In Stomach Adenocarcinoma

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Original Article Page 1 of 20

Expression and role of nuclear receptor-interacting protein 1 (NRIP1) in stomach adenocarcinoma

Dalang Fang, Guanming Lu

Department of Glandular Surgery, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China Contributions: (I) Conception and design: All authors; (II) Administrative support: G Lu; (III) Provision of study materials or patients: G Lu; (IV) Collection and assembly of data: D Fang; (V) Data analysis and interpretation: D Fang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Correspondence to: Guanming Lu. Department of Glandular Surgery, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, China. Email: [email protected].

Background: Nuclear receptor-interacting protein 1 (NRIP1), also named NR140, has been observed differentially express in multiple cancers, but the expression levels and the prognostic role of NRIP1 in stomach adenocarcinoma (STAD) remain unclear. Methods: We used the Gene Expression Profiling Interactive Analysis (GEPIA) to analyze the NRIP1 expression levels in STAD, subgroups analysis of expression of NRIP1 via the UALCAN dataset. Further, cBioPortal was used to investigate the aberration type, co-mutations status, and located mutation of NRIP1. Correlated genes, and kinases, microRNA (miRNA), and transcription factor (TF) targets were identified using LinkedOmics. The Kaplan-Meier (K-M) plotter was used to analyze the prognosis of NRIP1 and the significantly correlated genes in STAD. Then, the tumor immune estimation resource (Timer) was used to explore the relation between NRIP1 and the immune cell infiltration, and the role of immune cells in STAD. The Human Protein Atlas (HPA) was used to confirm the NRIP1 protein express in STAD stomach tissue and normal stomach tissue. Results: NRIP1 significantly overexpress in STAD, and the NRIP1 expression levels were impacted by clinical features. Overexpression of NRIP1 indicated the poor prognosis of STAD. Functional enrichment analysis showed the NRIP1 mainly enriched in immune response-regulating signaling pathway, cell-substrate adhesion, mRNA processing, and pathway in cancer. Overexpression USP25, SNYJ1 indicated the poor outcome of STAD, but the overexpression of BACH1 indicated protective biomarker. MIR-331 and MIR- 132 have important role in STAD. Further, NRIP1 had a significant relation with immune infiltrates and other defined genes that significantly impact immune infiltrates. Immunohistochemical showed NRIP1 protein was higher in STAD than normal sample. Conclusions: In this study, we revealed that overexpression of NRIP1 in the STAD sample compared to normal samples, NRIP1 significantly associated with macrophage. The high expression levels of NRIP1 and more macrophage infiltration led to poor prognosis of STAD.

Keywords: Nuclear receptor-interacting protein 1 (NRIP1); stomach adenocarcinoma; prognostic role

Submitted Jun 16, 2020. Accepted for publication Oct 19, 2020. doi: 10.21037/atm-20-6197 View this article at: http://dx.doi.org/10.21037/atm-20-6197

Introduction and diagnostic methods, the incidence and mortality of GC

Last century, gastric cancer (GC), also named stomach have decreased in the past five decades, but GC remains the adenocarcinoma (STAD), was the most common cancer primary cause of cancer-associated death. Each year, there type in the United States. With the development of therapy are about 1 million individuals be newly diagnosed with

GC, and 782,685 people die of this cancer worldwide (1). via comprehensive bioinformatics analysis. We present the With the high morbidity and mortality, GC is a considerable following article in accordance with the MDAR reporting challenge for global public health. Thus, it is urgently checklist (available at http://dx.doi.org/10.21037/atm-20- needed the explore the more sensitive and efficient 6197). biomarker for diagnosing, even as the targeted therapy of STAD. Nowadays, combination surgery with systemic Methods multi-line chemotherapy could prolong the longevity of STAD patients, but these treatment regimens only saved The Gene Expression Profiling Interactive Analysis less 30% STAD patients’ life (2). For the metastatic STAD, (GEPIA) analysis the cornerstone is remaining cytotoxic chemotherapy. To GEPIA is a web-based tool to deliver fast and customizable date, there were several studies that investigated the efficacy functionalities from The Cancer Genome Atlas (TCGA), of target therapy for STAD such as apatinib, trastuzumab, and Genotype-Tissue Expression (GTEx) project provides and ramucirumab (3-5). With the rapid development of immunotherapy in cancer therapy, several studies also essential interactive functions, including differential explored the efficacy and safety of immunotherapy for expression analysis, profiling plotting, correlation analysis, STAD as well (6,7). and patient survival analysis (18). We used the GEPIA to In 1995, nuclear receptor-interacting protein 1 (NRIP1) analyze the NRIP1 expression levels in STAD. was the first time be found in the cancer cell, and the NRIP1 function is it interacts with estrogen receptor α (ER- UALCAN database analysis α) (8). From then on, the interaction of NRIP1 and other nuclear receptors and transcription factors (TFs) had been The UALCAN (http://ualcan.path.uab.edu) is an easy- found. Further, studies also found that NRIP1 expressed to-use, interactive web-portal that can perform in-depth in multiple cancer types and served as a critical role in analyses of TCGA gene expression data (19). To further tumorigenesis, treatment response, and prognosis. For analyze the factors that affect the NRIP1 expression in breast cancer, the NRIP1 downregulation may be associated STAD. We use the UALCAN dataset to perform the with basal-like tumors (9). Turashvili et al. revealed the subgroup analysis. In our study, UALCAN, as used to NRIP1 higher expression in ductal carcinomas than lobular confirm the expression data of NRIP1, has examined via the carcinomas (10). The role of NRIP1 was the regulator or “Expression Analysis” module and the “STAD” dataset. interactor of ER signaling and E2F signaling to affect breast cancer progression (11,12). The inhibition of NRIP1 can Analyzing the mutation types, mutated location, and the decrease the growth of breast cancer cells (13). Compared structure of NRIP1 through cBioPortal with normal samples, the NRIP1 was downregulated in hepatocellular carcinoma (HCC), and downregulation The cBioPortal for Cancer Genomics (http://cbioportal. of NRIP1 leads to improve growth and migration ability org) provides a Web resource for exploring, visualizing, and HCC cancer cells (14). And the cir-NRIP1 accelerates analyzing multidimensional cancer genomics data (20). We the glycolysis and tumor progression via regulating miR- explored the aberration type of subgroups and investigated 186-5p/MYH9 axis in STAD (15). This indicated the the location and structure of NRIP1 in GC via cBioPortal. circulating NRIP1 also as the oncogenic roles. NRIP1 not only play a key role in cancer process but also acts as a Kaplan-Meier (K-M) plotter analysis significant part in the other diseases. NRIP1 also severed as an important regulator of energy metabolism and coronary The K-M plotter is capable of assessing the effect of 54 k artery disease (16,17). These results showed that NRIP1 genes [mRNA, microRNA (miRNA), protein] on survival expression levels had a significant role in cancer patients’ in 21 cancer types, including breast, ovarian, lung, and prognosis. However, the NRIP1biological functions GC. Sources for the databases include GEO, EGA, and in these cancers and the NRIP1 expression levels and TCGA (21). The primary purpose of the tool is a meta- prognostic role in STAD still unclear. With these, we found analysis-based discovery and validation of survival biomarkers. the expression levels and prognostic role of NRIP1 and We used KM plotter to confirm the prognostic values of further analyzed the potential biological functions in STAD NRIP1 and the significantly correlated genes in STAD.

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(20):1293 | http://dx.doi.org/10.21037/atm-20-6197 Annals of Translational Medicine, Vol 8, No 20 October 2020 Page 3 of 20

Protein-Protein network construction via GeneMANIA proteomics, transcriptomics, and systems biology (25). All the data in the knowledge resource is open access to allow GeneMANIA is a commonly used website for performing scientists both in academia and industry to access the data protein-protein interaction (PPI) network analysis and for exploration of the human proteome freely. Finally, we predicting the function of preferred genes (22). This user- used the HPA database to investigate the NRIP1 expression friendly online tool can display gene or gene lists using levels in normal and STAD tissue. bioinformatics methods, including gene co-expression, The study was conducted in accordance with the physical interaction, gene co-location, gene enrichment Declaration of Helsinki (as revised in 2013). analysis, and website prediction. We constructed the NRIP1 PPI network via GeneMANIA. Statistics analysis

The LinkedOmics dataset The statistics analysis of NRIP1 expression levels and the prognostics values for STAD via the public database The LinkedOmics database (http://www.linkedomics.org) statistics analysis methods. contains multi-omics data and clinical data for 32 cancer types comprising 11,158 patients from the TCGA project. It provides a unique platform for scholars to access, analyze, Results and compare cancer multi-omics data within and across Expression analysis tumor types (23). The “LinkFinder” module was used to investigate differentially expressed genes within the TCGA To explore the NRIP1 differentially express in different STAD cohort. The “LinkInterpreter” module was used to cancer types and GC. We used the GEPIA primarily to perform analysis of kinase target s, miRNA target, and TF investigate the NRIP1 expression in human cancers, and target for NRIP1. Results were analyzed for significance independently used GEPIA to perform NRIP1 expression using the spearman’s correlation test. The P value cutoff levels in GC. Further, we used the UALCAN database was 0.05. to analyze the relationship between clinical features and the NRIP1 expression. NRIP1 was found significantly express in six cancer types that including cervical squamous Tumor Immune Estimation Resource (Timer) analysis cell carcinoma and endocervical adenoma (ESCA), TIMER web server is a comprehensive resource for glioblastoma multiform (GBM), brain lower-grade glioma systematical analysis of immune infiltrates across diverse (LGG), pancreatic adenocarcinoma (PAAD), STAD, and cancer types. The abundances of six immunes infiltrate thymoma (Figure 1A), and we independently performed (B cells, CD4+ T cells, CD8+ T cells, Neutrophils, the expression of NRIP1 in STAD (Figure 1B). These Macrophages, and Dendritic cells) are estimated by the results showed that NRIP1 is an upregulated expression TIMER algorithm. TIMER web server allows users to in multiple cancers. Further, we used the UALCAN to input function-specific parameters, with resulting figures perform a subgroup analysis of NRIP1 expression of dynamically displayed to conveniently access the tumor STAD. These results showed that the NRIP1 significantly immunological, clinical, and genomic features (24). overexpressed in the STAD samples compared to normal We investigated the relation NRIP1 between immune samples. On the clinical features, we also reanalyzed the infiltration, the association prognosis with immune cell NRIP1 expression levels in different clinical status. The infiltration, and the effect factors of immune infiltration and results showed that the NRIP1 was the higher expression in outcomes of STAD via TIMER dataset. STAD samples compared with normal samples (Figure 2A), the more tumor burden the higher NRIP1 expression (Figure 2B). NRIP1 expression levels may affect H. pylori The analysis of Human Protein Atlas (HPA) infection, with H. pylori infection would have lower NRIP1 The HPA (http://www.proteinatlas.org) aims to map all expression than without infection (Figure 2C). Histological the human proteins in cells, tissues, and organs using subtypes influence NRIP1 expression as well. The intestinal the integration of various omics technologies, including adenocarcinoma (Papillary) does not have a difference in antibody-based imaging, mass spectrometry-based NRIP1 expression between tumor tissue and normal tissue

A B ACCBLCABRCACESCCHOLCOADDLBCESCAGBMHNSCKICHKIRCKIRPLAMLLGGLIHCLUADLUSCMESOOV PAADPCPGPRADREADSARCSKCMSTADTGCTTHCATHYMUCECUCSUVM 9 5 8

7 4

6 3

2 4

3 1 Transcripts per million (TPM) Transcripts

0 1 STAD [num (T) =408; num (N) =211] 0

Figure 1 NRIP1 expression levels in different human cancers. (A) Increased or decreased NRIP1 in data sets of different cancers compared with normal tissues in the GEPIA. (B) NRIP1 expression in STAD samples and paired normal samples determined by GEPIA. NRIP1, nuclear receptor-interacting protein 1; GEPIA, Gene Expression Profiling Interactive Analysis; STAD, stomach adenocarcinoma.

(Figure 2D). The patient’s race did not have a deep influence for the altered group and unaltered group of NRIP1 on NRIP1 expression (Figure 2E). There was no difference (Figure 3D). The located mutation of NRIP1 was performed found between nodal metastasis status and NRIP1 via cBioPortal as well (Figure 3E). expression (Figure 2F). The co-mutated status of TP53 also affected the NRIP1 expression that co-mutation with Survival analysis of NRIP1 TP53 led to downregulation (Figure 2G). Gender did not have any impact (Figure 2H). Tumor grade was associated We used the K-M plotter to confirm the NRIP1 prognostics with NRIP1 expression. The grade 1 and grade 3 had a role in STAD. We analyze the relationship between NRIP1 significant difference Figure( 2I). expression levels and post-progression survival (PPS), free- progression (FP), and overall survival for STAD via K-M plotter. The results showed that overexpression of NRIP1 led to shortening the OS (HR =1.49, log-rank P=1.8e−05), c-BioPortal analysis FP (HR =1.56, log-rank P=4e−05), PPS of STAD patients There are many mutation types for genes, including (Figure 4A,B,C). missense, amplification, and deep deletion. The different mutated types may be distinct functions of genes. We used Correlated significant genes analysis c-Bioportal to analyze the mutated-types, co-mutations, and the structure of NRIP1. The results showed that the mutate Tumorigenesis is a complicated process; this process may types of NRIP1, including missense mutation, truncating include many genes aberration. So, we use the LinkedOmics mutation, amplification, and deep deletion (Figure 3A). to find the top 50 associated genes of NRIP1, further The aberration types in different histological subtypes and selected the correlated significant (cor≥ 0.6) genes of NRIP1 microsatellite instability (Figure 3B,C). The most often to conduct prognosis analysis. The results showed that the co-mutated genes, including TTN, ARID1A, SYNE1, top 50 positively and negatively correlated significant genes MUC16, RYR2, MDN1, KMT2D, LRP1B, TP53, SACS (Figure 5A,B,C). There were three correlated significant

N3 (n=82) (n=153) (n=246) Grade 3 Not available H. pylori

N2 (n=79) H. pylori (n=157) (n=148) Grade 2 infection

Without N1

(n=112)

infection status

TCGA samples TCGA samples TCGA samples metastasis status

H. pylori (n=20) (n=12) Grade 1 infection N0 With (n=123) Expression of NRlP1 in STAD based on of NRlP1 in STAD Expression

Expression of NRIP1 in STAD based on nodal of NRIP1 in STAD Expression infection may decrease NRIP1 expression.

Expression of NRIP1 in STAD based on tumor grade of NRIP1 in STAD Expression (n=34) (n=34) Normal Normal (n=34) Normal H. pylori 0 0 5 0

–5 30 20 10 40 30 20 10 35 30 25 20 15 10

-10 -10 million per Transcript

Transcript per million per Transcript Transcript per million per Transcript I F C

(n=41) Stage4 Asian (n=87) infection status; Female (n=147)

Stage3 (n=169) H. pylori (n=12)

African-American Male (n=268) Stage2 (n=123) TCGA samples cancer stages TCGA samples TCGA samples patient’s gender patient’s

(n=260) Caucasian (n=18) Stage1

Expression of NRIP1 in STAD based on of NRIP1 in STAD Expression TCGA samples

Expression of NRIP1 in STAD based on individual of NRIP1 in STAD Expression (n=34) Normal Expression of NRIP1 in STAD based on patient’s race based on patient’s of NRIP1 in STAD Expression (n=34) Normal (n=34) Normal

0 5 0 5 0

–5 –5 40 30 20 10 30 25 20 15 10 35 30 25 20 15 10

Transcript per million per Transcript -10 million per Transcript Transcript per million per Transcript E B H

(n=7)

(n=20) (n=235) IntAdenoPapillary

TP53-NonMutant

(n=76) IntAdenoMucinous

Primary tumor (n=415)

(n=73)

IntAdenoTubular

status

(n=178)

IntAdenoNOS (n=12) TP53-Mutant Sample types TCGA samples TCGA samples mutation TCGA samples

histological subtypes

(n=69) AdenoSignetRing

Expression of NRIP1 in STAD based on of NRIP1 in STAD Expression based on of NRIP1 in STAD Expression

AdenoDiffuse

Expression of NRIP1 in STAD based on TP53 of NRIP1 in STAD Expression Normal (n=34) (n=155)

(n=34) Normal

AdenoNOS

The relation between NRIP1 expression levels and clinical features via UALCAN. (A) Compared cancer and normal samples; cancer sample with higher expression

(n=34) Normal

5 0 0 5 0

–5 –5

35 30 25 20 15 10 40 30 20 10 35 30 25 20 15 10

Transcript per million per Transcript Transcript per million per Transcript –10 Transcript per million per Transcript A G D Figure 2 of NRIP1. advance (B) cancer Stage patients of may STAD; have higher NRIP1 expression. (C) (D) Histological subtypes. (E) Race. (F) Nodal metastasis status; the more nodal metastasis, the higher expression NRIP1. (G) Co-mutated with TP53; TP53 mutation leads stomach adenocarcinoma. protein 1; STAD, grade. NRIP1, nuclear receptor-interacting (I) Tumor to downregulation of NRIP1. (H) Patients gender.

A Profiled for mutations

NRIP1 6%*

Missense mutation Truncating mutation Genetic alteration Amplification Deep deletion No alterations (unknown significance) (unknown significance) Not profiled

Profiled for mutations Yes No

100 B 8% C

MSI 6% 80 MSS High-level microsatellite instability 4% Microsatellite stable or low-level microsatellite instability 60 Alteration frequency

MSI Status 40 #samples (%)

Mutation data CNA data 20 TubularSignet stomachStomach ring celladenocarcinomaDiffuse carcinomaadenocarcinomaMucinous type stomach of stomachthe stomach adenocarcinoma adenocarcinoma

0 AlteredUnaltered group group

Group D 80

60 Altered group Unaltered group 40

20 Mutation frequency (%) Mutation frequency 0 TTN* ARID1A*SYNE1*MUC16*RYR2* MDN1* KMT2D*LRP1B*TP53 SACS*

Mutation Amplification Deep deletion E E729Rfs*5 11

0 #NRIP1 Mutations

0 200 400 600 800 1000 1158aa Figure 3 Aberration types, located mutation, the co-mutation status of NRIP1, and the relation between microsatellite instability and NRIP1 mutations. (A) Aberration types and frequency of NRIP1 in STAD; missense and deep deletion mutation types are the most common, 6% mutations of NRIP1 in STAD. (B) The aberration types and histological subtypes. (C) The relation between microsatellite instability and NRIP1 mutations; NRIP1 mutations may have higher levels microsatellite instability. (D) Co-mutations with NRIP1. (E) Locations of NRIP1 mutations. NRIP1, nuclear receptor-interacting protein 1; STAD, stomach adenocarcinoma.

A NRIP1 (202600_s_at) B NRIP1 (202600_s_at) 1.0 HR =1.49 (1.24–1.79) 1.0 HR =2.27 (1.8–2.86) Log-rank P=1.8e–05 Log-rank P=1.2e–12

0.8 0.8

0.6 0.6

Probability 0.4 Probability 0.4

0.2 0.2 Expression Expression Low Low 0.0 High 0.0 High

0 50 100 150 0 20 40 60 80 Time (months) Time (months) Number at risk Number at risk Low 645 245 28 0 Low 369 75 33 24 8 High 230 53 20 1 High 129 6 1 0 0

C NRIP1 (202600_s_at) 1.0 HR =1.56 (1.26–1.94) Log-rank P=4e–05

0.8

0.6

Probability 0.4

0.2 Expression Low 0.0 High 0 50 100 150 Time (months) Number at risk Low 472 134 17 0 High 168 28 15 1 Figure 4 The prognostic role of the NRIP1 gene (Kaplan-Meier plotter). (A,B,C) High NRIP1 expression was correlated with poor prognosis of OS, PPS, and FS in STAD. NRIP1, nuclear receptor-interacting protein 1; STAD, stomach adenocarcinoma; os, overall survival; PPS, post-progression survival; FS, free-progression.

genes of NRIP1 that USP25 (Spearman correlation =6.432- Enrichment functions of NRIP1 01, P value =1.176e−36), BACH1(Spearman correlation To investigate the potential biological functions of NRIP1. =6.429e−01, P value =1.300e−36), and SNYJ1 (Spearman correlation =6.134e−01, P value =1.325e−32)consistent with We used the Linkomics dataset to analyze the biological the selected threshold (Figure 6A,B,C). The prognostics process (BP), cellar component (CC), molecular function value of selected genes in STAD showed that overexpression (MF), and the KEGG pathway. The results showed that the USP25, SNYJ1 indicated the poor outcome of STAD functions of NRIP1, including regulation of small GTPase (Figure 7A,B,C,D,E,F), but the overexpression of BACH1 mediated signal transduction, immune response-regulating indicated a satisfactory prognosis (Figure 7G,H,I). signaling pathway, cell-substrate adhesion, mRNA

A NRIP1 association result 35

–log10 (p value) 10

–1 0 1 2 Spearmans rho statistic (spearman test) B C

z-Score Group >3 4 1 2 0 0 –1 –2 <–3 –4 –6

Figure 5 Correlated significant genes of NRIP1 (LinkedOmics). (A,B,C) Volcano plots and heat maps showing genes positively and negatively genes correlated with NRIP1 in STAD, respectively (top 50). Red suggests positively correlated genes and green shows negatively correlated genes. NRIP1, nuclear receptor-interacting protein 1; STAD, stomach adenocarcinoma.

processing, and protein production (Figure 8A,B,C). And 106A, MIR-106B, MIR-20B, MIR-519D, (TTGCACT) NRIP1 may take part in the crucial pathways, including MIR-130A, MIR-301, MIR-130B, (TTGGAGA)MIR- a pathway in cancer, ribosome, carbon mentalism, and 515-5P, MIR-519E, (TAGGTCA)MIR-192,MIR-215, phosphatidylinositol signaling system (Figure 8D). (CACTGTG)MIR-128A, MIR-128B, (GACTGTT)MIR- 212, MIR-132, (ACCGAGC)MIR-423, (CCAGGGG) MIR-331, (CGGTGTG)MIR-220, and (GGCGGCA) Kinase targets, TFs targets, and miRNA targets of NRIP1 MIR-371 (Figure8E). The kinases target including spleen Gene expression is affected by many factors, the most associated tyrosine kinase (SYK), epidermal growth factor important regulators, including kinase, TFs, and miRNA. receptor (EGFR), ATM serine/threonine kinase, cyclin- To incite regulators that regulate NRIP1 expression in dependent kinase 2 (CDK2), p21 (RAC1) activated kinase STAD, we used the LinkedOmics database to find the 1, and other important kinase targets (Figure 8F). TFs significant kinase, TFs, miRNA targets of NRIP1. The targets results showed that TTGTTT_V$FOXO4_01, results showed the significant miRNAs targets including YTATTTTNR_V$MEF2_02, RCGCANGCGY_ (TGAATGT)MIR-181A, MIR-181B, MIR-181C, MIR- V$NRF1_Q6, V$SP1_Q6_01, V$MYCMAX_01, and 181D, (GCACTTT)MIR-17-5P, MIR-20A, MIR- SCGGAAGY_V$ELK1_02 are the significant TFs targets

Spearman-correlation: 0.6432 Spearman-correlation: 0.6134 A P value: 1.176e–36 B P value: 1.325e–32 Sample size: (N=302) Sample size: (N=302) 13

12 11

11 10

10 9

USP25 9 SYNJ1 8 8 7 7 6 6 8 9 10 11 8 9 10 11 NRIP1 NRIP1

C Spearman-correlation: 0.6429 P value: 1.3e–36 Sample size: (N=302) 14

BACH1 10

7 8 9 10 11 NRIP1

Figure 6 Gene correlation expression analysis for NRIP1 (LinkedOmics). (A,B,C) The scatter plots show spearman-correlation of NRIP1 expression with expression of USP25, SYNJ1, and BACH1 in STAD. NRIP1, nuclear receptor-interacting protein 1; STAD, stomach adenocarcinoma; SYNJ1, Synaptojanin 1; BACH1, BTB and CNC homology 1, basic leucine zipper transcription factor 1; USP25, ubiquitin-specific protease 25.

for NRIP1 (Figure 8G). These clues further showed that DNA-templated transcription, initiation intracellular NRIP1 functioned as an important function in various receptor signaling pathway, ligand-activated sequence- cancers. specific DNA binding RNA polymerase II TF activity, direct ligand regulated sequence-specific DNA binding TF activity, intracellular steroid hormone receptor signaling PPI network analysis of NRPI1 pathway, and transcription corepressor activity (Figure 9). Further, we constructed the PPI network to analyze the These results showed the NRIP1 played a crucial role in NRIP1 interacts with other genes. The PPI network cancer. showed that NRIP1 significantly interacted with BRCA1, NR0B1, AR, RARA, HDAC1, and other essential genes. Timer analysis The biological functions of these genes may include the transcription initiation from RNA polymerase II promoter The immune cell infiltration has a deep influence on the

A SYNJ1 (207594_s_at) B SYNJ1 (207594_s_at) C SYNJ1 (207594_s_at) 1.0 HR =1.85 (1.52–2.24) 1.0 HR =2.64 (2.1–3.31) 1.0 HR =1.71 (1.4–2.09) Log-rank P=2.9e–10 Log-rank P<1E–16 Log-rank P=1.5e–07 0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4 Probability Probability Probability

D USP25 (1555559_s_at) E USP25 (1555559_s_at) F USP25 (1555559_s_at)

1.0 HR =1.78 (1.43–2.22) 1.0 HR =2.51 (1.91–3.31) 1.0 HR =1.83 (1.44–2.32) Log-rank P=1.3e–07 Log-rank P=1e–11 Log-rank P=5.6e–07 0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4 Probability Probability Probability

0.2 Expression 0.2 Expression 0.2 Expression Low Low Low High 0.0 High 0.0 High 0.0 0 50 100 150 0 20 40 60 80 0 50 100 150 Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Low 324 170 15 0 Low 263 67 30 21 6 Low 288 107 6 0 High 578 95 33 1 High 121 14 4 3 2 High 234 55 26 1

G BACH1 (204194_at) H BACH1 (204194_at) I BACH1 (204194_at) 1.0 HR =0.59 (0.49–0.7) 1.0 HR =0.54 (0.43–0.67) 1.0 HR =0.56 (0.45–0.68) Log-rank P=7e–10 Log-rank P=4.3e–08 Log-rank P=1.5e–08 0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4 Probability Probability Probability

0.2 Expression 0.2 Expression 0.2 Expression Low Low Low 0.0 High 0.0 High 0.0 High 0 50 100 150 0 20 40 60 80 0 50 100 150 Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Low 441 112 25 0 Low 149 13 3 2 0 Low 217 38 14 0 High 434 186 23 0 High 349 68 31 22 8 High 423 124 18 1 Figure 7 Prognostic analysis of genes correlated with NRIP1 in STAD (Kaplan-Meier plotter). (A,B,C) The overall survival, post- progression survival, free-progress curves of SYJN1. (D,E,F) The overall survival, post-progression survival, free-progress curves of USP25. (G,H,I) The overall survival, post-progression survival, free-progress curves of BACH1. NRIP1, nuclear receptor-interacting protein 1; STAD, stomach adenocarcinoma.

A FDR ≤0.05 FDR >0.05 Regulation of small GTPase mediated signal transduction Axon development Immune response-regulating signaling pathway Cell-substrate adhesion Multicellular organismal signaling Leukocyte differentiation Protein-containing complex disassembly mRNA processing Nucleoside monophosphate metabolic process Ribonucleoprotein complex biogenesis

–2.5 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 Normalized enrichment score B FDR ≤0.05 FDR >0.05 Receptor complex Cell leading edge Side of membrane Endosome membrane Secretory granule membrane Ciliary part

Protein-DNA complex Cytosolic part Mitochondrial matrix Mitochondrial inner membrane

–3.0 –2.5 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 Normalized enrichment score C FDR ≤0.05 FDR >0.05 Guanyl-nucleotide exchange factor activity Cytokine receptor activity Nucleoside-triphosphatase regulator activity Protein serine/threonine kinase activity Phospholipid binding Metal ion transmembrane transporter activity Olfactory receptor activity Catalytic activity, acting on RNA Electron transfer activity Structural constituent of ribosome

–3.0 –2.5 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 Normalized enrichment score D FDR ≤0.05 FDR >0.05 Phosphatidylinositol signaling system Axon guidance Staphylococcus aureus infection Pathways in cancer Alcoholism Carbon metabolism Huntington disease Pyrimidine metabolism Spliceosome Ribosome –3.0 –2.5 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 2.5 Normalized enrichment score

E FDR ≤0.05 FDR >0.05

Spleen associated tyrosine kinase Ribosomal protein s6 kinase B1 G protein-coupled receptor kinase 3 Mitogen-activated protein kinase 7 Epidermal growth factor receptor ATM serine/threonine kinase Cyclin dependent kinase 2 Ribosomal protein S6 kinase A4 Mechanistic target of rapamycin p21(RAC1) activated kinase 1

–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 Normalized enrichment score

F FDR ≤0.05 FDR >0.05

TGAATGT, MIR-181A, MIR-181B, MIR-181C, MIR-181D GCACTTT, MIR-17-5P, MIR-20A, MIR-106A, MIR-106B, MIR-20B, MIR-519D TTGCACT, MIR-130A, MIR-301, MIR-130B TTGGAGA, MIR-515-5P, MIR-519E TAGGTCA, MIR-192, MIR-215 CACTGTG, MIR-128A, MIR-128B GACTGTT, MIR-212, MIR-132 ACCGAGC, MIR-423 CCAGGGG, MIR-331 CGGTGTG, MIR-220 GGCGGCA, MIR-371

–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 2.5 Normalized enrichment score

G FDR ≤0.05 FDR >0.05

TTGTTT_V$FOXO4_01 YTATTTTNR_V$MEF2_02 TGACATY_UNKNOWN AACTTT_UNKNOWN WTGAAAT_UNKNOWN RCGCANGCGY_V$NRF1_Q6 ACTAYRNNNCCCR_UNKNOWN V$SP1_Q6_01 V$MYCMAX_01 SCGGAAGY_V$ELK1_02

–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 Normalized enrichment score

Figure 8 Functions enrichment and miRNA targets, transcription factors targets, kinases targets of NRIP1 (LinkedOmics). (A) Biological process (BP) of NRIP1 in STAD. (B) Cellar component (CC) of NRIP1 in STAD. (C) Molecular functions (MF) of NRIP1 in STAD. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in STAD. (E) Kinases targets of NRIP1. (F) miRNA targets of NRIP1. (G) Transcription factors of NRIP1. NRIP1, nuclear receptor-interacting protein 1. STAD, stomach adenocarcinoma.

Networks Functions Physical Interactions Transcription initiation from RNA polymerase Il promoter

Co-expression DNA-templated transcription, initiation

Predicted Intracellular receptor signaling pathway

Co-localization Ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity Pathway Direct ligand regulated sequence-specific DNA binding transcription factor Genetic Interactions activity

Shared protein domains Intracellular steroid hormone receptor signaling pathway

Transcription corepressor activity

Figure 9 Protein-protein interaction network of NRIP1 (GeneMANIA). Protein-protein interaction (PPI) network and functional analysis showing the gene set enriched in the target network of NRIP1. Distinct colors of the network edge indicate the bioinformatics methods applied: physical interactions, co-expression, predicted, co-localization, pathway, genetic interactions, and shared protein domains. The distinct colors for the network nodes show the biological functions of the sets of enrichment genes. prognosis of some cancer types. We aimed to investigate the correlation with macrophage(cor =0.633, P=9.05e−43) relationship between NRIP1 and immune cell infiltration and dendritic (cor =0.544, P=6.61e−30) cell infiltration, and to explore further whether immune cell infiltration and NRIP1 had the positive relation with CD8+ T Cell is associated with the prognosis of STAD via Timer. The (cor =0.298, P=4.90e−09), CD4+ T Cell (cor =0.342, results showed that NRIP1 had a positively significant p=1.72e−11), and neutrophil (cor =0.42, P=2.79e−17) as

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(20):1293 | http://dx.doi.org/10.21037/atm-20-6197 Page 14 of 20 The role of NRIP1 in stomach adenocarcinoma well (Figure 10A). Further, we investigated the relationship metastasis, and co-mutations, had an influence on NRIP1 between somatic copy number aberration (CAN) and expression as well. And the higher expression of NRIP1 an abundance of immune infiltrates. The results showed indicated the poor prognosis of STAD. To date, there is that arm-level gain and arm-level deletion were the most not any study that investigated the fundamental function significant impactors for immune infiltrates, and high of NRIP1 in STAD. The significantly correlated genes, amplification affected the CD8+ T Cell and neutrophil including USP25, BACH1, and SYNJ1. The overexpression infiltration (Figure 10B). Finally, we explored the of USP25 and SYNJ1 indicated the poor prognosis of relationship between immune cell infiltration and prognosis STAD, but the overexpression of BACH1 lead to positive for STAD. The results showed that high macrophage cell outcomes. Upregulations of BACH1 was found in prostate infiltration and overexpression NRIP1 indicated the poor cancer, and the high expression of BACH1 improved the outcome of STAD (Figure 10C). Further, the relation invasion and migration of cancer cell (26). Ou et al. study between immune cell filtration and other important genes revealed that overexpression of BACH1 was associated with was performed (Figure 11) poor prognosis of breast cancer patients (27). No study investigates the functions of USP25 and SYNJ1 in any cancer. The association these defined correlated significant HPA analysis genes with NRIP1 is not evident now. The fundamental To investigate the difference, NRIP1 protein expression biological functions of NRIP1 are needed to be further levels between normal and STAD tissue. We used the HPA classified. to analyze. Interestingly, the results showed the NRIP1 The PPI network and the enrichment functions of protein downregulation in STAD (Figure 12A) compared to NRIP1 showed that the essential biological functions, normal tissue (Figure 12B). including GTPase mediated signal transduction, immune response-regulating signaling pathway, cell-substrate adhesion, DNA transcription, mRNA processing, and Discussion transcription corepressor activity. Among these biological Although the advance of diagnosing and therapy methods, processes, the immune response, and the cell-substrate the STAD is still one of the significant cancer-related death adhesion is one of the critical functions in cancer causes worldwide. The underlying mechanisms of STAD (28,29). With these, NRIP1 further is an integral part of tumorigenesis are still unclear, resistance therapy, and the tumorigenesis and progression. tumor immune response, leading to the STAD patients, Tumorigenesis is a complicated process. Many factors could not have been managed. Therefore, a more sensitive regulate oncogene expression, including TFs, kinases, and and specific novel biomarker for diagnosing STAD, even miRNAs. So, we further explored the TFs, kinases, and as the therapy target is urgently needed. From the past, miRNAs potential targets of NRIP1. From the kinase many studies’ results showed that genetic changes acted as targets of NRPI1, we can see that SYK, EGFR, CDK2, one of the most critical promotors for tumorigenesis and and P21 activated kinases (PAKs) targets of NRIP1. The therapy resistance. Among the genetic changes, the NRIP1 overexpression of SYK indicated the poor prognosis of had been observed aberration in multiple cancer types defined solid tumor types (30). In squamous cell carcinoma (9,14). The fundamental function of NRIP1 in cancer is still of the head and neck (SCCHN), higher expression SYK led unclear. The functions of NRIP1 may be the regulator of to more mortality (31). These show SYK may function as the WNT signaling pathway, E2F signaling pathway, and the promoter of cancer progression. EGFR mutation was ER signaling. And these signaling pathways play a crucial observed in multiple cancer types, including lung cancer (32), role in multiple cancers. head and neck cancer (33), and pancreatic cancer (34), and From these, we used bioinformatics to investigate the use inhibitor of EGFR can improve the prognosis of these difference of NRIP1 expression level between normal patients. According to CDK2, it is an essential regulator of and cancer samples, further analyze the relationship of normal or cancer cell cycle, and its activity is vital for loss of NRIP1 expression level with STAD outcomes. The results proliferative control during oncogenesis. The aberration of showed that NRIP1 significantly overexpressed in STAD CDK2 is found in various cancers, including glioblastoma (35) samples compared to normal samples, and the STAD and B cell lymphoma (36). CDK2 promotes breast cancer patients’ clinical features, including age, cancer status, nodal progression via phosphorylating and activating hormone

A Purity B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell

4 STAD

0.25 0.50 0.75 1.00 0.0 0.1 0.2 0.3 0.4 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.1 0.2 0.3 0.4 0.6 0.8 1.0 NRP1 expression level (log2 TPM) NRP1 expression Infiltration level

B STAD

0.9

Copy number Deep deletion 0.6 Arm-level deletion Diploid / normal Arm-level gain 0.3 High amplication Infiltration level

0.0

B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell

C B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell NRP1 1.0 Log-rank P=0.786 Log-rank P=0.554 Log-rank P=0.23 Log-rank P=0.004 Log-rank P=0.436 Log-rank P=0.12 Log-rank P=0

0.8

STAD Level

0.6 Low (Bottom 50%) High (Top 50%) Cumulative survival 0.4

0 40 80 120 0 40 80 120 0 40 80 120 0 40 80 120 0 40 80 120 0 40 80 120 0 40 80 120 Time to follow-up (months)

Figure 10 The relation between immune cells infiltration and NRIP1 in STAD (Timer). (A) Association between NRIP1 and several types of immune cell infiltration. (B) Relation between aberration types and immune cell infiltration. (C) The prognostic role of various immune cells in STAD. Timer, Tumor Immune Estimation Resource; STAD, stomach adenocarcinoma.

BTLA CD200 TNFRSF14 NRP1 LAIR1 TNFSF4 CD244 LAG3 ICCS CD40LG CTLA4 CD48 CD28 CD200R1 HAVCR2 ADORA2A CD276 KIR3DL1 CD80 PDCD1 LGALS9 CD160 TNFSF14 IDO2 ICOSLG TMIGD2 VTCN1 IDO1 PDCD1LG2 HHLA2 TNFSF18 BTNL2 CD70 TNFSF9 TNFRSF8 CD27 TNFRSF25 VSIR TNFRSF4 CD40 TNFRSF18 TNFSF15 TIGIT CD274 CD86 CD44 TNFRSF9

OV LGG ACC UCS UVM LIHC KIRP GBM KIRC KICH STAD PAAD BLCA TGCT ESCA LUAD LAML LUSC READ THCA DLBC PRAD CESC SARC BRCA CHOL UCEC PCPG HNSC THYM COAD MESO SKCM

Pearson’s rho –log10 (P value)

–0.38 –0.19 0 0.19 0.38 5.63 6.26 6.89 7.52 Figure 11 The relations between the defined significant genes of immune cell infiltration and STAD. STAD, stomach adenocarcinoma.

A B

Stomach 1 Stomach cancer HPA046571 HPA046571 Female, age 65 Female, age 64 Stomach, upper Stomach (T-63000) (T-62350) Adenocarcinoma, Stomach (T-63000) NOS (M-81403) Adenocarcinoma, Normal tissue, High grade NOS (M-00100) (M-814033) Patient id: 1994 Adenocarcinoma, NOS (M-81403) Tumor cells Normal tissue, Staining: Low NOS (M-00100) Patient id: 2326 Intensity: Weak Quantity: 75%–25% Glandular cells Location: Nuclear Staining: Medium

Intensity: Moderate Quantity: >75% Location: Nuclear

Figure 12 The NRIP1 protein expression in stomach tissue (HPA). (A) NRIP1 protein expression in stomach adenocarcinoma tissue. (B) NRIP1 protein expression in normal stomach tissue. HPA, Human Protein Atlas. receptors (37). The PAKs are a family of serine-threonine MIR-212 functioned as tumor-suppressor of NSCLC by kinases that consist of 6 members, PAKs 1-6. In colorectal inhibition SOX4. For STAD, miR-212-3p was a regulator cancer cells, PAK1 knockdown resulted in inhibiting of carcinogenesis via targeting MeCP2 (49). Further cell growth, cell survival, and cell invasiveness (38). analysis showed that MIR-212 inhibited the proliferation Overexpression of PAK1 was observed in pancreatic ductal of GC by directly suppressing the retinoblastoma binding adenocarcinoma (PDAC), and the upregulation of PAK1 protein 2 (50). Liu et al. study revealed that MIR-128 had an was related to aggressiveness (39). These results showed that association with response to chemotherapy (51). Therefore, the significant kinases target of NRIP1 might play a key role MIR-106 appeared as a biomarker for STAD (52). MIR- in STAD. Regrettably, the apparent relationship between 130a was found in STAD, and miR-130a may promote cell NRPI1 and these significantly correlated genes remains migration and invasion by targeting RUNX3 (53). Among understood, no study investigates these genes in STAD. the miRNA’s targets of NRIP1, there are several miRNAs miRNAs are critical for post-transcriptional regulation have been approved. They correlate with STAD, but the of gene expression and function as a significant role in fewer miRNAs’ functions in STAD remain unclear. Further carcinogenesis. In this study, we found several significant analysis should be conducted. miRNAs of NRIP1, including MIR-181, MIR-331, MIR- Except for these factors, the cancer microenvironment 132, MIR-212, MIR-128, MIR-215, MIR-130, and MIR- also has a vital role in tumorigenesis and progression. The 106. Downregulation of MIR-181b associated with drug immune cell infiltration is one of the most critical elements resistance for AML (40), MIR-181a, and MIR-181c can of the cancer microenvironment. Few studies explore the improve the therapeutic sensitivity for chronic myeloid relation of immune cell infiltration between the prognosis leukemia patients (CML) (41,42). Dysregulation of of STAD. In our study, the results showed that NRIP1 the MIR-181 family has been observed in solid cancer, positively significant correlation with macrophage infiltration including breast cancer (43), cervical cancer (44), and in STAD, and the higher macrophage infiltration, the poorer ovarian cancer (45). In STAD, MIR-331 acts as the inhibit outcomes of STAD patients. Tumor-associated macrophages regulator of homolog 2 (HER2), in which over-expression (TAMs), show significant phenotypic heterogeneity and resulted in epithelial-mesenchymal transition (EMT) (46). diverse functional capabilities under the influence of the Overexpression of MIR-132 was found in GC, and the local tumor microenvironment. The vital role of TAMs is upregulation of MIR-132 can improve cell Proliferation via that it accelerated the angiogenesis (54), metastasis (55), retinoblastoma 1 targeting (47). The upregulation of MIR- drug resistance, and growth of cancer cell stemness (56). 132 promotes the invasion and migration of pancreatic Thus, the TAMs infiltration shows the adverse outcome of carcinoma cells by inhibiting PTEN (48). High expression the defined cancer type.

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Cite this article as: Fang D, Lu G. Expression and role of nuclear receptor-interacting protein 1 (NRIP1) in stomach adenocarcinoma. Ann Transl Med 2020;8(20):1293. doi: 10.21037/ atm-20-6197