataša Pavlovi´c,M.K.; Gerwins, P.; Heindryckx, F. Inhibiting P2Y12 in Macrophages Induces Endoplasmic Reticulum Stress and Promotes an Anti-Tumoral Phenotype. Int. J. Mol. Sci. 2020, 21, 8177. [CrossRef][PubMed]