Comprehensive Multi-Omics Analysis Reveals NPC2 and ITGAV Genes as Potential Prognostic Biomarkers in Gastrointestinal Cancers

doi:10.21203/rs.3.rs-2979119/v1

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Comprehensive Multi-Omics Analysis Reveals NPC2 and ITGAV Genes as Potential Prognostic Biomarkers in Gastrointestinal Cancers

https://doi.org/10.21203/rs.3.rs-2979119/v1

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Background

Gastrointestinal cancers (GICs) continue to dominate in terms of both incidence and mortality worldwide. Due to the absence of efficient and accurate prognostic biomarkers, the prognosis and treatment outcomes of many GICs are poor. Identifying biomarkers to predict individual clinical outcomes efficiently is a fundamental challenge in clinical oncology. Although several biomarkers have been continually discovered, their predictive accuracy is relatively modest, and their therapeutic use is restricted. In light of this, the discovery of reliable biomarkers for predicting prognosis and outcome in GIC is urgently required.

Methods

We evaluated the HPA dataset and identified NPC2 and ITGAV as probable poor predictive genes for these cancers. In addition, we used the GEPIA2, cBioPortal, UALCAN, LinkedOmics, STRING, Enrichr, TISDB, TIMER2.0, hTFTarget, miRTarBase, circBank, and DGIdb databases to conduct a comprehensive and systematic analysis of the NPC2 and ITGAV genes.

Result

Our results found high expression levels of NPC2 and ITGAV in most GICs. The aforementioned gene expressions were linked to several clinicopathological characteristics of GICs as well as poorer prognosis in LIHC and STAD. The most common alteration type of NPC2 was amplification, and for ITGAV was deep deletion. Significant promotor hypermethylation was also seen in NPC2 and ITGAV in PAAD and COAD, respectively. For the immunologic significance, NPC2 and ITGAV were positively correlated with the abundance of tumor-infiltrating lymphocytes and macrophages. Furthermore, various immuno-modulators showed strong correlations with the expression of these genes. There were currently ten small molecule drugs targeting ITGAV.

Conclusion

Consequently, our bioinformatics analysis showed that NPC2 and ITGAV might be used as potential biomarkers to determine the prognosis of various GICs and are also related to immune infiltration.

Bioinformatics

Gastrointestinal cancer

Prognostic factors

Bioinformatics

Multiomics

NPC2

ITGAV

With an estimated 5 million new cases and 3.5 million deaths worldwide in 2020, gastrointestinal cancers (GICs) represent over one-quarter (26%) of the global cancer incidence and over one-third (35.4%) of all cancer-related deaths (1); to such an extent that six GICs, including liver, stomach, colon, esophagus, pancreas, and rectum cancers, are in the top 10 for death rates from all tumors (1). Histopathological parameters of tumor invasion, according to the American Joint Committee on Cancer (AJCC) and Union for International Cancer Control (UICC) TNM classification system, are used to predict the prognosis of patients with GICs (2, 3). Nevertheless, several studies have shown that, even among patients with the same TNM stage (4), there may be significant variation in their clinical outcomes despite receiving the same treatment, owing to the genetic and epigenetic heterogeneity of cancer (5, 6). Given this, it is essential to comprehend the molecular mechanisms underlying cancer to develop efficient prognostic and predictive biomarkers for clinical research and practice (7). Only a small number of the cancer biomarkers discovered in recent decades are presently employed in clinical practice, indicating that there is still room to enhance biomarkers and open the door to more personalized therapy approaches (8, 9).

Nowadays, promising cancer prevention and screening particularly show that understanding pan-cancer development would provide novel opportunities for confronting diverse malignant tumors (10). As a result, identifying the hub tumor-related genes is critical for developing efficient diagnostic and prognostic biomarkers as well as therapeutic targets. Massive high-throughput data and various publicly accessible big data online public databases are advantageous for identifying tumorigenic genes and undertaking pan-cancer investigations (11–13). In that context, the focus of a body of research has been the discovery of efficacious comprehensive markers for predicting the prognosis and treatment for GI cancer to a better prognosis and fewer side effects. As shown by Fang et al. (14) SNAI1 can be used as a prognostic biomarker for determining prognosis and immune infiltration in GICs. Pan et al. (15) comprehensively analyzed LAYN expression and its relationship to prognosis in numerous cancers across several databases, concluding that LAYN may be employed as a predictive biomarker for prognosis and immune infiltration in colon and gastric cancer.

To the best of our knowledge, an in-depth analysis to explore biomarkers for the prognosis of gastrointestinal cancer patients, in light of the most up-to-date knowledge, remains largely unknown. Herein, we use bioinformatics databases to comprehensively and systematically discover the role of NPC2 (NPC Intracellular Cholesterol Transporter) and ITGAV (Integrin Subunit Alpha V) as potential unfavorable prognostic biomarkers for GICs. The result indicated that the expression levels of NPC2 and ITGAV are related to GICs prognosis and tumor—immune signature. Additionally, it was looked into whether the mentioned genes might have a role in the pathophysiology of GICs. Finally, we explored the NPC2 and ITGAV regulatory networks. In summary, these results may give a new avenue for the development of useful prognostic biomarkers for GICc patients.

Through the HPA database, we first extracted all the unfavorable prognostic genes of the COAD/READ, LIHC, PAAD, and STAD. By the intersection of these four datasets, we concluded that NPC2 (NPC intracellular cholesterol transporter) and ITGAV (integrin alpha V subunit) are common among most of these cancers. Then via several bioinformatics databases, we comprehensively and systematically explored the roles of these two genes in the most prevalent gastrointestinal cancers. Employing the GEPIA2, UALCAN, and cBioPortal databases, we analyzed the expression, methylation, and genetic alterations of NPC2 and ITGAV. Further, co-expression genes, protein-protein interaction networks, and pathway enrichment analysis of mentioned genes were scrutinized by the LinkedOmics, STRING, and Enrichr databases, respectively. Then, we explored the correlation of NPC2 and ITGAV with tumor-immune signatures in the different tumor microenvironments via TISIDB and TIMER2.0. Predicting NPC2 and ITGAV regulatory networks, including ceRNA and TF—Genes network, was another part of our investigation. Eventually, we inspected the DGIdb database for NPC2 and ITGAV interacted drugs. The schematic analysis steps are illustrated in Fig. 1.

2.1. Human Protein Atlas Database

The Human Protein Atlas (HPA) (https://www.proteinatlas.org) (16) is an online database for exploring the human proteins using omics data integration in 17 major cancer types, additionally offering diagnostic and prognostic data on the protein expression profiles of a variety of cancers, normal tissues, and cell lines based on immunohistochemistry. Here, we combed through databases of prognostic proteins in four gastrointestinal malignancies (PAAD, STAD, LIHC, and COAD/READ) to identify unfavorable prognostic proteins that were common to all of them. A Venn diagram tool (https://bioinformatics.psb.ugent.be/webtools/Venn/) was employed to display the intersection of these four datasets.

2.2. cBioPortal Database

cBioPortal (https://www.cbioportal.org) (17) is an open-source platform for visualizing and analyzing multidimensional cancer genomes and clinical data. The public cBioPortal offers over 200 cancer genome studies, including all TCGA data and many other datasets selected from the literature. Its biologist-friendly interface includes a graphical overview of gene-level data across several platforms, correlation analysis between genes or other data types, survival analysis, and per-patient data visualization. The cBioPortal was used to examine and visualize NPC2 and ITGAV genetic alteration in four GIC cohorts, including structural variation data, copy number variation (CNV), and mutation.

2.3. UALCAN Database

The UALCAN database (http://ualcan.path.uab.edu/index.html) allows genomics, bioinformatics, and integrative approach to better understand the molecular mechanism of cancer (18). The UALCAN database was utilized in this work to evaluate the methylation and protein phosphorylation levels of the NPC2 and ITGAV promoter regions in normal and cancer tissues. The significance of differences was determined using a student's t-test, in which P-value < 0.05 was considered significant.

2.4. GEPIA2 Database

GEPIA2 (http://gepia2.cancer-pku.cn/#index) is a comprehensive cancer database based on TCGA and GTEx data that contains gene expression and prognostic information for over thirty prevalent human malignancies (19, 20). In this investigation, we utilized the GEPIA2 Database to analyze the expression of NPC2 and ITGAV in digestive tract tumor tissues and their matched normal tissues and normal individuals. We exhibited the results using BodyMap, dot plot, and box plot. Following that, we utilized this Database to investigate the relationship between NPC2 and ITGAV expression and tumor pathological stage. All of the above use log2 (TPM + 1) as the log scale. Besides that, we utilized the "Survival Plots" module to investigate the link between NPC2 and ITGAV expression and four GIC prognosis.

2.5. LinkedOmics Database

LinkedOmics (http://linkedomics.org/admin.php) is an online portal with multi-omics data from 32 TCGA Cancer types (21). The LinkedOmics website allowed a flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. This study utilized LinkedOmics to examine NPC2 and ITGAV co-expression models from TCGA GI cohorts. NPC2 and ITGAV co-expression was analyzed statistically using Pearson’s correlation coefficient and finally presented in volcano plots and heat maps.

2.6. STRING Database

Protein-protein interaction (PPI) data can be analyzed using the STRING database (https://cn.string-db.org) (22). This Database was used in our study to investigate the PPI network of NPC2 and ITGAV. To achieve this, we independently selected the protein names "NPC2" and "ITGAV" and the organism "Homo sapiens" from the STRING website. Then, we adjusted the primary settings such that we have a minimum interaction level of 'low confidence [0.150]' maximum number of displayed interaction factors (‘no more than 50 interactors in the first shell’), the meaning of the network edge (‘evidence’), and active interaction sources (‘experiments’). Furthermore, we obtained experimentally determined proteins that bind NPC2 and ITGAV. Our study's false discovery rate (FDR) was less than 0.01.

2.7. Enrichr database

The Enrichr database (http://amp.pharm.mssm.edu/Enrichr/) database is a comprehensive online server for gene set enrichment analysis (23, 24). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for NPC2 and ITGAV genes was performed using the Enrichr dataset in the present investigation. GO terms consist of biological process (BP), cellular component (CC), and molecular function (MF).

2.8. TISIDB Database

TISIDB (http://cis.hku.hk/TISIDB/index.php) is a web service for assessing the interrelations between cancers and the immune system. It integrates multiple diverse data types, such as literature mining results from the PubMed Database, high throughput screening data, exome, and RNA sequencing data sets of patient cohorts with immunotherapy, genomics, transcriptomics, and clinical information of 30 cancer types from TCGA and public databases (25). We investigated the association between NPC2 and ITGAV expression and tumor immune-related components using this information. In addition, the TISIDB database was used to examine the expression levels of NPC2 and ITGAV in various immuno-subtype of selected cancers.

2.9. TIMER2.0 Database

TIMER2.0 (http://timer.cistrome.org) is an immune evaluation tool based on the expression of tumor gene signatures that incorporates 10,897 samples from TCGA for a comprehensive analysis of immune infiltrates cells information across 32 distinct cancer types (26). In this study, we examined the relationship between NPC2 and ITGAV expression and the levels of six types of immune cells, including CD8 + T cells, CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells.

2.10. hTFtarget Database

Bioinformatics prediction website hTFtarget (http://bioinfo.life.hust.edu.cn) has compiled detailed transcription factor (TF) — target regulations from massive ChIP-Seq data of human TFs (7,190 experiment samples of 659 TFs) across 569 conditions (399 cell-line types, 129 classes of tissues or cells, and 141 types of treatments) (27). Here, we selected only the TFs that target NPC2 and ITGAV in the tissues of the GI tract. Using the Venn diagram (https://bioinformatics.psb.ugent.be/webtools/Venn/), the overlapping TFs of NPC2 and ITGAV in hTFtargets were identified. The TF- Genes regulatory network was built and mapped utilizing the Cytoscape tool (version 3.9.1).

2.11. miRTarBase Database

The miRTarBase Database (http://mirtarbase.mbc.nctu.edu.tw) already contains over 360 thousand miRNA-target interactions that have been experimentally validated (28). The miRTarbase Database was utilized in this investigation to anticipate miRNA interactions with the NPC2 and ITGAV mRNAs.

2.12. CircBank Database

A large collection of human circRNA with predicted binding miRNA can be found in the circBank Database (http://www.circbank.cn/) (29). The circBank database stores circRNAs and is accessible to the public for searching, browsing, and downloading. The Database may be utilized as a tool to facilitate research into the function and regulation of circRNAs. Using this Database, we determined the potential interactions between the selected miRNAs and circRNAs.

2.13. DGIdb Database

The DGIdb (http://www.dgidb.org/) database is an online resource that compiles drug-gene interactions from various sources, including databases and text mining (30). This study utilized the DGIdb database to identify the drugs that interact with NPC2 and ITGAV. Those found in DGIdb were compared to those that have been or are being studied for the treatment of CRC in the ClinicalTrials.gov database (31).

3.1. NPC2 and ITGAV shared the maximum overlap across the GIC types

The search for prognostic cancer genes with unfavorable values was sought to utilize the HPA database. For PAAD, LIHC, STAD, and COAD/READ, the HPA database was mined to retrieve 668, 2615, 179, and 243 genes with poor prognosis, respectively (Table S1). Venn analysis was carried out to investigate the intersection between the unfavorable genes across these cancers (Table S2). The list was narrowed down to the two candidate genes, which shared the maximum overlap between the cancer types, including NPC2 and ITGAV (Fig. 2A). It was shown that NPC2 was common in COAD/READ, LIHC, and STAD, whereas ITGAV was prevalent in PAAD, LIHC, and STAD. In addition, to further verify the protein level of NPC2 and ITGAV expression, we compared the IHC results from the HPA database; and we found that the NPC2 and ITGAV proteins were expressed higher in tumor tissues than the normal ones (Fig. 2B and C).

NPC2 (NPC Intracellular Cholesterol Transporter 2) is a protein-coding gene with a lipid recognition domain that has been linked to lipoprotein metabolism and the innate immune pathway. NPC2 binds free cholesterol and modulates intracellular free cholesterol transportation and homeostasis (32). ITGAV (Integrin Subunit Alpha V) is a Protein Coding gene. This gene's product is a member of the integrin alpha chain family. Integrins are heterodimeric integral membrane proteins consisting of alpha and beta subunits that act in adhesion and signaling at the cell surface. Hydrolysis of the coding proprotein yields light and heavy chains containing the alpha V subunit. ITGAV consists of five members αvβ1, αvβ3, αvβ5, αvβ6, and αvβ8 and is a component of the receptors for fibronectin, vitronectin, and fibrinogen (33).

3.2. The mRNA Expression Landscape of NPC2 and ITGAV in GICs

We comprehensively investigated NPC2 and ITGAV mRNA expression levels using the GEPIA2 dataset. Based on the overall level of the interactive BodyMap, we discovered that the expression levels of NPC2 and ITGAV in most human cancers and comparable normal tissues varied, notably in the digestive organs (esophagus, pancreas, liver, and stomach), as well as the brain, blood, thyroid, and other organs and tissues (Fig. 3A and B). Following this, we investigated the mRNA expression levels of NPC2 and ITGAV in 33 human cancers and matched normal tissues and healthy individuals (Fig. 3C and D). Among GICs, NPC2 mRNA expression was significantly higher in LIHC, PAAD, and STAD (Fig. 3E); ITGAV mRNA expression was considerably higher than the average level in ESCA, PAAD, and STAD compared to normal tissues (Fig. 3F).

3.3. Correlations between the NPC2 and ITGAV Expression and Tumor Pathological Stage

The pathological stage of the tumor is a significant indication of the patient’s prognosis. Consequently, we revisited the GEPIA2 dataset to investigate if there was a linkage between NPC2 and ITGAV expression and the pathogenic stage of GICs. Our findings indicated that, surprisingly, NPC2 expression was substantially associated with tumor stage in LIHC (P-value = 0.00575) (Fig. 4A). ITGAV expression was strongly correlated with tumor stage in LIHC (P-value = 0.0198) and PAAD (P-value = 0.0151) (Fig. 4B). Other tumor pathological stages were not associated with these gene expression levels (P-value > 0.05) (Fig. 4). Collectively, our findings show that NPC2 and ITGAV expression levels are linked to the pathological staging of LIHC and PAAD, which may serve as a valuable guide for clinical stage assignment.

3.4. The Relationship between NPC2 and ITGAV Expression Levels and Prognosis of Tumor Patients

To explore the prognostic value of the NPC2 and ITGAV in patients with GI cancer, we assessed the impact of differentially expressed NPC2 and ITGAV on clinical outcomes, including overall survival (OS) and Disease-free survival (DFS) using GEPIA2. The Kaplan-Meier survival curves of the results from 4 types of GIC suggest that high NPC2 expression was significantly associated with worse OS in LIHC (log-rank P = 0.028) (Fig. 5A). In addition, we also found that high expression of ITGAV in LIHC (OS: log-rank P = 0.048) and STAD (OS: log-rank P = 0.0023; DFS: log-rank P = 0.024) have a worse prognosis (Fig. 5B) and there was no statistical difference between the high or low expression of NPC2 and ITGAV in other tumors’ OS and DFS.

3.5. NPC2 and ITGAV Genetic Alteration Analysis

Genetic mutations significantly influence cancer development, and these altered genes may potentially serve as useful prognostic biomarkers (34). To dissect the molecular landscape of NPC2 and ITGAV alterations in GICs, at the first step, we conducted an in-depth study using the cBioPortal Database and found that NPC2 and ITGAV were altered in 0.5% (8/1659) and 4% (67/ 1659) of patients in four publicly available datasets, respectively (Fig. 6A). Furthermore, we also analyzed the alteration frequency of the NPC2 and ITGAV genes in these types of tumors. As Figs. 6B and 6C show, amplification is the most frequent form of NPC2 gene mutation, while deep deletion is the most common form of ITGAV gene mutation. Intending to decipher the mutational landscape of NPC2 and ITGAV in digestive cancer types across protein domains, we conducted an in-depth analysis, which revealed a total of two and 28 mutation sites, respectively (Fig. 6D and E). According to the Post-translational Modification (PTM) data in Figs. 6D and 6C, Phosphorylation modification was the most common kind of PTM in NPC2 and ITGAV, with four and eleven sites, in order.

3.6. Analysis of NPC2 and ITGAV Methylation Level in GICs

One other significant factor in the development of cancer is aberrant DNA methylation (31). For this reason, we next investigated NPC2 and ITGAV methylation in GICs and corresponding tissues using the UALCAN database (Fig. 7A and B). Our results indicate that, compared with normal tissues, NPC2 methylation levels were significantly increased in PAAD tissues (Fig. 7A), and ITGAV methylation was increased considerably in COAD tissues (Fig. 7B).

3.7. Gene Co-expression, Protein-Protein Networks Construction, and Pathway Enrichment Analysis

We postulated that NPC2 and ITGAV’s functions in GICs could be intimately tied to that of its adjacent genes. In light of this, the co-expression genes were analyzed in the TCGA GI cohort using the LinkedOmics database. Genes positively or negatively correlated with NPC2 (Fig. 8A) and ITGAV (Fig. 8B) were represented by dark red dots and dark green pots in volcano plots. Supplementary Figures S1A and B also demonstrated heat maps of the positive and negative correlations between NPC2 and ITGAV with fifty significant gene sets, respectively.

Using the STRING website, we constructed a PPI network to assess the interactions for NPC2 and ITGAV better. The PPI networks include eleven nodes and nineteen edges with an average node degree of 3.45 For NPC2 (Fig. 8C), and eleven nodes and 48 edges with an average node degree of 8.73 for ITGAV (Fig. 8D), respectively. Furthermore, we compared 50 interacted proteins to the top 100 correlated genes with NPC2 and ITGAV derived from the GEPIA2 Database, which revealed no common gene for NPC2 (Fig. 8E) and ITGB1 for ITGAV (Fig. 8F).

Subsequently, GO and KEGG pathway enrichment analyses of NPC2 and ITGAV were performed by combining their 100 correlated genes and binding proteins. According to the findings, the most significant biological process, molecular function, cellular component, and KEGG pathway analyses for NPC2 were sterol transport (GO:0015918), cholesterol binding (GO:0015485), and lysosomal lumen (GO:0043202), respectively (Fig. S1C). Also, the most significant biological process, molecular function, and cellular function for ITGAV were cell-matrix adhesion (GO:0007160), C-X3-C chemokine binding (GO:0019960), and focal adhesion (GO:0005925), respectively. The most significant KEGG pathway analyses for ITGAV were the regulation of the actin cytoskeleton (Fig. S1D).

3.8. Association between NPC2 and ITGAV with Immune-related Signatures

It is well-established that tumor-infiltrating immune cells play a crucial role in tumor development and progression. In addition, immunomodulators and cytokines in the tumor-immune microenvironment (TIME) may significantly impact infiltrating immune cells’ activity (35, 36). There is also evidence that immune cells may serve as an independent predictor of survival and chemotherapy response (37, 38). Therefore, evaluating the relationship between NPC2 and ITGAV with TIME is necessary. We initially performed a GIC analysis of the NPC2 and ITGAV gene expression with infiltration of immune cells using the TISIDB database (Fig. 9A and B, respectively). NPC2 expression was the most closely related to infiltrating levels of Gamma delta T cell in COAD (r = 0.33, P = 5.30e-13) and Macrophage in PAAD (r = 0.452, P = 3.25e-10) and STAD (r = 0.392, P < 2.2e-16) and Monocyte in LIHC (r = 0.44, P < 2.2e-16) (Fig. S2-1). What’s more, the ITGAV gene expression was the most associated with the infiltration of Macrophage in COAD (r = 0.466, P < 2.2e-16), natural killer T cell in LIHC (r = 0.424, P < 2.2e-16), NK in PAAD (r = 0.543, P < 2.2e-16) and STAD (r = 0.384, P < 2.2e-16) (Fig. S2-1). We then analyzed the correlation of NPC2 and ITGAV expression with immune stimulators (Fig. 9C and D), immune inhibitors (Fig. 9E and F), and major histocompatibility complex (MHC) molecules (Fig. 9G and H) in different GICs via the TISIDB database. The Spearman correlations between NPC2 and ITGAV expression with the top four immune stimulators, immune inhibitors, and MHC for each GIC are shown in Supplementary Fig. 2–2.

We also investigated if there was a correlation between immune subtypes and target gene expression. It's important to note that the immune subtypes are divided into six distinct groups: C1 (wound healing), C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-b dominant). The results showed that NPC2 expression levels were significantly related to LIHC and STAD (Fig. 9I). The ITGAV gene expression was correlated to COAD and STAD immuno-subtype (Fig. 9J). Finally, we corroborated the immune filtration analysis using data from the TIMER2.0 database. The study's findings indicated that NPC2 and ITGAV expressions positively correlated with CD8 + T cell, CD4 + T cell, B cell, neutrophil, macrophage, and dendritic cell infiltration in nearly all of the chosen GIC tissues (Fig. 9K and L). Overall, the data showed that higher levels of immune cell infiltration were connected to overexpression of the genes mentioned above.

3.9. Gene regulatory network

Transcriptional regulation is a fundamental biological process, and considerable effort has been expended worldwide to dissect it and identify its key characters to comprehend how its molecular components work together to regulate gene expression levels (39). Gene Regulatory Networks (GRNs) are potent instruments for describing and computationally reconstructing the intricate network of interactions underlying the transcriptional regulation of gene expression. GRNs are topological maps that represent and predict molecular entity relationships. On occasion, GRNs consist of regulatory interactions between a TF and its target genes, as well as between noncoding RNAs and target genes as a ceRNA network (40). The TF and ceRNA regulatory networks as GRNs were the primary focus of our investigation.

3.9.1. NPC2 and ITGAV-Related Transcription Factors Regulatory network

TFs are vital components of signal transduction expressed in all human cells. They are generally regarded as the terminal effectors of signaling pathways in cells. As such, the deregulation of a few TFs could profoundly impact overall gene expression profiles. Recent studies have underlined the importance of TFs in cancers (41–43), and revealed the crucial functions of TFs in tumor initiation, progression, and metastasis (44). To expound on the role of TFs in the regulation of GICs, we queried the hTFtarget Database for potential TFs for NPC2 and ITGAV, and 58 and 49 TFs were identified, respectively (Table S3). Thirty-four common candidate TFs were also obtained by the intersection of ITGAV and NPC2 transcription factors based on hTFtargets through Venn Diagram (Fig. 10A and Table S4). Eventually, the TF- Genes regulatory network was visualized with Cytoscape 3.9.1 (Fig. 10B).

3.9.2. Prediction of NPC2 and ITGAV mRNA – miRNA – circRNA regulatory network

The competing endogenous RNA (ceRNA) is a novel regulatory mechanism in cancer, which is attracting widespread attention, and emerging evidence demonstrates that the ceRNA hypothesis has played a role in molecular biological mechanisms of occurrence and development of various cancer, including hepatocellular carcinoma, gastric, colorectal, and pancreatic cancer (45–48). To better understand the role of circRNAs and miRNAs in the ceRNA network of NPC2 and ITGAV mRNAs, we established a circRNA–miRNA–mRNA (ceRNA) network. As the first step, the miRTarBase tool identified a total of one and eight miRNAs targeting NPC2 and ITGAV mRNA, respectively (Table S5). Further, we retrieved circRNAs data that interact with miRNAs from the CircBank online database and identified a hundred and five pairs of interacting circRNAs and miRNAs (Table S6). Finally, the ceRNA network based on 105 circRNA nodes, nine miRNA nodes, and two mRNA (NPC2 and ITGAV) nodes were constructed and visualized using Cytoscape 3.9.1 (Fig. 10C).

3.10. Drug-gene interaction

The drug-gene interaction database (DGIdb) was used to identify the drugs interacting with the NPC2 and ITGAV. Table 1 shows the interacted drugs with ITGAV genes in addition to related clinical trials in various GICs’ cohorts. IMGN-388, CILENGITIDE, and CYPATE have the highest interaction score for ITGAV (6.47). However, drug interaction for NPC2 was not indicated by DGIdb.

Table 1

Drug–Gene Interactions Evaluated via DGIdb and ClinicalTrials.gov databases.
Target Gene
ITGAV	IMGN-388		NA	6.37		NA
	CILENGITIDE		Antagonist	6.37		NA
	CYPATE		NA	6.37		NA
	STX-100		Inhibitor	3.18		NA
	INTETUMUMAB		Antagonist	2.55		NA
	ABITUZUMAB		Inhibitor	2.55		NCT03688230, NCT01008475, and NCT00848510
	VOLOCIXIMAB		NA	2.12		NCT00401570
	ETARACIZUMAB	Antagonist	1.59		NCT00027729 and NCT00284817
	ABCIXIMAB	Inhibitor	1.06		NA
	GLPG-0187	NA	1.06		NA
NPC2	NA

Accurately predicting cancer prognosis is of significant value to patients, oncologists, and researchers. Nevertheless, it continues to be a challenge because of the heterogeneity of tumors. With the rise of high-throughput data comes the expectation that prognostic prediction may be improved by combining information from clinical and molecular biomarkers. We systematically used several available online databases, pursuing to discover promising biomarkers to predict the prognosis of GIC patients. We first comprehensively analyzed unfavorable prognostic genes among COAD/READ, LIHC, PAAD, and STAD and determined NPC2 and ITGAV as two probable prognostic markers that shared maximum overlap in four GICs. NPC2 is a protein-coding gene with a lipid recognition domain that has been demonstrated to be associated with lipoprotein metabolism and innate immune system pathways (32). High NPC2 protein expression has been detected in several cancers, such as breast, colon, and thyroid cancer (49, 50). Emerging pieces of evidence indicate that the ITGAV gene leads to the development and progression of various malignancies, such as colon carcinoma, pancreatic adenocarcinoma, esophageal adenocarcinoma, gastric cancer, hepatocellular carcinoma (51–55). Multiple mechanisms have been discovered so far to explain how ITAGV promotes tumor growth, such as epithelial-mesenchymal transition (EMT), proliferation, migration, and chemoresistance (53, 56). However, it was not fully understood how these genes were altered or what role they played in the GICs. Therefore, we used bioinformatics tools to delve further into the functions of these genes in GICs. We conduct an in-depth investigation of how NPC2 and ITGAV are expressed in human cancers and adjacent normal tissues. Furthermore, we show that NPC2 expression correlates with LIHC pathological staging, and ITGAV expression correlates with LIHC and PAAD pathological staging. In addition, we discovered that in PAAD and COAD, methylation levels of NPC2 and ITGAV are significantly elevated. These results were supplemented by our identification of linkages between NPC2 and ITGAV and the immune signature in the TIME.

In this study, our data suggest that the mRNA expression level of NPC2 was higher in several tumors, particularly in LIHC, PAAD, and STAD. Besides, ITGAV expression was higher in ESCA, PAAD, and STAD. Next, we, on the one hand, found that, in GICs, NPC2 expression levels were significantly associated with LIHC prognosis (lower OS) and pathological stage. On the other hand, ITGAV was correlated with LIHC and PAAD pathological stage significantly but not with other GIC stages. Moreover, we provide evidence that ITGAV expression levels were associated with worse OS of LIHC and OS, PFS of STAD patients. These results suggest that NPC2 and ITGAV may be useful as novel pathological staging markers for LIHC, PAAD, and STAD; nevertheless, it would be noteworthy to elucidate further the mechanism by which NPC2 and ITGAV are only linked with LIHC, PAAD, and STAD prognosis or pathological staging.

Cancer refers to a group of illnesses characterized by the unrestrained proliferation of aberrant cells, which is supposed to be predominantly the result of genetic mutations. Some of these mutations are dubbed "drivers" because of their tendency to spur carcinogenesis and provide certain selective advantages for precancerous cells over their neighboring cells. Moreover, it has become apparent that cancer genomes also include several "passenger" mutations. Mutational patterns in driver and passenger genes in cancer genomes shed light on the varied cancer phenotypes (57). It is interesting to note that our data suggests a mutation rate of 0.5% for NPC2 and 4% for ITGAV in GICs.

Currently, DNA methylation analysis is an emerging method for improving the accuracy of pathological diagnosis and prognosis of several malignancies (58, 59). We provide preliminary evidence that, according to the UALCAN database, NPC2 and ITGAV DNA methylation levels are reduced in PAAD and COAD, respectively. Based on the UALCAN database, we report preliminary information that promotor methylation levels for NPC2 and ITGAV are increased in PAAD and COAD, respectively. It has been shown that suppressing tumor suppressor genes by hypermethylation of DNA regions in the promoter region may modulate the pathophysiology of GICs. Cancer-related genes frequently regulate the DNA repair, cell cycle, apoptosis, and tumor-specific signaling pathways; However, promoter hypermethylation can cause genomic instability (60). A growing body of research demonstrates a significant relationship between NPC2 and ITGAV genetic aberrations and carcinogenesis. According to earlier research, abnormal NPC2 expression is significantly linked to a less favorable prognosis for glioblastoma (61). Using a panel of monoclonal antibodies against NPC2, Liao et al. (50) identified NPC2 overexpression in breast, colon, and lung cancers. In addition, Taguchi et al. (62) confirmed that human and mouse lung adenocarcinoma cells abundantly secreted NPC2. It has been demonstrated that ITGAV expression increases in a variety of epithelial tumors despite being practically undetectable in normal tissue, which highlights the possible role of integrin expression during cancer development (52). Integrins participate in cell surface adhesion and signaling and conclusively play crucial roles in cancer development. For illustration, ITGAV overexpression stimulated the synergistic effect of integrin and selectin, which promoted adhesion between pancreas cancer cells and peritoneal mesothelial cells, ultimately resulting in the development of PAAD (51). The association between ITGAV expression and poor prognosis in individuals with LIHC was uncovered by Kang et al. (63). As reported by Kemper et al., high levels of ITGAV expression have been found as a risk factor for PAAD. (51). One point that should be noted is that there are no reports about the association between cancers and methylation of these genes, indicating the novelty of our study. Overall, the fact that NPC2 and ITGAV expression makes it possible to identify numerous cancerous tissues of the GI tract from their noncancerous counterparts highlights the possibility of the aforementioned gene expressions in screening GICs.

Moreover, we carried out a related-TF prediction for NPC2 and ITGAV and Identified thirty-four common TFs between these two genes. Most of these TFs have significant roles in cancer development. For instance, the transcription factor Yin Yang 1 (YY1) participates in various biological functions, such as cell proliferation (64), invasion, migration (65), and EMT (66). Therefore, YY1 is critical for tumor progression, and increasing evidence suggests a close association between YY1 and cancer. Recently, Sato et al. (67) declared that YY1 suppressed the expression of ITGAV, and this transcriptional regulation may lead to the suppression of CRC cell migration and invasion.

We explored the PPI network of NPC2 and ITGAV and found 20 genes associated with NPC2 and ITGAV (10 genes for each). These NPC2 or ITGAV-related genes all play a role in various cancers. For example, Chen et al. (68) corroborated that low expression of NPC1L1 and NPC2 in HCC tissues might prompt poor clinical outcomes in HCC patients after resection. NPC1L1 and NPC2 combination was an independent prognostic factor for OS and time to recurrence (TTR) in HCC patients (68). Furthermore, there is clinical evidence that ITGB1 expression has been significantly associated with poor prognosis in CRC patients (69). In conclusion, our discovery of NPC2 and ITGAV-related genes provides new insights for the diagnosis and treatment of GICs.

Tumor development is profoundly affected by TIME, which consists of tumor cells and infiltrating immune cells. Immune cells were identified as the key component in controlling cancer development among the many TME factors (70). Therefore, we looked into the relationship between NPC2 and ITGAV and immune cell infiltration in GICs and discovered a positive correlation between these genes and the infiltration of several immune cells, including CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells. These data suggest that NPC2 and ITGAV may play a significant role in the infiltration of immune cells and, ultimately, the TIME of the GICs. An impressive finding of this study was that the expression of NPC2 and ITGAV were significantly correlated with the abundance of macrophage infiltration in all kinds of aforementioned GICs. According to reports, macrophages upregulate PD-L1 expression via tumor growth factor (TGF-β)—induced EMT, which plays a crucial role in tumor immunosuppression and immune evasion (71). Taking into consideration that the expression of NPC2 and ITGAV causes an increase in macrophage infiltration, this finding may provide more evidence for the association between NPC2 and ITGAV expression and a dismal prognosis for GIC patients. In light of the above results, we next evaluated the association between NPC2 and ITGAV with immunomodulators and immunosubtypes. The expression of NPC2 and ITGAV was significantly related to the majority of immune modulators, especially CD274 (PD-L1), CD96, CSF1R, HAVCR2, and PDCDCDLG2. With the discovery of tumor-related immune inhibitors, immune checkpoint inhibitors (ICIs) have been extensively used in immunotherapy with remarkable results. Meanwhile, PD-1/PD-L1 inhibitors have been authorized as a treatment for various cancers, including COAD, LIHC, PAAD, and STAD (72–75). Further, we determined that ITGAV expression was linked to COAD and STAD immune subtypes, whereas NPC2 expression was linked to LIHC and STAD immuno-subtypes. Therefore, it is plausible to hypothesize that NPC2 and ITGAV expression may govern the infiltration level of tumor immune cells and immunological responses, and accordingly alter the prognosis of tumor patients.

With the use of the DGIdb database, ten candidate drugs targeting ITGAV were identified. Among them, for Abituzumab (NCT03688230, NCT01008475, and NCT00848510), Volociximab (NCT00401570), and Etaracizumab (NCT00027729 and NCT00284817) clinical trials on GICs’ patients have been conducted or are being conducted. In one of these clinical trials, exploratory analyses indicated that abituzumab-based therapy might be effective in patients with mCRC with high ITGAV (particularly αvβ6 member) expression who have otherwise poor prognosis (76). There are a number of limitations in our study that need further examination. Before anything else, it's important to note that the samples and clinical records in online databases were gathered retrospectively, and as a result, they skewed toward instances with fresh-frozen specimens of good quality and big tumor sizes in late-stage patients. Second, there may be uneven prognostic power and overfitting due to the small sample size of some kinds of cancer. Finally, NPC2 and ITGAV's molecular pathways in cancer are complex and need to be validated in fully independent research before being implemented in clinical practice.

Taken as a whole, our investigation into NPC2 and ITGAV in GICs revealed detailed information on gene expression, genetic alteration, clinical prognosis, and immune signature. These results will offer a solid basis for future molecular experiments of NPC2 and ITGAV in tumorigenesis and a rationale for developing innovative prognostic and therapeutic approaches for GICs patients.

BP Biological Process

CC Cellular Component

Ce-RNA Competing endogenous RNA

CNV Copy number variation

COAD Colon adenocarcinoma

DFS Disease-free survival

EMT Epithelial-mesenchymal transition

ESCA Esophageal carcinoma

GI Gastrointestinal

GICs Gastrointestinal cancers

GO Gene Ontology

GRN Gene Regulatory Networks

HPA Human Protein Atlas

ICIs Immune checkpoint inhibitors

IHC Immunohistochemistry

ITGAV Integrin Subunit Alpha V

KEGG Kyoto Encyclopedia of Genes and Genomes

LIHC Liver hepatocellular carcinoma

MF Molecular Function

NK Natural killer cells

NPC2 NPC Intracellular Cholesterol Transporter

OS Overall survival

PAAD Pancreatic adenocarcinoma

PPI Protein-protein interaction

PTM Post Translational Modification

READ Rectum adenocarcinoma

STAD Stomach adenocarcinoma

TF Transcription Factor

TGF Tumor growth factor

TIME Tumor immune microenvironment

TTR Time to Recurrence

YY1 Yin Yang 1

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

CONFLICTS OF INTEREST

The authors declare that they have no conﬂicts of interest.

AUTHOR CONTRIBUTIONS

Conceptualization: ZS and MZ. Methodology: ZS, MZ, and MP. Investigation: MP, PJ, MZ, and ZS. Visualization: MP, PJ, and MZ. Manuscript draft: MP. Manuscript review and editing: ZS, MP. Supervision: ZS. All authors contributed to the article and approved the submitted version.

FUND

This research did not make use of any external fund.

ACKNOWLEDGMENTS

We thank everyone who provided support for this study.

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Comprehensive Multi-Omics Analysis Reveals NPC2 and ITGAV Genes as Potential Prognostic Biomarkers in Gastrointestinal Cancers

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Abstract

Background

Methods

Result

Conclusion

Figures

1. Introduction

2. Materials and Methods

2.1. Human Protein Atlas Database

2.2. cBioPortal Database

2.3. UALCAN Database

2.4. GEPIA2 Database

2.5. LinkedOmics Database

2.6. STRING Database

2.7. Enrichr database

2.8. TISIDB Database

2.9. TIMER2.0 Database

2.10. hTFtarget Database

2.11. miRTarBase Database

2.12. CircBank Database

2.13. DGIdb Database

3. Results

3.1. NPC2 and ITGAV shared the maximum overlap across the GIC types

3.2. The mRNA Expression Landscape of NPC2 and ITGAV in GICs

3.3. Correlations between the NPC2 and ITGAV Expression and Tumor Pathological Stage

3.4. The Relationship between NPC2 and ITGAV Expression Levels and Prognosis of Tumor Patients

3.5. NPC2 and ITGAV Genetic Alteration Analysis

3.6. Analysis of NPC2 and ITGAV Methylation Level in GICs

3.7. Gene Co-expression, Protein-Protein Networks Construction, and Pathway Enrichment Analysis

3.8. Association between NPC2 and ITGAV with Immune-related Signatures

3.9. Gene regulatory network

3.9.1. NPC2 and ITGAV-Related Transcription Factors Regulatory network

3.9.2. Prediction of NPC2 and ITGAV mRNA – miRNA – circRNA regulatory network

3.10. Drug-gene interaction

4. Discussion

Abbreviations

Declarations

References

Supplementary Files

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