Network Analysis Identifies Drug Targets and Small Molecules to Modulate Apoptosis Resistant Cancers

cancers

Article Network Analysis Identiﬁes Drug Targets and Small Molecules to Modulate Apoptosis Resistant Cancers

Samreen Fathima 1, Swati Sinha 1,*,† and Sainitin Donakonda 2,*,†

1 Department of Biotechnology, Faculty of Life and Allied Health Sciences, MS Ramaiah University of Applied Sciences, Bengaluru 560054, India; [email protected] 2 School of Medicine, Institute of Molecular Immunology and Experimental Oncology, Klinikum Rechts Der Isar, Technical University of Munich, 81675 Munich, Germany * Correspondence: [email protected] (S.S.); [email protected] (S.D.); Tel.: +49-17674167311 † These authors equally contributed.

Simple Summary: With the thriving efﬁcacy of cancers to evolve and evade treatment strategies targeting the apoptosis cell death mechanism, it has become imperative to reorient these conventional cancer therapy methods. In this study, we opted for an in-silico approach to mine the essential non-apoptotic cell death genes. We holistically examined the extensive protein–protein interaction networks in three such cancers with poor prognosis: colon adenocarcinoma, glioblastoma multiforme, and small cell lung cancer. Our analysis identiﬁed non-apoptotic cell death drug targets.

Abstract: Programed cell death or apoptosis fails to induce cell death in many recalcitrant cancers. Thus, there is an emerging need to activate the alternate cell death pathways in such cancers. In this study, we analyzed the apoptosis-resistant colon adenocarcinoma, glioblastoma multiforme, and small cell lung cancers transcriptome profiles. We extracted clusters of non-apoptotic cell death Citation: Fathima, S.; Sinha, S.; genes from each cancer to understand functional networks affected by these genes and their role in Donakonda, S. Network Analysis Identifies Drug Targets and Small the induction of cell death when apoptosis fails. We identified transcription factors regulating cell Molecules to Modulate Apoptosis death genes and protein–protein interaction networks to understand their role in regulating cell death Resistant Cancers. Cancers 2021, 13, mechanisms. Topological analysis of networks yielded FANCD2 (ferroptosis, negative regulator, 851. https://doi.org/10.3390/ down), NCOA4 (ferroptosis, up), IKBKB (alkaliptosis, down), and RHOA (entotic cell death, down) cancers13040851 as potential drug targets in colon adenocarcinoma, glioblastoma multiforme, small cell lung cancer phenotypes respectively. We also assessed the miRNA association with the drug targets. We iden- Academic Editor: Krish Karuturi, tified tumor growth-related interacting partners based on the pathway information of drug-target Joshy George and Jeffrey Chuang interaction networks. The protein–protein interaction binding site between the drug targets and their interacting proteins provided an opportunity to identify small molecules that can modulate Received: 25 January 2021 the activity of functional cell death interactions in each cancer. Overall, our systematic screening of Accepted: 15 February 2021 non-apoptotic cell death-related genes uncovered targets helpful for cancer therapy. Published: 18 February 2021

Keywords: apoptosis resistant cancers; cell death; transcriptome; protein–protein Publisher’s Note: MDPI stays neutral interaction networks with regard to jurisdictional claims in published maps and institutional afﬁl- iations.

1. Introduction The apoptosis pathway is generally assigned as the only form of programed cell death that accounts for development and tissue homeostasis. However, animal models Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. deﬁcient of the necessary apoptotic machinery are known to develop naturally and attain This article is an open access article adulthood [1,2]. So, cell machinery that is an alternative to the apoptotic methods of cell distributed under the terms and death is not only present but is capable of eliminating cells in the absence or malfunction conditions of the Creative Commons of the apoptotic machinery. Apoptosis, immune cancer cells, are a signiﬁcant roadblock Attribution (CC BY) license (https:// in the mechanism of cell death in response to radiation and chemotherapy [3]. Due to creativecommons.org/licenses/by/ the cancer cells’ malleable nature, the traditional chemotherapy and radiotherapy fall 4.0/). short, and a population of recalcitrant cancer cells begins to establish itself and cause

Cancers 2021, 13, 851. https://doi.org/10.3390/cancers13040851 https://www.mdpi.com/journal/cancers Cancers 2021, 13, 851 2 of 16

recurrence. Cancers such as colon adenocarcinoma (COAD) [4], glioblastoma multiforme (GBM) [5] and small cell lung cancer (SCLC) [6] become resistant to conventional apoptosis, and available cancer therapies are rapidly proving inefficient in treating these types of cancers. Therefore, it is essential to find alternative mechanisms to induce cell death in these cancers. As discussed, activation of alternative methods presents a lucrative modality. These may either support conventional apoptosis or occur independently of it. For example, caspase-3/8 inhibition can trigger necroptosis [7,8]. A better understanding of the relationship between necroptosis and cancer may help us to develop novel strategies for cancer therapy [9]. Py- roptosis is a caspase-1/4/5/11 dependent pathway that promotes inflammatory cell death and inhibits proliferation and migration of cancer cells [10]. Ferroptosis is morphologi- cally, biochemically, and genetically distinct from other cell death forms [11]. Recently, other forms of cell death pathways such as alkaliptosis [12], entotic cell death [13], oxeiptosis [14], lysosome mediated cell death [15] and parthanatos [16] have been investigated as cancer therapeutics. Complex diseases such as cancer involve perturbations of multiple biological pathways causing molecular alterations in the process of cell death. Hence, it is essential to comprehensively check the potential cell death pathways in the resistant cancer cells. To ac- curately distinguish the forms of cell death, we need to perform functional tests such as in silico, in vitro, and in vivo studies. We recognize these complex pathways operate through a highly organized protein–protein interaction network (PPIN) to produce a functional output [17]. PPIN allows us to systematically dissect the essential proteins connected with various functional proteins that ultimately lead to pathway readout [18]. Recent reports suggest that we can explore protein–protein interaction binding sites to modulate the functionality of the two interacting protein’s using drug molecules [19]. Thus, we can take protein–protein interactions (PPIs) can be taken further to predict the drug molecules that can modulate cell death pathways in cancer tissues. The development of novel drugs for selectively activating such pathways holds great promise for eliminating malignant cells. In this study, we studied apoptosis-resistant cancers COAD, GBM, and SCLC and screened them for the non-apoptotic cell death genes (NACDGs) in their transcriptome profiles. Our systematic network-level analysis directed us to dissect distinct cell death signaling pathways and identify molecular effectors for evaluating pro-survival or re- programing mechanisms in apoptotic resistant cancers. Our analysis also allowed us to discover the probable therapeutic molecules that can open up possibilities of inducing non-conventional cell death in apoptotic immune cancer cells.

2. Results 2.1. In Silico Workﬂow to Identify the Non-Apoptotic Cell Death Genes in Cancers Evasion of apoptosis, the molecular level pathway involved in cell death, is known to cause resistance to cancer therapy [20]. Reports suggest that COAD, GBM, and SCLC progressively become resistant to apoptosis mediated cell death, and are hence immune to conventional chemotherapy or radiotherapy [4–6]. This prompted us to dissect NACDGs in COAD, GBM, and SCLC. In this current study, we utilized a computational pipeline, as shown in Figure1A. We extracted the gene expression datasets of COAD and GBM, of normal and cancer patients, from The Cancer Genome Atlas (TCGA). The SCLC gene expression dataset (normal and cancer patients) was extracted from Jiang L et al. [21]. We followed an identical statistical analysis approach to analyze the datasets and performed literature mining to extract NACDGs. Then we predicted the upstream transcription factors (TFs) and constructed the protein–protein interaction networks of NACDGs. Next, we performed the drug target prediction, miRNA drug target interaction and drug target network analysis. Finally, we screened for drug molecules against the drug targets, targeting their protein– protein interactions in each cancer phenotype. Cancers 2021, 13, 851 3 of 16

2.2. Gene Expression Analysis of COAD, GBM, and SCLC and Regulation of Non-Apoptotic Cell Death Genes To understand the governing effects in COAD, GBM, and SCLC, we separately analyzed gene expression proﬁles of these tumors. The principal component analysis (PCA) revealed clear segregation between normal and cancer patients (Figure1B–D). We identi- ﬁed following number of differentially expressed genes (DEGs) in cancer datasets: 4788 (COAD, 3214 up, 1574 down) (Table S1), 6575 (GBM, 3444 up, 3131 down) (Table S2) and 4026 (SCLC, 2575 up, 1451 down) (Table S3).

A STEP1

STEP2 STEP3 STEP4 STEP5

CAS P8 RIPKMLK 3 L Colon adenocarcinoma RFX UBE 6 Non-apoptotic ANK 2A TSC2 2 L3MB HIP1 ATF CUL 2D1 TL1 PBK 7 FOX 3 KAT TBPSRP BICD SET O3 IMPD GLS LAM TK1 SIN3 OTU RIPK2B SREB K1 H2 1 D7 A4 D5 COP HDA COP NFK G 2 B S7A TAC1 UBEF2 S TPX C3 C1KEA 3A SP1S4 BIZ MAF Drug target HIPKHMG CDC CCT KDM NCO COP K TP5 2 P1 TPTRNF2 FAM1 PELPDOK F 1 B1 3 14A 5 4D AURA1 TAC CRE S3 1 2 3 1 16 NCO17B CUE MAF NFE2MAF MOR BCR ELL CUL HIPK ZMI PPP2KA C1 B1 NFK B NMER1A IKZFTFAP A2CLTCCHUDC2 G L2 K C4 (COAD)(n=431) cell death 9 3 Z1 TNFR1 CDK LCKB1 CTN NR4 MAP EP3 K RPS CCD MYC2 NFK3 2A NB1 A1 TNN NDUF RNF1 MAP3K1 SF1AIKBK TP7 00RET DOKCBL ECH MRP 10 C106 HDA N BIA PIAS 3 T1 A13 OGD 28 9 CDK C2 G NPM 3 HSP9 HSP PPA GADD S1 S31 Prediction of ING DAX HL SMN CSE N1A CRE 1 2 0AB1 RD WAXAF 45GIP CA8 CCD ELP ZNF8 NDN PKNHOM 4 UHR X HSP A9PLCCSF2 IKBK 2 1L BBP USP F2 S100 HSPA C 1 1 C87 2 29 1 ER3 NCLCOP B1 EGFG2 RA ERBECH B KAT MAP 7 A4PML 1LHTRA AKT VPS BCK STATPAQ RABGCOR RPA S5 R 1 FGRB2 1 SMN 5 K14SIAH MD RPS TRATRAIF1TRA PTK ABL KDM 39 DK 3 R7 AP1 O1A 1 PIAS3 CSNKF3 P 2 PIK3 network analysis 1 1 M4 REL POMF2 PIASDD SYK 2 RAC1A DTN TWIS WR BLM 1RAN2A1 K R1PIK3 CAP RPS SOC EFE CHDA 121 PIAS4 1 A T1 NRCHTRIM38E4FBP9 RPAUBEKRTA2 SMAFANR2 N1 STAT 9 S7 genes retrieval TOPMP2 3 TP53 3 CA AGA SRI GNL BRW TM4S Y1 MD 1 SUM2I P10-7 D2 BMX C17o LCK GTF21 ERC CCNBP1 PPP12 BRC AIMP SH2PIK3 P2 1 3 D1 F19 NDUFA13 HuRI SFN M2 O1 YWH FAD ZNF2 rf62 NFKBIZ C2 CXX TOPH CC 2 D2AR3 HES CYB BTBH1 GLR MAPA1 AE MAPKD BLK PKN 81 MRPS31 DOK2 ZNF5C1 ORS FAS LMO SULT OGDHL D2 BCL2X3 K8 PPP2APK2 FAM2 2 GSTX1 B drug targets 58 2A1 PAR L1 TUBE MEO PICK WDF2 GADD45GIP1 RABGAP1 CSR PIN1 X2 R5C 08B CD SP1 P1 ZNF4 PARIH2K TRIP 1 BID RAF Y3 PELP1 P1 PLK BRD PEB LAS RIPO PSM 26 52 7 6 FAN1 R3 ZNF829 1 GSD3 VACYWHP1 P1WW CE RAP1 BICD1 CCDC87 ELP2 A1 MAP ROC NUFI CA8 CORO1A MDB 14 AG OX GDS1 ECHS1 K1 ATMCSN K1FANCP2 ARH ARH FAM117B ZBTB PER CNKP SRI SULT2A1 KTNK1D KCT D2 GDIA GAP1 33 2 SR1 IMPDH2 STAT3 CA9 2 1 D6 ASP DOK3 VPS39 PAQR7 TM4SF19 CASFAN RET PPARD G NFKB1 BCKDK P1 CI MORC4 SOCS7 MPR SIAH1 ANK CBL BRWD1 IP ARHG CDKN1A S1B TWIST1 ARHG EF11 ERBB2 DTNA PYCA FGR RPS9 EF12 GNL3 RD RHO RIPO HDAC2 ECH1 BMX KCN A R2 EGFR MAP3K1 NR4A1 PKN1 RAC1 HINT A2 DIAP POM121 ARHGDIA RPA2 ARH CIT H1 PTK2 IKZF3 PIK3R2 GEF2 Network ABL2 STAT1 PISD PIAS3 CCL BLK CXCL PEM SYK PIK3R1 11 22 CXC T CHP CAPN1 DKK L9 T1 in-silico analysis of SH2D2A ZNF281 CEP DPP LPCA AGAP2 HESX1 1 T1 T3 P SMAD2 KRTAP10-7 4 LPCA P ADA GPA PIK3R3 T2 A FADD PKN2 STRING CXCL T2 LPCA FAM208B CXCLCXC12 AGPAGPA T1 P MAPKAPK2 GSTCD 10 L2 T9 M 2 statistics RAP1GDS1 WDFY3 C BID LMO2 CCL11 FAS

CIT RIPOR3 LASP1 MAPK8 ANKS1B DIAPH1 drug targets NUFIP2 TRIP6 Transcription MPRIP ARHGEF11 Small Cell RHOA ARHGAP1 ROCK1 KCNA2 CNKSR1 ...... KTN1 RIPOR2 ARHGEF2 Glioblastoma factor analysis ARHGEF12 multiforme Lung Cancer DRUG BANK (GBM)(n=172) (SCLC)(n=88) TRRUST protein - protein Chea3 interaction network TTD RNA-sequencing (PPIN) DINIES Pathway analysis Cancer DR analysis

STEP8 STEP7 STEP6

Drug molecules miRNA interaction Drug target retrieval with drug targets network ChEMBL db DEMC Interacting partner database miRWalk analysis

B C D COAD GBM SCLC 50

20 50 25 variance variance 0 0 0 -20 PC2: 9.4%

PC2: 9.87% variance -25 PC2: 10.3% -50 -40

-50 -100 0 100 -90 -60 -30 0 30 -50 0 PC1: 29.52 % variance PC1: 38.7 % variance PC1: 27.26 %variance Normal (n=51) Normal (n=5) Normal(n=7) COAD (n=380) GBM (n=166) SCLC (n=79)

Figure 1. RNA-sequencing analysis of colon adenocarcinoma (COAD), glioblastoma multiforme (GBM) and small cell lung cancer (SCLC). (A) The schematic representation of data collection and integrative analysis. (B–D) Principal component analysis (PCA) built on RNA-sequencing datasets of COAD (PC1 = 29.52%, PC2 = 9.87%), GBM (PC1 = 38.7%, PC2 = 10.3%) and SCLC (PC1 = 27.26%, PC2 = 9.4%).

Next, we investigated the DEGs in each cancer responsible for NACDGs by mining the literature (see methods) in GBM (n = 17), COAD (n = 10), and SCLC (n = 7) (Figure2A). This analysis revealed the following in the GBM: we observed 2 upregulated and 15 downregulated NACDGs (Figure2B, and Table S4a). On the other hand, in COAD, 4 and 6 NACDGs are up and down-regulated (Figure2C and Table S4b). Furthermore, in SCLC, 3 and 4 are up and down-regulated (Figure2D and Table S4c). Herein after, we focused our analysis only on NACDGs. We performed intersection analysis between NACDGs and found that CYBB and CA9 are shared among all cancer datasets. Whereas LPCAT3, AURKA, GLS2, and FANCD2 are shared between the COAD and GBM, and RHOA, STAT3 Cancers 2021, 13, 851 4 of 16

are present in GBM and SCLC (Figure2E and Table S5). Overall, our transcriptome analysis identiﬁed a wide variety of non-apoptotic cell death-related genes involved in cell death in these three cancer phenotypes.

A 20 16 17

Tang.D et.al 12 NACDGs (n=71) 8 10 Overlap with COAD GBM and SCLC 7 4

DEGs Number of NACDGs

0 GBM SCLC B COAD GBM NACDGs C COAD NACDGs alkaliptosis alkaliptosis alkaliptosis (negative regulator) parthanatos entotic cell death entotic cell death ferroptosis netotic cell death ferroptosis (negative regulator) ferroptosis lysosome dependent cell death ferroptosis (negative regulator) necroptotsis necroptotsis pyroptosis MIF CA9 GLS2 CYBB CA9 CAMP TP53 GLS2 NCOA4 DPP4 AURKA CYBB RIPK1 LPCAT3 RIPK3 RHOA IKBKB STAT3 PEBP1 FANCD2 CASP1 SLC7A11 AURKA NFE2L2 LPCAT3 GSDMD FANCD2 Z-score of expression Z-score of expression -2 -1 0 1 2 -2 -1 0 1 2

E D 10.0 SCLC NACDGs Unique entotic cell death 9 lysosome dependent cell death (negative regulator) lysosome dependent cell death 7.5 oxeiptosis parthanatos 5.0 alkaliptosis (negative regulator) Common 4 4 CA9 Intersection size 3 CYBB RHOA STAT3 PARP1 NFKB1 2.5 PGAM5 2 2 Z-score of expression

-2 -1 0 1 2 0.0 SCLC COAD GBM 15 10 5 0 Number of NACDGs

Figure 2. Literature evaluation of non-apoptotic cell death genes (NACDGs). (A) The overlap between NACDGs in each cancer dataset. (B–D) The heatmaps represents the regulation of NACDGs in COAD, GBM and SCLC. (E) The UpSet plot shows the unique (single black dot) and common (connecting black dots) NACDGs across SCLC, COAD and GBM.

2.3. Upstream Transcriptional Regulation of Cell Death Genes in COAD, GBM, and SCLC Transcription factors (TFs) are vital for regulating gene expression of various genes (targets), and transcriptional response is the central guiding force to all biological systems. Given their importance, we investigated the upstream TFs that regulate NACDGs in each cancer. To predict upstream TFs of NACDGs, we utilized TRRUST v2 and Chea3 tools [22,23]. This analysis identiﬁed 13 TFs, 21 TFs, and 20 TFs in COAD, GBM, and SCLC respectively (Figure3A–C). Cancers 2021, 13, 851 5 of 16

A B C COAD GBM SCLC

BACH1 L3MBTL3 MLX ZNF385B ZBTB14 ZNF229 RELA MTERF4 SP1 MYBL1 NFE2L2 ZNF589 TIGD5 STAT2 RBPJ KLF1 FANCD2 GLS2 ETS1 NFKB1 ANKZF1 STAT3 CREB1 AURKA STAT3 NR4A1 GSDMD SP3 NFE2L2 GLS2 YY2 E2F1 PEBP1 TP53 CYBB FOXA2 SLC7A11 RHOA RHOA MBD3 STAT1 AURKA LPCAT3 CA9 RELA EP300 NCOA4 IKBKB PARP1 HDAC1 CASP1 PGAM5 ZNF367 ETS1 CA9 HMX1 CENPA DPP4 RIPK3 IRF1 CYBB SKOR2 SP1 VHL HIF1A TP53 E2F7 ZNF100 ZNF382 MBNL2 EZH2 Node shape legend EP300 HIF1A SP1 BCL6 HIF1A CYBB HDAC1 SPI1 Transcription factor Target (NACDG) FANCD2 Node color legend Node color legend Node color legend EP300 CA9 log fold change -5.12 4.57 -1.76 2 4.88 -5.07 log2 fold change 1.23 log2 fold change Down Up Down Up Down Up MIF

D E F COAD GBM SCLC 3 8 14 7 12 6 2 10 5 8 4

Degree 6 3 1 4 2 2 1 0 0 0 SP1 SP3 SP1 VHL SPl1 SP1 YY2 IRF1 MLX TP53 ETS1 E2F1 E2F7 TP53 BCL6 ETS1 RELA KLF1 RBPJ EZH2 RELA HMX1 MBD3 HIF1A HIF1A HIF1A TIGD5 STAT3 STAT1 EP300 EP300 STAT2 EP300 NR4A1 HDAC1 CREB1 SKOR2 MYBL1 HDAC1 BACH1 MBNL2 FOXA2 CENPA ZNF582 ZNF100 NFE2L2 ZBTB14 ZNF589 ZNF229 ZNF367 ANKZF1 MTERF4 ZNF385B Transcription factors Transcription factors L3MBTL3 Transcription factors

Figure 3. Transcription factor network analysis of NACDGs. (A–C) Network plots represent the predicted transcription factors (TF) regulating the NACDGs in COAD (n = 13 TFs), GBM (n = 21 TFs) and SCLC (n = 20 TFs). (D–F) Bar plots show the degree of each TF in TF-target networks. Note: red and blue color represent the up and down-regulation, respectively.

Next, we aimed to identify the prominent TFs based on the number of targets in each TF-target (TF-NACDGs) network. We computed degree of each TF in the network and found ZNF100 (3 NACDGs) in COAD (Figure3D). In the GBM TF-NACDG network, we found TP53 (15 NACDGs) (Figure3E). In SCLC, STAT3 (8 NACDGs) emerged as a critical TF (Figure3F). In sum, our analysis identiﬁed key TFs which can regulate NACDGs in COAD, GBM, and SCLC, respectively.

2.4. Protein–Protein Interaction Networks Regulated by Cell Death Genes in COAD, GBM, and SCLC To gain a deeper understanding between the NACDGs and their functional interacting proteins, connected with a variety of tumorigenic pathways in COAD, GBM, and SCLC, we analyzed PPIs between the protein products of the NACDGs and their interacting proteins (note: In the following sections, we will discuss them in the context of protein as NACDPs). We logged the NACDGs into the following databases to extract the PPIs: Human Reference Interactome map (HuRI), High-quality InTeractome database (HINT), and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING DB). Through this approach, we generated networks consisting of 41 interactions between 56 proteins, 297 interactions among 794 proteins, and 147 interactions among 319 proteins in the COAD, GBM, and SCLC, respectively (Figure S1A–C). We further analyzed these NACDPs protein– protein interaction networks (NACDP-PPINs) to understand the topological structure. These analyses demonstrated that our PPINs follow a power-law distribution and scale- free property (R = 0.61, 0.81, and, 0.81) (Kolmogorov–Smirnov (KS) = 0.24, p = 0.87, COAD), (Kolmogorov–Smirnov (KS) = 0.18, p = 0.95, GBM) and (Kolmogorov–Smirnov (KS) = 0.18, p = 0.98, SCLC) Clauset test (Figure S2A–C). Next, we ratiocinated the pathways enriched in the NACDP-PPINs in COAD, GBM, and SCLC datasets. These analyses revealed that NACDP-PPINs in COAD is involved in cell death and cell cycle pathways (Figure S3A). On the other hand, the NACDP- PPINs in the GBM were mainly enriched in cell death and signaling pathways (Figure S3B), whereas in SCLC, the NACDP-PPINs are involved in the signaling pathways such as MAPK, Rho, and NFκB pathways (Figure S3C). In sum, this analysis reinforced our workﬂow Cancers 2021, 13, 851 6 of 16

to illustrate that PPINs in each cancer are scale-free and play a role in a wide variety of tumor-related pathways.

2.5. Essential Proteins in Protein Interaction Networks Serve as Drug Targets in COAD, GBM, and SCLC Earlier reports suggest that hub proteins in PPINs are essential for proper functional readouts [24]. To scrutinize which proteins in NACDP-PPINs (as described above) are hubs, we computed the degree and Kleinberg’s hub score of each protein in the PPINs to identify the highly connected proteins. We considered the top 10 proteins, which showed high degree and Kleinberg’s hub score for further analysis; this allowed us to identify the vital proteins in each cancer dataset. In COAD macrophage migration inhibitory factor (MIF, down), vitamin D receptor (VDR), NME/NM23 nucleoside diphosphate kinase 1(NME1), androgen receptor (AR), aurora kinase A (AURKA, down), vascular cell adhesion molecule 1 (VCAM1), retinoid X receptor alpha (RXRA), nuclear receptor coactivator 4 (NCOA4, up), FA complementation group D2 (FANCD2, down) and cathelicidin antimicrobial peptide (CAMP, up) (Figure4A). In GBM tumor protein 53 (TP53), ubiquitin-conjugating enzyme E2L (UBE2l), signal transducer and activator of transcription 3 (STAT3, down), epider- mal growth factor receptor (EGFR), inhibitor of nuclear factor-kappa B kinase subunit beta (IKBKB, down), ras homolog family member A (RHOA, down), AKT serine/threonine kinase 1 (AKT1), small ubiquitin-like modifier 1 (SUMO1), RELA proto-oncogene, NF-kB subunit (RELA), TNF receptor-associated factor 2 (TRAF2) identified as hubs (Figure4B). On the other hand, in SCLC STAT3 (down), EGFR, TP53, nuclear factor kappa B subunit 1 (NFKB1, down), Poly (ADP-ribose) polymerase 1 (PARP1, up), RHOA (down), UBE2l, VCAM1, fibronectin 1 (FN1) and RELA were recognized as hubs (Figure4C). To validate the hub molecules identified using the Kleinberg hub centrality hub score and degree, we computed two independent centrality parameters: closeness (an indicator of communication) and betweenness (an indicator of information flow). This analysis revealed 80% of hub proteins identified in Kleinberg’s hub score, and degree analysis (Figure S4A–D) also showed high closeness and betweenness, thus corroborating our hub analysis. We compared hubs to identify the common and unique hubs across the cancer datasets. This analysis revealed that VCAM1 is present in both COAD and SCLC, and RHOA (down), RELA, TP53, EGFR, UBE2l, STAT3 (down) are shared by the GBM and SCLC (Figure4D and Table S6). The essential proteins in COAD are involved in cell death (NACDP) and differentiation pathways (Figure4E). In GBM, the hubs play a role in cell cycle arrest, cell death (NACDP), and signaling pathways (Figure4F) , whereas the hubs in SCLC are involved in cell death (NACDP) and EGFR signaling pathways (Figure4G). Apart from the hubs, we aimed to screen the NACDP-PPINs in each cancer dataset for drug targets. For this purpose, we calculated the following two scores: the first one is eccentricity, which shows the protein’s ability to be functionally available to all other proteins in NACDP-PPIN. The second one is coreness: this reveals the protein’s local and global importance in a given NACDP-PPIN. We utilized these measures to cull the drug targets in each cancer PPINs. As the main focus of our study is to identify critical non-apoptotic cell death genes, our examination uncovered the following NACDPs: MIF (down, pathanatos), AURKA (down, necroptosis), FANCD2 (down, ferroptosis (negative regulator)), CAMP (up, netotic cell death) and NCOA4 (up, ferroptosis) in COAD (Figure4H), STAT3 (down, lysosome dependent cell death), IKBKB (down, alkaliptosis) and RHOA (down, entotic cell death) in GBM, and STAT3 (down, lysosome dependent cell death), NFκB1 (down, lysosome dependent cell death negative regulator), RHOA (down, entotic cell death) and PARP1 (up, pathanatos) in SCLC, respectively (Figure4I–J). Interestingly, all these targets showed high eccentricity and coreness values; thus, we considered them as a drug target. Taken together, our topological analyses revealed essential proteins in PPINs and drug targets in each of the cancer dataset. Cancers 2021, 13, 851 7 of 16

A COAD B GBM C SCLC 1.00 1.00 1.00

0.75 0.75 0.75

0.50 0.50 0.50

NFκB1

Kleinberg's hubscore 0.25 0.25 0.25

0.00 0.00 0.00 0 1 2 3 4 5 6 7 0 20 4060 80 100 120 0 10 20 3040 50 60 70 80 Degree Degree Degree D E F G Degree COAD GBM SCLC 10.0 MIF TP53 STAT3 Unique AURKA STAT3 NFκB1 9 NME1 UBE2l RHOA 7.5 VDR EGFR EGFR Common AR IKBKB PARP1 6 FANCD2 RHOA TP53 5.0 CAMP TRAF2 UBE2I VCAM1 SUMO1 VCAM1 4 RXRA RELA FN1 2.5 3

Intersection size AKT1 RELA

elccearrest cycle Cell

eldifferentiation Cell

GRsgaigpathway signaling EGFR

yooedpnetcl death cell dependent Lysosome

noi eldeath cell Entotic

N repair DNA

elcycle Cell signaling NFKB

elccearrest cycle Cell elcycle Cell

NCOA4 adhesion Focal

noi eldeath cell Entotic GRsgaigpathway signaling EGFR Parthanatos

FBsignaling NFKB

yooedpnetcl death cell dependent Lysosome

eladhesion Cell

Apoptosis

y.dp eldah(neg.regulaton) death cell dep. Lys.

Alkaliptosis

nrymetabolism Energy

eldifferentiation Cell

Ferroptosis

nrgnrcpo signaling receptor Androgen

Apoptosis

eoi eldeath cell Netotic

Parthanatos

eladhesion Cell

erpoi e.regulator neg. Ferroptosis Necroptosis

1 0.0 COAD GBM SCLC 10.07.5 5.02.50.0 Number of hubs Legend

Yes No

H I J COAD GBM SCLC MIF 10 TP53 7 STAT3 6 AURKA STAT3 6 NFκB1 NME1 8 UBE2l 5 5 RHOA VDR EGFR EGFR 4 AR 6 IKBKB 4 PARP1 3 FANCD2 RHOA 3 TP53 CAMP 4 TRAF2 2 UBE2I 2 VCAM1 2 SUMO1 VCAM1 RXRA RELA 1 FN1 1

NCOA4 0 AKT1 0

Eccentricity

Eccentricity Coreness

Coreness RELA 0

Eccentricity Coreness

Figure 4. Hub protein analysis of NACDP protein–protein interaction networks. (A–C) The topology analysis of COAD, GBM, and SCLC protein–protein interaction networks, including Kleinberg’s hub centrality score and degree, divulge the top 10 hub proteins. (D) The UpSet plot shows the common and unique hubs across the cancer datasets. (E–G) Pathways of top 10 hub proteins in COAD, GBM, and SCLC. (H–J) Heatmaps represent the eccentricity and coreness of hub proteins in COAD, GBM, and SCLC, respectively. Note: Red and blue color denote up and down-regulation, respectively.

2.6. Drug Target Validation and miRNA Regulatory Effect in COAD, GBM, and SCLC As discussed above, our NACDP-PPINs topology analysis forecasted the drug targets in COAD, GBM, and SCLC cancer datasets. We used these predicted drug targets to derive the subnetworks from PPINs (Figure5A–C). Our pathway enrichment analysis of the COAD drug-target subnetwork showed that mitosis, Fanconi anemia pathway, and ER-stress are modulated (Figure S5A). The GBM drug-target subnetwork is enriched with the signaling pathways (MAPK and Rho), cell death, and cell motility (Figure S5B). On the other hand, the SCLC drug-target subnetwork is enriched with regulating cell death, MAPK, and NFκB signaling pathways (Figure S5C). To ﬁlter the key new drug targets, we Cancers 2021, 13, 851 8 of 16

further performed in silico validation of the predicted drug targets using various drug- target databases, such as the Drug Bank, Therapeutic Target Database (TTD), Drug-target Interaction Network Inference Engine Based on Supervised analysis (DINIES), and Cancer Drug Resistance Database (Cancer DR) (see methods). This analysis shows that in COAD, Cancers 2021, 13, x FOR PEER REVIEW 8 of 16 AURKA (down) is identiﬁed in all databases, MIF (down) and CAMP are present in 2/4 databases. Interestingly, we found two targets, FANCD2 (down) and NCOA4 (up), are not present in any of the database (Figure S6A). In GBM, STAT3 (down) is spotted in (3/4) (3/4)databases databases and and IKBKB, IKBKB, RHOA RHOA (1/4) (1/4 databases) databases (Figure (Figure S6B). S6B). In In SCLC, SCLC, NF NFκB1κB1 (down) (down) is isacknowledged acknowledged in in all all databases. databases. PARP1 PARP1 (up) (up) and and STAT3 STAT3 (down) (down) are foundare found in (3/4) in (3/4) databases, databases,whereas whereas RHOA RHOA is identiﬁed is identified only only in one in resourceone resource (Figure (Figure S6C). S6C). Finally, Finally, we we considered consid- eredtargets targets which which are notare foundnot found in any in any or found or found in only in only one drugone drug target target database database as a as new a newvalid valid drug drug target. target. Thereby, Thereby, we proceeded we proceeded our analysis our analysis by considering by considering FANCD2, FANCD2, NCOA4 NCOA4(COAD), (COAD), IKBKB (GBM),IKBKB and(GBM), RHOA and (GBMRHOA and (GBM SCLC) and as SCLC) the central as the targets. central targets.

A COAD B GBM

TPX2 TACC1 PELP1 COPS4 TACC3 COPS3 MORC4 EGFR DOK2 IMPDH2 COPS5 DOK3 AURKA IKZF3 RELA MYCN HDAC2 NDUFA13 COPS7A TNFRSF1A ECHS1 MRPS31 CUEDC2 OGDHL KEAP1 TP53 PPARD CREBBP FAM117B BNIPL NME1 CTNNB1 GADD45GIP1 CA8 ZNF829 CSF2RA BICD1 CCDC87 ELP2 VCAM1 NFKB1 IKBKB SP1 NR4A1 MIF EGFR TRAF2 CBL WDYHV1 COPS5 AKT1 ERBB2 ECH1 IKBKG FOXO3 CORO1A TRAF1 FGR CLTC VPS39 BCKDK STAT3 PAQR7 RABGAP1 NFKBIZ TP73 GORASP2 PTK2 CHUK ABL2 HSP90AB1 RET PIK3R1 LCK SYK RAC1 DTNA TM4SF19 VDR PIK3R2 CAPN1 RPS9 TWIST1 SOCS7 RXRA MAPK8 PIAS3 SMAD2 FANCA POM121 RPA2 STAT1 KRTAP10-7 AGAP2 SRI GNL3 BRWD1 NCOA4 MID2 SH2D2A PIK3R3 BMX SULT2A1 AR FADD ZNF281 HESX1 MAPKAPK2 BLK PKN2 FAS LMO2 HLA-DRB1 FAM208B GSTCD WDFY3 TRIP6 BID BRCA1 KAT5 LASP1 RIPOR3 CAMP RAP1GDS1 ROCK1 NUFIP2 FANCD2 MAPK8 ARHGDIA ARHGAP1 FANCI HOMER3 ATM PKN1 HLA-DRA MPRIP Node color legend FANCE Node color legend ANKS1B CDKN1A ARHGEF11

ARHGEF12 SIAH1 -1.02 log2 fold change -1.09 -1.07 log2 fold change -1.54 down down MAP3K1 down down RIPOR2 KTN1 RHOA KCNA2 CNKSR1 DIAPH1 ARHGEF2 C SCLC CIT

ANKS1B CIT KCNA2 ARHGEF2 ARHGEF11 RIPOR3 KTN1 RAP1GDS1 RIPOR2 CNKSR1 AGAP2 ARHGAP1 SP1 PKN2 HDAC1 SRI HDAC2 ARHGDIA ARHGEF12 CORO1A VPS39 RHOA SOCS7 RAC1 MPRIP CAPN1 DIAPH1 ABL2 SULT2A1 RPA2 BID GNL3 RPS9 PKN1 MORC4 IMPDH2 ROCK1 ZNF829 BCKDK IKZF3 DOK2 OGDHL LASP1 DOK3 FAS ECHS1 SYK MAPK8 FADD GADD45GIP1 CA8 VCAM1 TRIP6 CBL PIK3R2 BLK CDKN1A SH2D2A BMX SMAD2 MAP3K1 STAT3 LCK RET TM4SF19 PAQR7 LMO2 CCDC87 DTNA ELP2 FGR PTK2 EGFR SIAH1 TRIM29 POLA1 UBE2I TOP1 HESX1 KRTAP10-7 POM121 RABGAP1 MRPS31 ERBB2 NCAPD2 ZNF281 PELP1 TP53 MAPKAPK2 IL24 BRWD1 WDFY3 STAT1 NUFIP2 PPARD TWIST1 FAM208B PIAS3 POU2F1 PARP1 ZNF423 GSTCD NDUFA13 PIK3R3 CTNNB1 HSPA1L BICD1 ECH1 NFKBIZ NR4A1 TCF3 APTX NFKBIA XRCC1 FAM117B COPS5 HSP90AB1 TXN IKBKB LIG3 RELA POLA2OARD1 PIK3R1 MAP3K8 HMGA2 NFKB1 Node color legend Node shape legend LYL1 TNIP2 NFKBIE RELB Drug target COPB2 -1.56 log fold change 1.12 TRIP4 2 down down PDCD11 Interacting HIF1AN PLD3 partner MPP6 TNIP1 ABCC2 NFKB2

Figure 5. Drug target networks. (A–C) These network plots represent the drug targets and their interacting partners in Figure 5. Drug target networks. (A–C) These network plots represent the drug targets and their interacting partners in COAD,COAD, GBM, GBM, and and SCLC. SCLC. Note: Note: node node color color red red an andd blue blue signify signify the the up up and and down-regulation. down-regulation. It is known that miRNAs are dynamically associated with NACDP-PPINs [24]. There- It is known that miRNAs are dynamically associated with NACDP-PPINs [24]. fore, we explored the possible miRNA regulatory effect on drug targets in COAD, GBM, Therefore, we explored the possible miRNA regulatory effect on drug targets in COAD, and SCLC. We extracted miRNAs based on the prominent expression proﬁle. This analysis GBM, and SCLC. We extracted miRNAs based on the prominent expression profile. This enabled us to identify COAD speciﬁc 202, 204 miRNAs targeting FANCD2 and NCOA4, analysis enabled us to identify COAD specific 202, 204 miRNAs targeting FANCD2 and respectively (Figure S7A). Based on the expression, further investigations revealed that NCOA4, respectively (Figure S7A). Based on the expression, further investigations revealed that hsa-miR-492 (log2FC 1.37) miRNA is highly upregulated, targeting FANCD2 and NCOA4. In the GBM, we identified 98 miRNAs targeting IKBKB (Figure S7B). Dis- section of these modules captured highly upregulated miRNA hsa-miR-138-5p (log2FC 8.11) which regulates IKBKB. Finally, in GBM and SCLC, we identified 73 miRNAs regulating RHOA. Further anatomization of these miRNA networks identified highly upregulated miRNA hsa-miR-181a-5p targeting RHOA (log2FC 5.32) (Figure S7B). Overall, in

Cancers 2021, 13, 851 9 of 16

hsa-miR-492 (log2FC 1.37) miRNA is highly upregulated, targeting FANCD2 and NCOA4. In the GBM, we identified 98 miRNAs targeting IKBKB (Figure S7B). Dissection of these modules captured highly upregulated miRNA hsa-miR-138-5p (log2FC 8.11) which regulates IKBKB. Finally, in GBM and SCLC, we identified 73 miRNAs regulating RHOA. Further anatomization of these miRNA networks identified highly upregulated miRNA hsa-miR-181a-5p targeting RHOA (log2FC 5.32) (Figure S7B). Overall, in silico database analysis identified new drug targets, and our miRNA network analyses showed important cancer-specific miRNAs regulating drug targets.

2.7. Drug Target Network Predicts Key Interactions in COAD, GBM, and SCLC and Small Molecules Next, we aimed to understand the critical functional interactions of those drug targets in each cancer. Towards this end, we selected each drug target’s interacting proteins of each drug target based on the vital tumor-related pathways in each tumor. In COAD, we found FANCI, which plays a role in Fanconi anemia pathway, as an interacting partner of FANCD2 (down, ferroptosis negative regulator). The second target in COAD, NCOA4 (ferroptosis, up), interacts with apoptotic protein RXRA. In GBM we identified KEAP1, a regulator of the apoptotic pathway, as an interacting partner of IKBKB (down, alkaliptosis). RHOA (down, entotic cell death) interacts with RHO signaling and apoptotic molecule ARHGEF2 in GBM and SCLC. Once we had these interaction data in hand, we sought to predict the partner-specific interface residues between the drug target and their interacting partner at the structural level. To do this, we retrieved experimentally derived structures of FANCD2, FA complementation group I (FANCI), RXRA, IKBKB, KElch-like ECH associated protein 1 (KEAP1), RHOA, and Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) from the PDB database. However, we did not find the structure of the drug target NCOA4 protein structure; thus, we computationally modeled the 3D structure of NCOA4 using ITASSER (Figure S8A). We performed energy minimization and removed steric clashes followed by validation using ProSA by comparing the 3D structure of NCOA4 with experimental structures (X-ray and NMR). This analysis showed that the NCOA4 model is in the range of X-ray based structure (z-score: −5.98) (Figure S8B). We executed protein–protein docking using the HDOCK server (see methods) and identified critical residues of each drug target responsible for interaction with the above discussed interacting partners in cancer datasets (Figure S9A–C). To identify the drug molecules which can target the protein–protein interactions of predicted drug targets we extracted drug molecules from ChEMBL database for each target. This analysis highlighted Doxorubicin hydrochloride and Lenvatinib targets FANCD2, NCOA4 (COAD). Wortmannin targets IKBKB (GBM), and Clausine E binds to RHOA (GBM, SCLC). Overall, our drug-target interaction network analysis revealed vital drug targets and their key functional interacting partners along with drug molecules in COAD, GBM, and SCLC cancers.

3. Discussion The primary mechanism of cell death in response to chemo and radiotherapy is apoptosis. It is the failure of apoptosis in cancer cells that causes a severe clinical problem. Due to apoptosis pathway resistance, COAD, GBM, and SCLC hinder the treatment strategies in clinics. As an alternative route of cell death in these cancers, it is imperative to target non-apoptotic pathways for new anticancer drugs. So, activation of non-conventional methods of cell death holds the promise of cancer amelioration. Recent advances in cell death mechanisms have brought the alternatives to apoptosis into focus [24]. The abundance of genomic data related to these cancers allows us to explore genes responsible for various non-conventional cell death pathways. In this study, we extracted normal and patient transcriptome proﬁles of COAD, GBM, and SCLC. Analysis of these datasets highlighted clusters of NACDGs involved in alkaliptosis, entotic cell death, Cancers 2021, 13, 851 10 of 16

ferroptosis, netotic cell death, necroptosis, lysosome dependent cell death, parthanatos and oxeiptosis pathways (Figure2B–D). Parthanatos was previously linked to GBM [25], and our current research shows its involvement in COAD and SCLC. Mechanisms such as entotic cell death is observed in the breast [26], colon [27], and pancreatic carcinoma [28]. Non-apoptotic mechanisms involving autophagy proteins and lysosome-mediated cell digestion could also be potentially targeted in cancer tissues. In cancer cells that have an increased iron demand compared to non- cancer cells. This iron dependency can make cancer cells more vulnerable to iron-catalyzed necrosis, referred to as ferroptosis [29]. These observations suggest that alternative cell death pathways can be activated in apoptotic resistant cancers, and this offers promise in cancer therapy. We used TRRUST and ChEA tools to identify the upstream transcription factors (TFs) responsible for the transcriptional response for each NACDGs in the cancer phenotypes. This approach revealed that 13 (COAD), 21 (GBM), and 20 (SCLC) TFs are possibly regulating NACDGs (Figure3A–C). Further analysis of TF-target networks identified ZNF100 (COAD); the factor known to sensitize the tumor cells [30], TP53 (GBM), a well-known regulator of cell cycle arrest, and cell death pathways [31] and STAT3 (SCLC), which plays a role in cell proliferation, survival [32] (Figure3D–F). TFs control a high number of NACDGs in these three recalcitrant cancers. After identifying the transcription factors controlling the NACDGs, we investigated the proteins that interact with the NACDGs gene product, to gain further insight into their role in inducing cell death in these three cancers, individually. The NACDP-PPINs follow a scale-free pattern (Figure S2), and topological analysis exposed the top 10 hub proteins in COAD, GBM, and SCLC (Figure4A–C and Figure S4). NACDP-PPINs play a role in various signaling pathways and cell growth-related functions. Furthermore, hub proteins were subjected to further analysis to predict drug targets (Figure4G–I). We cross-validated these predicted drug targets with multiple drug-target databases (Figure S6A–C). We identified two drug targets in COAD as follows: the first one is FANCD2 (ferroptosis negative regulator, down), and it is involved in ferroptosis, a newly described cell death mechanism potentially used as cancer therapy [33]. The second one is NCOA4 (ferroptosis, up). The overexpression of NCOA4 is known to promote ferroptosis [34]. In GBM, we observed IKBKB (down, alkaliptosis) as a target; it regulates alkaliptosis in tumor tissues [12]. Intriguingly, we observed RHOA (down, entotic cell death) as a common drug target in GBM and SCLC. Entosis regulates the malignant cells [35]. We found the following TFs targeting drug targets: TF ZNF100 targets FANCD2 and BACH1, KLF1, ZBTB14 interacts with NCOA4 in COAD. TP53 TF interacts with IKBKB (GBM) and RHOA in (GBM and SCLC). This validates our presumption that TFs play a significant role in guiding protein interaction of the NACDGs. Further experimentation is needed to delineate the molecular mechanism of TFs targeting the drug targets in each cancer. miRNAs are known to regulate the protein production [36], and they also play an important role in harmonizing the protein abundance [37]. Thus, to interpret the post- transcriptional regulation on drug targets predicted from protein interaction analysis, we performed miRNA-protein interaction analysis. These analyses revealed that hsa- miR-492 is a significantly upregulated miRNA targeting the FANCD2, NCOA4 in COAD (Figure S7A). miR-492 is known to enhance tumor growth inhibition in colorectal cancer [38]. hsa-miR-138-5p (up) regulating IKBKB in GBM (Figure S7B), is reported to suppress tumor development in GBM [39]. We observed that hsa-miR-181a-5p (up) connected to RHOA in GBM and SCLC (Figure S7B) is known to regulate the cell growth pathways in tumors [40]. Thus, we believe that the connection between identified miRNAs and cell death related drug targets will open up therapeutic avenues in treating cancer. Given our findings, we went on to analyze the drug-target interaction networks. Our pathway analysis augmented that these networks are involved in various pathways related to signaling pathways and tumor growth-related pathways (Figure5 and Figure S5). In modern drug discovery, one of the preferred methods is to target PPIs [41] with drug Cancers 2021, 13, 851 11 of 16

molecules that can efﬁciently modulate the interactions responsible for multiple functions. This prompted us to explore how we can use drug targets and their interacting proteins for therapeutic purposes. To this end, we mined interacting partners of drug targets based on the functions important for tumorigenesis. This approach revealed the FANCI (Fanconi anemia pathway) as a partner of FANCD2 (ferroptosis negative regulator, up) in COAD (Figure5A). Reports suggest that the Fanconi anemia pathway plays a role in interstrand crosslink repair of cancer tissues [42]. The second target, NCOA4 in COAD (ferroptosis, up), interacts with RXRA (apoptosis). It is shown that RXRA induces cell death in tumor tissues [43]. In GBM, we found that KEAP1 (apoptosis) interacts with IKBKB (alkaliptosis, down) (Figure5B). It is reported that KEAP1 regulates cancer cell growth [ 44]. We observed the ARHGEF2 (RHOA signaling) connected with RHOA (entotic cell death, down) in GBM and SCLC (Figure5C). ARHGEF2 plays a role in cell migration in tumor tissues [ 45]. To identify the small molecules to target the PPI, we need to understand the PPI binding site using protein–protein docking. Thus, we predicted the binding site responsible for each drug target and its interacting partner in COAD, GBM, and SCLC (Figure S9A–C). Our study aims to identify the reliable drug molecules that could be used directly for experimental validation in the future. In brief, we used following parameters: ﬁrst we extracted the drugs from the CheMBL database for each drug target based on the Lipinski rule of 5 (RO5), which deals with drug likeliness of molecules [46]. Second, we considered drug molecules with anti-cancer potential for further analysis. Here, we show that Doxorubicin hydrochloride (D-HCL) targets FANCD2 in COAD, and interestingly, Dox- orubicin induces cell death in cancer cells [47]. This reveals that D-HCL has the potential to attenuate cancer growth. Lenvatinib targets NCOA4 in COAD; this drug is known to reduce tumor growth, and it is tested in phase 2 clinical trials to treat gastric cancer patients [48,49]. In GBM, IKBKB can be targeted by wortmannin. This molecule curbs cancer cell proliferation and promotes cell death [50]. Clausine E binds to RHOA in GBM and SCLC. Interestingly, this small molecule belongs to the class of clausines which are known to impart anti-proliferative potential in cancer cells [51]. This study provides a view of non-apoptotic cell death interactions and predicted drug targets intertwined with proteins involved in tumor growth pathways. Our analysis also revealed small molecules that could bind to the drug targets, and it paves a path to experimental validation to modulate the protein–protein interactions of NACDGs in cancer cells.

4. Materials and Methods 4.1. RNA-Sequencing Data Analysis We retrieved RSEM normalized expression data related to COAD, GBM normal, and cancer patients from The TCGA firehouse (https://gdac.broadinstitute.org/). The SCLC patient gene expression dataset (normal and cancer) acquired from the GEO database, de- posited under accession number: GSE60052 [21], raw reads were further processed by the GRIEN database [52]. All the data analysis was performed separately for each cancer. We performed principal component analysis using the ‘prcomp’ R function. DEGs identified using normalized expression data through the DESeq2 package, which is a part of integrated Differential Expression and Pathway analysis (iDEP v.90) software [53]. We considered genes as a DEG that satisfied the following criteria: log2 fold change 1 (absolute fold change: 2) and p-adjusted-value ≤ 0.05.

4.2. Literature Mining of Non-Apoptotic Cell Death Genes We performed literature mining [54] to extract NACDGs related to following pathways: necroptosis, pyroptosis, ferroptosis, parthanatos, entotic cell death, netotic cell death, lysosome dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis. We pulled 71 NACDGs from table 1 [54]. We overlapped these NACDGs with DEGs extracted from COAD, GBM, and SCLC and focused on this subset of non-apoptotic cell death genes for further analysis. Cancers 2021, 13, 851 12 of 16

4.3. Transcription Factor Network Analysis Transcription factor (TF) enrichment analysis was done using TRRUSTv2 (https: //www.grnpedia.org/trrust/) and Chea3 tool (https://maayanlab.cloud/chea3/)[22,23]. Initially, we logged NACDGs on to TRRUST v2 to retrieve the TFs connected to NACDGs. If we did not ﬁnd a NACDG in TRRUST, we uploaded them to the Chea3 tool to extract the TFs. Finally, we merged the TF-targets networks downloaded from TRRUSTv2 and Chea3 tool, and they visualized them as networks using Cytoscape v3.7.1 [55]. We computed each TF’s degree to identify the key TF in each network using network analyzer cytoscape plugin.

4.4. Protein–Protein Interaction Network Analysis We used three high-quality resources to extract protein–protein interactions of differentially expressed NACDGs in COAD, GBM, and SCLC by uploading them to the following databases: first, we uploaded NACDGs to HuRI (Human Reference Interactome map), which contains ~50,000 interactions (http://www.interactome-atlas.org)[56]. If protein interaction of NACDG was not found in HuRI, then we connected them to the second database, HINT, which contains ~53,000 high quality physical binary protein–protein interactions (http://hint.yulab.org)[57]. Finally, if we did not find protein interaction of NACDG in the HuRI and HINT databases, we used the STRINGV11 database to extract protein interaction data for those NACDGs (https://string-db.org)[58]. This database consists of all known and predicted protein–protein interactions based on the experimental, text mining and co-expression evidence of human organism. We retrieved the interactions with a 0.7 confidence score from STRING DB. The PPI networks were pictured in Cytoscape v3.7.1 [55].

4.5. Drug Target Prediction from Protein–Protein Interaction Networks and Validation We used the graph theory to analyze the topological importance of a node in protein– protein interaction networks. This approach can assess the vitalness of each protein in the network. In our study, we utilized the four topological metrics to predict drug targets. We used the following parameters to identify the hub proteins: we computed degree by counting the number of links of each node (protein). Kleinberg’s hub score is based on the following notion where a graph k and hubs of k identiﬁed by adjacency matrix b of k by cal- culating the eigenvectors [59]. In order to validate hubs identiﬁed by Kleinberg’s hub score and degree, we calculated two centrality parameters closeness and betweenness. The closeness used to assess the overall vicinity of a node (protein) to other nodes in the PPIN in turn measures the effectiveness of the communication of the node [60]. Betweenness com- putes the nonredundant shortest paths passing through a node. Elevated betweenness indicates the central proteins in the PPIN [61]. To mine drug targets from these hub proteins, we further analyzed them with eccentricity and coreness metrics [18]. We validated drug target proteins as follows: we uploaded these target proteins to databases such as Drug Bank (https://go.drugbank.com)[62], TTD (http://db.idrblab.net/ttd/) [63], DINIES (https://www.genome.jp/tools/dinies/)[63], and Cancer DR (http://crdd.osdd. net/raghava/cancerdr/)[64]. Drug target not found in none or found in one of the databases was considered as a new target for further analysis.

4.6. Identiﬁcation of miRNA Regulating Drug Targets To extract the cancer-speciﬁc (Colon adenocarcinoma, Glioblastoma multiforme, and Small Cell Lung Cancer) miRNAs, we used dbDEMC v2.0 (https://www.picb.ac.cn/ dbDEMC/)[65]. dbDEMC also provides log2 fold changes for each miRNA derived from gene expression studies. We culled miRNAs (Human organism) with drug targets proteins obtained from PPIN networks characteristic to each cancer from miRWalk v3.0 (http://mirwalk.umm.uni-heidelberg.de) [66]. We merged miRNAs from dbDEMC with miRNA-drug target networks. Finally, the most upregulated miRNAs are retained for further analysis. Cancers 2021, 13, 851 13 of 16

4.7. Pathway Enrichment Analysis of Networks We performed the pathway analysis of proteins from interaction networks using METASCAPE (https://metascape.org/gp/index.html#/main/step1)[67] which contained the KEGG pathways, gene ontology biological process. The pathways considered statisti- cally signiﬁcant using a p-adjusted-value ≤ 0.05.

4.8. Drug Target Interaction Network Analysis and Prediction of PPI Binding Sites Identification of Drugs We derived the protein interacting partners of drug target proteins from protein– protein interaction networks. We extracted the direct interacting partners of each drug target responsible for tumor-related pathways. Experimental based structures of drug targets (COAD: FANCD2-FANCI complex, PDB id- 6VAD, GBM: IKBKB, PDB id- 4KIK, GBM and SCLC: RHOA, PDB id- 1CC0) and their interacting partners (COAD: RXRA, PDB id- 6JNO, GBM: KEAP1, PDB id- 6SP1, GBM and SCLC ARHGEF2, PDB id- 5EFX) and their interacting partners were downloaded from PDB database (https://www.rcsb.org/). We removed the ligands present in these .PDB files and took only structures for analysis. To model NCOA4 3D structure a drug target in COAD, we used the following procedure: first retrieved the sequence of NCOA4 in fasta format from the Uniprot database (https://www.uniprot.org) [68] (uniport id: Q13772, homo sapiens). This sequence was given as an input to the online ITASSER software (https://zhanglab.ccmb. med.umich.edu/I-TASSER/)[69] to model the 3D structure NCOA4. The model aligned with PDB id: 6N7PX (S. cerevisiae spliceosomal E complex, aligned residues coverage score: 0.94) NCOA4 model was retrieved and minimized using the ModRefiner tool (https://zhanglab.ccmb.med.umich.edu/ModRefiner/) and corrected side chains of the structure via Chiron tools (https://dokhlab.med.psu.edu/chiron/login.php)[70,71]. We used ProSA-web (https://prosa.services.came.sbg.ac.at/prosa.php)[72] to compute the z-score of the NCOA4 structure to compare it with experimental derived structures (X-ray or NMR). To identify the binding site residues between the drug target and interacting partner, we performed protein–protein docking using HDOCK online server [73]. We extracted drugs from the CheMBL online database (https://www.ebi.ac.uk/chembl/)[74] based on the Lipinski rule of 5 (RO5), which measures drug likeliness of small molecules [46] and known cancer drugs. Finally, drug molecules that satisfied these parameters and target the drug target proteins from cancer datasets were retained for further analysis.

4.9. Statistical Analysis and Data Visualization The statistical analysis and visualization in this study were done using the R statistical software v3.5.1 (https://www.r-project.org/). The principal component analysis was visualized as a scatter plot using the ggplot2 R package. We computed z-score of expression for NACDGs in each cancer using heatmap.2 function in gplots R package. Common and unique genes/proteins were visualized as a barplot using the UpSet R package. We plotted the node degree distribution of protein–protein interaction networks using the igraphv1.0.0 R package (https://igraph.org/r/). Statistical evaluation of power-law distributions done using Clauset’s method [75]. This method uses a goodness-of-fit test based on the Kolmogorov–Smirnov (KS) statistic to compute the null hypothesis. This hypothesis rejected with significance (p-value < 0.1), which done with power law fit function in the igraph package in R. We used igraph R package to compute the Kleinberg’s hub score, degree, closeness, betweenness, eccentricity, and coreness. Scatter plots related to hub analysis and barplots related to the number of NACDGs and pathway enrichment analysis, were rendered using the ggplot R package. Heatmaps generated using the pheatmap R package. We utilized Pymol v2.7 software to visualize the protein–protein complexes.

5. Conclusions This study took advantage of publicly available apoptosis-resistant colon adenocarcinoma (COAD), glioblastoma multiforme (GBM) and small cell lung cancer (SCLC), Cancers 2021, 13, 851 14 of 16

transcriptome datasets, and performed gene expression and layered network analyses. This approach highlighted non-apoptotic cell death genes as well as vital transcription factors and miRNA regulating these genes. Through the protein–protein interaction network analysis, we connected non-apoptotic cell death proteins with various tumorigenic functions and predicted drug targets along with small molecules. Our study lays the foundation for more comprehensive experimental study (in vitro, in vivo) on modulating the cell death drug targets FANCD2, NCOA4 (COAD), IKBKB, (GBM), and RHOA (GBM and SCLC), to improve the therapeutic avenues in cancer treatment.

Supplementary Materials: The following are available online at https://www.mdpi.com/2072-6 694/13/4/851/s1, Figure S1: Protein–protein interaction networks regulated by cell death genes in COAD, GBM and SCLC cells. Figure S2: Topological analysis of cell death regulated protein–protein interaction networks (PPIN) in the COAD, GBM and SCLC. Figure S3: Pathway enrichment analysis of PPINs. Figure S4: Hub validation analysis using closeness and betweenness. Figure S5: Pathway enrichment analysis of drug target networks. Figure S6: Drug target database validation analysis. Figure S7: miRNA and drug target networks. Figure S8: Structure model of NCOA4 protein. Figure S9: Drug target and interacting partner docking analysis. Table S1: Significantly up and down regulated genes in COAD. Table S2: Significantly up and down regulated genes in GBM. Table S3: Significantly up and down regulated genes in SCLC. Table S4: list of NACDGs in cancer datasets. Table S5: Common and unique NACDGs across the cancer datasets. Table S6: Common and unique hubs across the cancer datasets. Author Contributions: Conceptualization, S.S. and S.D.; methodology, S.D.; software, S.D. and S.F.; validation, S.F. and S.D.; formal analysis, S.F.; investigation, S.F. and S.D.; resources, S.D.; data curation, S.F.; writing—original draft preparation, S.S. and S.D.; writing—review and editing, S.S., S.D. and S.F.; visualization, S.F.; supervision, S.S. and S.D.; project administration, S.S. and S.D. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding Conflicts of Interest: The authors declare no conflict of interest.

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