Identification of key pathways and genes in colorectal cancer to predict the prognosis based on mRNA interaction network

Bioinformatics analysis-based screening of circRNA gene with mainstream expression trend in colorectal cancer and construction of a coexpression regulatory network

OBJECTIVE

Since circRNA can be utilized as a potential diagnostic marker for cancer, to explore the regulatory mechanism of colorectal cancer (CRC) using bioinformatics, the public database of circRNA was mined.

METHODS

CRC differentially expressed miRNAs were screened in the Cancer Genome Atlas (TCGA) database, CRC differentially expressed circRNAs were searched in the Gene Expression Omnibus (GEO) database, the two databases were combined to identify CRC differentially expressed mRNAs, and a circRNA-miRNA‒mRNA regulatory network was constructed by combining a plurality of target prediction databases to identify key genes. The upstream circRNA and regulatory axis of the key genes were identified for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis to explore the biological functions of circRNA in CRC using the regulatory axis.

RESULTS

After the screening of the GSE21815 dataset, a total of 22 differentially expressed circRNAs were obtained, with 12 upregulated and 10 downregulated genes. Similarly, the GSE126094 dataset yielded 104 differentially expressed circRNAs, comprising 56 upregulated and 48 downregulated genes. Among the differentially expressed circRNAs, five were identified, with VDAC3 and SETD2 showing downregulated expression, while RAD23B, RPPH1, and MYBL2 exhibited upregulated expression. Following the selection process, five DEcircRNAs, eight target miRNAs, and 105 target DEmRNAs were identified. The protein-protein interaction (PPI) network revealed close relationships among the mRNAs, with E2F2, E2F3, CCND1, TNRC6A, and KAT2B identified as key genes. Notably, CCND1 emerged as a critical gene in the PPI network. Through the upregulation of has-circ-0087862, which binds to miR-892b, the translation inhibition of CCND1 by miR-892b was attenuated, leading to enhanced CCND1 expression. Functional enrichment analysis indicated that CCND1 was involved in protein binding and positive regulation of cellular processes, among other functions.

CONCLUSION

The differentially expressed genes (DEGs) in CRC markedly affected the survival time of patients. CircRNAs could be utilized as diagnostic markers of CRC, and the key genes in CRC could be screened out by bioinformatics, which would be helpful to understand the drug targets for the treatment of human immunodeficiency virus (HIV)-related CRC patients.

Differential gene expression of immunity and inflammation genes in colorectal cancer using targeted RNA sequencing

Introduction

Colorectal cancer (CRC) is a heterogeneous disease caused by molecular changes, as driver mutations, gene methylations, etc., and influenced by tumor microenvironment (TME) pervaded with immune cells with both pro- and anti-tumor effects. The studying of interactions between the immune system (IS) and the TME is important for developing effective immunotherapeutic strategies for CRC. In our study, we focused on the analysis of expression profiles of inflammatory and immune-relevant genes to identify aberrant signaling pathways included in carcinogenesis, metastatic potential of tumors, and association of Kirsten rat sarcoma virus (KRAS) gene mutation.

Methods

A total of 91 patients were enrolled in the study. Using NGS, differential gene expression analysis of 11 tumor samples and 11 matching non-tumor controls was carried out by applying a targeted RNA panel for inflammation and immunity genes containing 475 target genes. The obtained data were evaluated by the CLC Genomics Workbench and R library. The significantly differentially expressed genes (DEGs) were analyzed in Reactome GSA software, and some selected DEGs were used for real-time PCR validation.

Results

After prioritization, the most significant differences in gene expression were shown by the genes TNFRSF4, IRF7, IL6R, NR3CI, EIF2AK2, MIF, CCL5, TNFSF10, CCL20, CXCL11, RIPK2, and BLNK. Validation analyses on 91 samples showed a correlation between RNA-seq data and qPCR for TNFSF10, RIPK2, and BLNK gene expression. The top differently regulated signaling pathways between the studied groups (cancer vs. control, metastatic vs. primary CRC and KRAS positive and negative CRC) belong to immune system, signal transduction, disease, gene expression, DNA repair, and programmed cell death.

Conclusion

Analyzed data suggest the changes at more levels of CRC carcinogenesis, including surface receptors of epithelial or immune cells, its signal transduction pathways, programmed cell death modifications, alterations in DNA repair machinery, and cell cycle control leading to uncontrolled proliferation. This study indicates only basic molecular pathways that enabled the formation of metastatic cancer stem cells and may contribute to clarifying the function of the IS in the TME of CRC. A precise identification of signaling pathways responsible for CRC may help in the selection of personalized pharmacological treatment.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Metabolic modeling of host-microbe interactions for therapeutics in colorectal cancer

The onset of colorectal cancer (CRC) is often attributed to gut bacterial dysbiosis, and thus gut microbiota are highly relevant in devising treatment strategies. Certain gut microbes, like Enterococcus spp., exhibit remarkable anti-neoplastic and probiotic properties, which can aid in silver nanoparticle (AgNPs) induced reactive oxygen species (ROS)-based CRC treatment. However, the effects of AgNPs on gut microbial metabolism have not been reported thus far. In this study, a detailed systems-level understanding of ROS metabolism in Enterococcus durans (E. durans), a representative gut microbe, was gained using constraint-based modeling, wherein, the critical association between ROS and folate metabolism was established. Experimental studies involving low AgNP concentration treatment of E. durans cultures confirmed these modeling predictions (an increased extracellular folate concentration by 52%, at the 9^th h of microbial growth, was observed). Besides, the computational studies established various metabolic pathways involving amino acids, energy metabolites, nucleotides, and SCFAs as the key players in elevating folate levels on ROS exposure. The anti-cancer potential of E. durans was also studied through MTT analysis of HCT 116 cells treated with microbial culture (AgNP treated) supernatant. A decrease in cell viability by 19% implicated the role of microbial metabolites (primarily folate) in causing cell death. The genome-scale modeling approach was then extended to extensively model CRC metabolism, as well as CRC-E. durans interactions in the context of CRC treatment, using tissue-specific metabolic models of CRC and healthy colon. These findings on further validation can facilitate the development of robust and effective cancer therapy.

Pan-drug and drug-specific mechanisms of 5-FU, irinotecan (CPT-11), oxaliplatin, and cisplatin identified by comparison of transcriptomic and cytokine responses of colorectal cancer cells

Colorectal cancer (CRC) caused over 900,000 deaths worldwide in 2020. A majority of late-stage CRC patients are treated with 5-fluorouracil (5-FU) combined with either irinotecan (CPT-11), oxaliplatin, or both. Despite their widespread use, the mechanisms of efficacy and toxicity of these drugs remain incompletely understood. While previous work has investigated cellular responses to these agents individually, we directly compare the transcriptomic and cytokine profiles of HCT116 wild-type and p53-/- colorectal cancer cells treated with these drugs and report pan-drug, drug-specific, drug class-specific, p53-independent, and p53-dependent signatures. We observed downregulation of histone genes by 5-FU (that significantly correlates with improved survival in CRC patients) and upregulation of FOS and ATF3 by oxaliplatin (which may contribute to peripheral neuropathy). BTG2 was identified as a top gene upregulated by all four drugs, suggesting its critical role in the cellular response to chemotherapy in CRC. Soluble TRAILR2 (death receptor 5; DR5) is a decoy receptor for TRAIL, an apoptosis-inducing cytokine. TRAILR2 was down-regulated by oxaliplatin and 5-FU, was not affected by CPT-11, and was increased by cisplatin. There was an increase in IL-8 by oxaliplatin and increase in ferritin by cisplatin which may contribute to cancer cell survival. Novel drug-specific mechanisms of efficacy or toxicity identified in these signatures may be targeted with combination therapies or development of new targeted therapies. Together, the findings here contribute to our understanding of the molecular bases of efficacy and toxicity of chemotherapeutic agents often used for treatment of GI cancer such as CRC.

RNA-sequencing identification and validation of genes differentially expressed in high-risk adenoma, advanced colorectal cancer, and normal controls

Distinct gene expression patterns that occur during the adenoma-carcinoma sequence need to be determined to analyze the underlying mechanism in each step of colorectal cancer progression. Elucidation of biomarkers for colorectal polyps that harbor malignancy potential is important for prevention of colorectal cancer. Here, we use RNA sequencing to determine gene expression profile in patients with high-risk adenoma treated with endoscopic submucosal dissection by comparing with gene expression in patients with advanced colorectal cancer and normal controls. We collected 70 samples, which consisted of 27 colorectal polyps, 24 cancer tissues, and 19 normal colorectal mucosa. RNA sequencing was performed on an Illumina platform to select differentially expressed genes (DEGs) between colorectal polyps and cancer, polyps and controls, and cancer and normal controls. The Kyoto Gene and Genome Encyclopedia (KEGG) and gene ontology (GO) analysis, gene-concept network, GSEA, and a decision tree were used to evaluate the DEGs. We selected the most highly expressed genes in high-risk polyps and validated their expression using real-time PCR and immunohistochemistry. Compared to patients with colorectal cancer, 82 upregulated and 24 downregulated genes were detected in high-risk adenoma. In comparison with normal controls, 33 upregulated and 79 downregulated genes were found in high-risk adenoma. In total, six genes were retrieved as the highest and second highest expressed in advanced polyps and cancers among the three groups. Among the six genes, ANAX3 and CD44 expression in real-time PCR for validation was in good accordance with RNA sequencing. We identified differential expression of mRNAs among high-risk adenoma, advanced colorectal cancer, and normal controls, including that of CD44 and ANXA3, suggesting that this cluster of genes as a marker of high-risk colorectal adenoma.

Downregulation of miR-199a-3p in Hepatocellular Carcinoma and Its Relevant Molecular Mechanism via GEO, TCGA Database and In Silico Analyses

Existing reports have demonstrated that miR-199a-3p plays a role as a tumor suppressor in a variety of human cancers. This study aims to further validate the expression of miR-199a-3p in HCC and to explore its underlying mechanisms by using multiple data sets. Chip data or sequencing data and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were integrated to assess the expression of miR-199a-3p in HCC. The potential targets and transcription factor regulatory network of miR-199a-3p in HCC were determined and possible biological mechanism of miR-199a-3p was analyzed with bioinformatics methods. In the results, miR-199a-3p expression was significantly lower in HCC tissues compared to normal tissues according to chip data or sequencing data and qRT-PCR. Moreover, 455 targets of miR-199a-3p were confirmed, and these genes were involved in the PI3K-Akt signaling pathway, pathways in cancer, and focal adhesions. LAMA4 was considered a key target of miR-199a-3p. In CMTCN, 11 co-regulatory pairs, 3 TF-FFLs, and 2 composite-FFLs were constructed. In conclusion, miR-199a-3p was down regulated in HCC and LAMA4 may be a potential target of miR-199a-3p in HCC.

Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer

Background

Colorectal cancer (CRC) is the second most common cause of cancer death in the United States and the third most common cancer globally. The incidence of CRC tends to be younger, and we urgently need a reliable prognostic assessment strategy.

Methods

Protein expression profile and clinical information of 390 CRC patients/samples were downloaded from the TCPA and TCGA database, respectively. The Kaplan-Meier, Cox regression, and Pearson correlation analysis were applied in this study.

Results

Based on the TCPA and TCGA database, we screened 6 hub proteins and first constructed protein risk signature, all of which were significantly associated with CRC patients' overall survival (OS). The risk score was an independent prognostic factor and significantly related with the size of the tumor in situ (T). 6 hub proteins were differentially expressed in cancer and normal tissues and in different CRC stages, which were validated at the ONCOMINE database. Next, 40 coexpressed proteins of 6 hub proteins were extracted from the TCPA database. In the protein-protein interaction (PPI) network, HER1, HER2, and CTNNB1 were at the center. Function enrichment analysis illustrated that 46 proteins were mainly involved in the EGFR (HER1) tyrosine kinase inhibitor resistance pathway.

Conclusion

Studies indicated that 6 hub proteins might be considered as new targets for CRC therapies, and the protein risk signature can be used to predict the OS of CRC patients.

Panomicon: A web-based environment for interactive, visual analysis of multi-omics data

Multi-omics analyses, combining transcriptomics, genomics, proteomics, and so on, have led to important insights in many areas of biology and medicine. To support these analyses, software that can handle the difficulties associated with multi-omics datasets is crucial. Here, we describe Panomicon, a web-based, interactive analysis environment for multi-omics data. Building on Toxygates, a tool previously created to study single-omics data that features interactive clustering, heatmaps, and user data uploads, Panomicon introduces improvements for the storage and handling of additional omics types, as well as tools for the generation and visualization of interaction networks between different types of omics data. Panomicon is a new type of environment for the collaborative study of multi-omics data, both for users uploading data to our server and for groups wishing to host their own deployment of Panomicon. We demonstrate Panomicon's capabilities by revisiting a microRNA-mRNA interaction networks study in a non-small cell lung cancer dataset.