Maoliang Chen, Qipeng Tan1, Yin Tao2 and Chunyun Wang*

Department of Gastrointestinal Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, 1Department of Pre-Hospital First Aid, 2Department of General Surgery, Zhuzhou Central Hospital, Zhuzhou, Hunan 412000, China

*Corresponding Author:
Chunyun Wang
Department of Gastrointestinal Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001
E-mail: 892357467@qq.com
This article was originally published in a special issue, “Advanced Targeted Therapies in Biomedical and Pharmaceutical Sciences”
Indian J Pharm Sci 2023:85(1) Spl Issue “205-213”

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.spl.631

Abstract

To explore the genes correlated with the prognosis in colorectal cancer is the main objective of the study. Identification of differential genes through series of GSE20970, GSE37182, GSE44861 and GSE64392 from gene expression Omnibus database and then the differential genes were analyzed by gene ontology and Kyoto encyclopedia of genes and genomes enrichment. Through protein-protein interaction networks, screening of key genes was done and then we analyzed the prognosis of colorectal cancer from the cancer genome Atlas database. We obtained the differential genes through limma screening in series data GSE20970, GSE37182, GSE44861 and GSE64392 from gene expression Omnibus database. Venn diagram was used to screen 206 intersecting differentially expressed genes. Gene ontology enrichment analysis terms used were cytosol, transcription, deoxyribonucleic acid-templated, nucleic acid-templated transcription, ribonucleic acid biosynthetic process, nucleobase-containing compound biosynthetic process, heterocycle biosynthetic process, aromatic compound biosynthetic process, regulation of nucleobase-containing compound metabolic process, regulation of ribonucleic acid metabolic process, organic cyclic compound biosynthetic process. Kyoto encyclopedia of genes and genomes pathway terms were interleukin-17 signaling pathway, bladder cancer, phospholipase D signaling pathway, human cytomegalovirus infection, aldosterone-regulated sodium reabsorption, Kaposi’s sarcoma-associated herpesvirus infection, forkhead box O signaling pathway, nuclear factor kappa B signaling pathway, hepatitis B. Protein-protein interaction network has shown 206 key genes. According to the top 50 of betweenness value, we analyzed the prognosis of colorectal cancer from the cancer genome Atlas database. We found Ki-ras2 Kirsten rat sarcoma viral oncogene homolog, C-X-C motif chemokine ligand 8, partner and localizer of breast cancer gene 2, peroxiredoxin 4 and translocase of outer mitochondrial membrane 20 are the key genes which impact prognosis in colorectal cancer. We predict that these genes can promote the survival time of colorectal cancer in future.

Keywords

Colorectal cancer, gene expression Omnibus database, the cancer genome Atlas database, differential genes, gene ontology, Kyoto encyclopedia of genes and genomes

Colorectal Cancer (CRC) is the most common cancer in gastrointestinal tract[1]. It is the second and the third common cancer in woman and men, respectively[2]. In global cancer statistics 2020, more than 1.9 million new CRC (including anus) cases and 935 000 deaths were estimated to occur in 2020[1]. The 5 y and 10 y survival rates are 65 % and 58 % respectively[3], and incidence and mortality rates are 25 % higher in men than in women[4]. 20 % cases show metastatic and 40 % cases recurred with localized CRC[5]. However, the prognosis of metastatic and recurrence CRC is poor, 5 y survival rate is less than 20 %[6]. The incidence rates are approximately 4-fold in transitioned countries than transitioning countries[1]. In recent years, the incidence rate of people >50 y is lower than people with <50 y[3]. Colonoscopy screening reduces and improves the incidence and prognosis of CRC[7]. The prevalence of colon cancer is closely related to living habits, smoking and drinking, and so on[8,9]. In addition, there are 10 %-20 % of patients with CRC who possess a positive family history and ~5 % of cases of CRC are linked to a known hereditary CRC syndrome detectable by germline testing[4].

The development and prognosis of CRC are closely related to genetic factors besides tumor stage[10]. More and more gene targeted drugs have been developed or they are being developed for Vascular Endothelial Growth Factor (VEGF) and Ki-ras2 Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS)[11,4]. We obtained genes related to the prognosis of CRC to improve survival rate. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database are the most commonly used tumor database in the global, which includes gene sequencing.

In this study, we used GEO database for the analysis of the Differentially Expressed Genes (DEGs) with tumor tissue and normal tissue in CRC and then validate DEGs expression and analyze the prognosis of CRC from TCGA database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to analyze DEGs. This study was performed in order to study the prognosis of key DEGs in CRC.

Materials and Methods

Data collection and workflow illustration:

We obtained messenger Ribonucleic Acid (mRNA) transcriptome data in series of GSE20970, GSE37182, GSE44861 and GSE64392 from GEO database (https://www.ncbi.nlm.nih.gov/geo/) and clinical data from TCGA database of CRC, respectively. The workflow is illustrated in fig. 1.

IJPS-workflow

Fig. 1: Data collection and workflow illustrated in the study

Limma screening of DEGs:

We obtained genes profile through clusterProfiler package in R version 4.0.2 (http://www.r-project.org/) from series of GSE20970, GSE37182, GSE44861 and GSE64392. Limma R package (version 3.40.6) was used to analyze the DEGs. The threshold for identifying significant DEGs was False Discovery Rate (FDR)<0.01 and |log2 (fold change)|≥2. Venn diagram was drawn to show the relationship between DEGs and obtained intersecting differential key genes from four series data.

Key DEGs expression and prognosis in CRC from TCGA database:

From TCGA database, we obtained 455 tumor samples and 45 normal tissues. The key DEGs expression was through clusterProfiler package in R version 4.0.2. The key DEGs of prognosis were obtained from online website, University of Alabama at Birmingham Cancer Data Analysis (UALCAN) (http://ualcan.path.uab.edu/).

GO and KEGG enrichment analysis:

GO analysis is performed and functional enrichment analysis includes Biological Process (BP), Molecular Function (MF) and Cellular Component (CC). KEGG is the advanced function and mechanism involved in the biological system at the molecular level. We analyze the key DEGs function and mechanisms pathway though Database for Annotation, Visualization and Integrated Discovery (DAVID) platform (http://david.ncifcrf.gov/), p value<0.05 and FDR<0.25 were considered statistically significant.

Protein-Protein Interaction (PPI) network and key genes:

The GeneMANIA (http://genemania.org/) prediction server was designed to assess the PPI network[12]. The network was analyzed and visualized by Cytoscape 3.6.1 (http://www.cytoscape.org/). In the network, a high degree value indicated a more essential role for that gene. The degree value of each gene was calculated by the network analyzer tool that was built in the Cytoscape software. The genes whose degree value, closeness centrality and betweenness centrality were greater than the median value were identified as key genes.

Statistical analysis:

The Student’s t-test (R function t-test) was used to determine whether there were significant differences between the two groups and p-value<0.05 was considered to be statistically significant. The ggplot package was used for plotting.

Results and Discussion

Identification of DEGs was shown here. We used limma screening of DEGs in series data GSE20970, GSE37182, GSE44861 and GSE64392 from GEO database (fig. 2). And then, we use DEGs to draw Venn diagram. There are 206 intersecting key genes (fig. 3).

IJPS-differential

Fig. 2: Volcano map of GSE20970, GSE37182, GSE44861 and GSE64392 differential genes,Eqaution

IJPS-intersection

Fig. 3: Intersection of differential genes of GSE20970, GSE37182, GSE44861 and GSE64392

GO and KEGG analyses of the DEGs were performed using DAVID software. GO and KEGG enrichment analysis include GO enrichment terms like cytosol, transcription, DNA-templated, nucleic acid- templated transcription, RNA biosynthetic process, nucleobase-containing compound biosynthetic process, heterocycle biosynthetic process, aromatic compound biosynthetic process, regulation of nucleobase-containing compound metabolic process, regulation of RNA metabolic process, organic cyclic compound biosynthetic process. KEGG pathway include Interleukin-17 (IL-17) signaling pathway, bladder cancer, phospholipase D signaling pathway, human cytomegalovirus infection, aldosterone- regulated sodium reabsorption, Kaposi’s sarcoma- associated herpesvirus infection, Forkhead Box O (FOXO) signaling pathway, Nuclear Factor kappa B (NF-κB) signaling pathway, hepatitis B (fig. 4).

IJPS-analyses

Fig. 4: GO and KEGG analyses of the DEGs were performed using DAVID

The PPI network of the 206 key genes was generated with the GeneMANIA platform and the PPI enrichment p-value=3.62-05. From the removal of marginal genes, we obtained 52 genes. The PPI network analyzed betweenness value using Cytoscape software (fig. 5).

IJPS-network

Fig. 5: The PPI network of the 206 key genes was generated with the GeneMANIA platform, PPI enrichment

Key DEGs related to prognosis of CRC was shown here. According to Betweenness value of top 50 genes (Table 1), we analyzed their relationship with the prognosis of CRC in TCGA database. Finally, we found that 5 key DEGs were significantly different from the prognosis of CRC (p<0.05) (fig. 6). They are KRAS, C-X-C Motif Chemokine Ligand 8 (CXCL8), Partner and Localizer of Breast Cancer Gene 2 (PALB2), Peroxiredoxin-4 (PRDX4), Translocase of Outer Mitochondrial Membrane 20 (TOMM20), respectively. They are significantly different from the prognosis of CRC i.e. CXCL8, p=0.007, Hazard Ratio (HR)=0.571 (95 % Confidence Interval (CI): 0.381-0.857); KRAS, p=0.029, HR=0.643 (95 % CI: 0.432-0.956); PALB2, p=0.046, HR=0.67 (95 % CI: 0.482-0.993); PRDX4, p=0.039, HR=0.657 (95 % CI: 0.442-0.979) and TOMM20, p=0.015, HR=0.615 (95 % CI: 0.415-0.911), respectively.

Genes Betweenness Closeness
CXCL8 5541.41 0.0535009
KRAS 3970.5308 0.053539347
MAPK1 2948.6423 0.052743364
LCN2 2819.2688 0.05170021
EXOSC5 2787.6616 0.052650176
HFE 2685.3203 0.050050385
DDX10 2152.4856 0.05179006
CCND1 2144.006 0.053443328
AURKA 2112.3137 0.0520979
NUP153 2017.262 0.051772065
UBE2C 1961.2054 0.05268741
BRD4 1925.4847 0.052317414
DST 1546 0.049732976
PNPLA3 1524 0.048111074
MLXIPL 1288 0.046287667
MAPK8 1214.8868 0.052706048
ARHGEF11 1179.0071 0.049932975
LPAR1 1134.8159 0.051592797
RB1 1040.2368 0.052575864
ADRM1 1030.7721 0.049932975
SNRPF 1007.0974 0.051628552
POLR2G 825.45984 0.05018525
PRDX4 804.98456 0.050783914
GTF2IRD1 780 0.044544097
CDK4 727.26843 0.052706048
SNRPE 700.8439 0.051592797
DDX31 661.7624 0.050818555
ERO1L 647.8914 0.049370445
MMP1 621.92694 0.052299052
KATNA1 588.569 0.05049136
TOMM20 526 0.049866132
GTF2I 526 0.04290239
MUT 526 0.05137931
WDFY3 526 0.047695264
ABI2 526 0.05045716
TERF1 524 0.04798712
TEAD4 524 0.047879178
SNRPD2 519.55206 0.051574938
STX6 417.98175 0.050525602
EXOSC4 410.3557 0.052116126
RPN2 359.62906 0.04864512
ASXL1 345.27673 0.051736113
TRIM5 333.8962 0.05
PALB2 310.83032 0.05141477
IL1RN 290.33334 0.051450275
TRIM24 283.84546 0.05045716
F2R 278.9583 0.051988836
BCOR 277.04736 0.051397033
ESRP2 274.84512 0.049093906
RNF39 272.8513 0.048629243

Table 1: PPI Network Analyzed Betweenness Value of Top 50 Genes

IJPS-prognosis

Fig. 6: We analysed the prognosis of CXCL8, KRAS, PALB2, PRDX4 and TOMM20 in CRC from TCGA database,Eqaution Eqaution

In our study, we analyzed DEGs through series GSE20970, GSE37182, GSE44861 and GSE64392 from GEO database. We found 206 DEGs have significant role in four series from GEO database. Subsequently, we analyzed 206 DEGs by GO enrichment and KEGG pathway, through PPI network analysis. Finally, we found KRAS, CXCL8, PALB2, PRDX4, TOMM20 genes are key DEGs in the prognosis of CRC. Then we used 206 DEGs analysis and prognosis of CRC in TCGA database. We found that 17 DEGs were significantly different in the prognosis of Liver Hepatocellular Carcinoma (LIHC).

KRAS is a proto oncogene and belongs to Rat Sarcoma Virus (RAS) family. It involve the intracellular signal transmission and relate to tumor generation, proliferation, migration, diffusion and angiogenesis[12]. Although KRAS protein is evenly expressed by most tissues but overexpressed only by a few[13]. However, it most frequently mutate in human cancers. More than 80 % of pancreatic cancers and more than 30 % of CRC, cholangiocarcinoma and lung adenocarcinomas harbor activating mutations of KRAS gene as one of the founder carcinogenic mutation in the genome[14]. Mutation of KRAS is the main pathogenic factor of CRC, about 40 %[14,15]. KRAS mutation has poor prognosis than wild-type mutation in CRC[14-16]. In addition, KRAS mutations, especially G12D are predictive of an inferior response to chemotherapy and a high risk of recurrence[17]. At present, KRAS is used as a routine test in clinic of CRC. More and more drugs research and development for KRAS mutation, such as ARS-853[18], ARS-1620[19], AMG 510[20] and MRTX859[21,22] was carried out.

IL-8, CXCL8 belongs to the neutrophil-specific CXC family of chemokines[23]. It is produced by macrophages and is primarily responsible for neutrophil chemotaxis during the inflammatory process. Recently, many researches has found that CXCL8 play a crucial role in tumor immune escape[24]. Overexpression of CXCL8 promotes the proliferation, migration and invasion of CRC cells[25]. Some studies show that CXCL8 is closely related with metastasis, poor prognosis and poor disease- free survival[26]. In addition, up-regulation of CXCL8 expression is associated with a poor prognosis and enhances tumor cell malignant behaviors in liver cancer[27].

PALB2 as a linker between Breast Cancer gene (BRCA) 1 and BRCA2 bridging protein, it encodes a protein that may function in tumor suppression[28]. In breast cancer, PALB2 has been confirmed as an important carcinogenic factors[29]. In prostate cancer, Woko?orczyk et al. found the PALB2 mutation is associated with high-grade prostate cancer. They suggest PALB2 mutations predispose specifically to aggressive prostate cancers[30].

PRDX4 belongs to the typical 2-Cys PRDXs group and involved cell protection against oxidative injury, regulation of cell proliferation, modulation of intracellular signaling and the pathogenesis of tumors[31]. The PRDX4 show overexpression in many tumors. In breast cancer, the tumor tissues have overexperssion than normal tissues. More than this, the overexpression of PRDX4 have better survival time in breast cancer which show impact and prognosis[32]. In prostate cancer, overexperssion of PRDX4 promoted tumor cell proliferation and invasion[33]. At this time, the overexperssion of PRDX4 have a poor prognosis in prostate cancer[34].

TOMM20 is a receptor that target mitochondria functional protein[35]. Overexperssion of TOMM20 is seen in various cancers such as liver cancer, CRC, breast cancer, lung cancer[36]. In CRC, TOMM20 inhibited cell proliferation, migration and invasion[37]. In Park et al.[38] research in CRC, they suggest TOMM20 as a potential therapeutic target of CRC. Yang et al.[39] found overexperssion of TOMM20 which impacted liver cancer prognosis.

Through our study, the results show KRAS, CXCL8, PALB2, PRDX4, TOMM20 are the key genes and impact prognosis in CRC. We predict that these genes of target drug can promote the survival time of CRC in future.

Conflict of interests:

The authors declared no conflict of interest.

References