Employing bioinformatics analysis to identify hub genes and microRNAs involved in colorectal cancer

Transcriptomic signatures of prostate cancer progression: a comprehensive RNA-seq study

Transcriptomics has been entirely transformed by RNA-sequencing (RNA-seq) due to its high sensitivity, accuracy, and precision. This study analyzed RNA-seq data to identify potential biomarkers for prostate cancer (PCa), a serious health issue among aging men. Despite several existing studies, biomarkers that effectively detect PCa or its prognosis have yet to be entirely determined. The differentially expressed genes (DEGs) that are critical and clinically informative were identified in PCa patient samples that had been progression stage categorized into medium risk (MR) and high risk (HR). A total of 174 DEGs were found to be shared between MR and HR samples. Functional enrichment analysis revealed their involvement in crucial biological processes, such as p53 signaling, mitotic nuclear division, and inflammation. To further examine their interactions, a Protein-Protein Interaction (PPI) network was constructed, where key genes, such as KIF20A, TPX2, BUB1, BIRC5, BUB1B, and MKI67, were found in significant modules, hubs, and motifs. Several transcription factors, including STAT5B, MYC, and SOX5 controlled these genes. Heatmap analysis indicates that the expression of the six crucial genes (KIF20A, TPX2, BUB1, BIRC5, BUB1B, and MKI67) increases with progression from benign state to medium-risk and high-risk states. Additionally, a nomogram model was constructed to predict the prognostic value of these biomarkers. Among the studied genes, BIRC5, MKI67, and KIF20A are suggested as potential prognostic biomarkers, while NIFK and PPP1CC are suggested as new therapeutic targets. These findings indicate that these biomarkers show considerable promise in improving early detection and prognosis of PCa.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04297-3.

Comprehensive bioinformatics analysis was used to identify and verify differentially expressed genes in targeted therapy of colon cancer

Colorectal cancer (CRC) is one of the most common malignant tumors. CLCA1 and ZG16 are lowly expressed in CRC, and we wanted to investigate whether they could be prognostic biomarkers for the malignant progression of CRC. 12,195 DEGs and 12,071 DEGs were identified through the GSE39582 dataset and TCGA dataset, and then 50 coexisting genes were selected for further analysis using Venn diagrams. These 50 DEGs were then subjected to GO and KEGG functional enrichment analyses, along with genome-wide GSEA. the first 5 core genes were identified and visualized using Cytoscape through the PPI network. Then the expression of ZG16 and CLCA1 in normal and tumor tissues were analyzed using GSE39582 and TCGA datasets, and correlation analysis, and survival analysis were performed. The expression of ZG16 and CLCA1 in CRC cells was verified by qRT-PCR, and cell proliferation, migration, and invasion abilities were detected by CCK-8, scratch assay, clone formation assay, and Transwell assay. The expression levels of ZG16 and CLCA1 were significantly lower in CRC tissues than in normal tissues. Survival analysis showed that low expression of ZG16 and CLCA1 was associated with poor survival outcomes. Multifactorial analysis showed that low expression of ZG16 and CLCA1 was an independent risk factor affecting tumor prognosis. Cellular experiments showed that cell proliferation, migration, and invasion were inhibited after overexpression of ZG16 and CLCA1. Correlation analysis showed that ZG16 and CLCA1 expression levels were positively correlated and the correlation was statistically significant. GSEA enrichment analysis based on CLCA1-related genes and ZG16-related genes (FDR < 0.25, P < 0.05) revealed that the related genes of both genes were closely related to the GnRH signaling pathway. CLCA1 and ZG16, which are lowly expressed in CRC tissues, are associated with poor prognosis of CRC and may be one of the markers for diagnostic screening and prediction of prognostic outcome in CRC. Meanwhile, CLCA1 and ZG16 may also be new targets for tumor immunotherapy.

The impact of disease and species differences on the intestinal CLCA4 gene expression

The human chloride channel regulator, calcium-activated (CLCA) 4 is discussed as a driver of epithelial-to-mesenchymal transition as well as a biomarker for colorectal cancer (CRC) and ulcerative colitis. In contrast to humans, the Clca4 gene is duplicated in the mouse, a common model species to study gene functions. However, the relevance of the functional murine Clca4 variants in healthy and diseased intestine is largely unknown. Here, we characterized the spatiotemporal expression patterns of the murine Clca4a and Clca4b genes in the healthy intestinal tract as well as in dextran sulfate sodium (DSS)-induced colitis and colitis-associated colon cancer (CAC) mouse model using RT-qPCR and in situ-hybridization. Similarly, we analyzed expression of the human CLCA4 in healthy, inflamed and cancerous intestinal tracts at single cell level. Murine Clca4a and -4b but not the human CLCA4 were detected in small intestine enterocytes of the respective species. Conversely, healthy colonocytes expressed the human CLCA4 and its murine ortholog Clca4a but not the murine Clca4b. Under inflammatory conditions, de novo expression of Clca4b was observed with both murine homologs abundantly expressed in enterocytes adjacent to ulcerations. Neoplastic colonocytes expressed none or only minimal amounts of the CLCA4 homologs both in humans and mice, whereas adjacent non-neoplastic colonocytes strongly up-regulated the human or both murine homologs, respectively. Our results suggest marked species- and homolog-specific differences in the expression patterns of the three CLCA4 homologs. Moreover, all three seem to play a role in reactive, non-neoplastic colonocytes adjacent to ulcerated and neoplastic lesions. KEY MESSAGES: Human CLCA4 and murine Clca4a, but not Clca4b, are expressed in healthy colonocytes. Inflammation leads to a de novo expression of the murine Clca4b in colonocytes. Human and murine CLCA4 homologs are absent from neoplastic enterocytes. Human and murine CLCA4s are highly expressed in tumor-adjacent, reactive colonocytes.

The diagnostic and prognostic value of C1orf174 in colorectal cancer

Introduction

Colorectal cancer (CRC) is among the lethal cancers, indicating the need for the identification of novel biomarkers for the detection of patients in earlier stages. RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients.

Methods

The genome-wide RNA sequencing of 631 samples, comprising 398 patients and 233 normal cases was extracted from the Cancer Genome Atlas (TCGA). The DEGs were identified using DESeq package in R. Survival analysis was evaluated using Kaplan-Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. The biological pathways, protein-protein interaction (PPI), the co-expression of DEGs, and the correlation between DEGs and clinical data were evaluated. Additionally, the diagnostic markers were assessed with a combioROC package. Finally, the candidate tope score gene was validated by Real-time PCR in CRC patients.

Results

The survival analysis revealed five novel prognostic genes, including KCNK13, C1orf174, CLEC18A, SRRM5, and GPR89A. Thirty-nine upregulated, 40 downregulated genes, and 20 miRNAs were detected by SVM with high accuracy and AUC. The upregulation of KRT20 and FAM118A genes and the downregulation of LRAT and PROZ genes had the highest coefficient in the advanced stage. Furthermore, our findings showed that three miRNAs (mir-19b-1, mir-326, and mir-330) upregulated in the advanced stage. C1orf174, as a novel gene, was validated using RT-PCR in CRC patients. The combineROC curve analysis indicated that the combination of C1orf174-AKAP4-DIRC1-SKIL-Scan29A4 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.90, 0.94, and 0.92, respectively.

Conclusion

Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of C1orf174 in colorectal cancer.

Microarray analysis points to LMNB1 and JUN as potential target genes for predicting metastasis promotion by etoposide in colorectal cancer

Etoposide is a second-line chemotherapy agent widely used for metastatic colorectal cancer. However, we discovered that etoposide treatment induced greater motility potential in four colorectal cancer cell lines. Therefore, we used microarrays to test the mRNA of these cancer cell lines to investigate the mechanisms of etoposide promoting colorectal cancer metastasis. Differentially expressed genes (DEGs) were identified by comparing the gene expression profiles in samples from etoposide-treated cells and untreated cells in all four colorectal cancer cell lines. Next, these genes went through the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis. Among the top 10 genes including the upregulated and downregulated, eight genes had close interaction according to the STRING database: FAS, HMMR, JUN, LMNB1, MLL3, PLK2, STAG1 and TBL1X. After etoposide treatment, the cell cycle, metabolism-related and senescence signaling pathways in the colorectal cancer cell lines were significantly downregulated, whereas necroptosis and oncogene pathways were significantly upregulated. We suggest that the differentially expressed genes LMNB1 and JUN are potential targets for predicting colorectal cancer metastasis. These results provide clinical guidance in chemotherapy, and offer direction for further research in the mechanism of colorectal cancer metastasis.

Identification of microRNA-mRNA Regulatory Networks with Therapeutic Values in Alzheimer's Disease by Bioinformatics Analysis

Background

Alzheimer's disease (AD) is the most prevalent neurological disorder worldwide, affecting approximately 24 million individuals. Despite more than a century of research on AD, its pathophysiology is still not fully understood.

Objective

Recently, genetic studies of AD have focused on analyzing the general expression profile by employing high-throughput genomic techniques such as microarrays. Current research has leveraged bioinformatics advancements in genetic science to build upon previous efforts.

Methods

Data from the GSE118553 dataset used in this investigation, and the analyses carried out using programs such as Limma and BioBase. Differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRs) associated with AD identified in the studied areas of the brain. Target genes of the DEmiRs identified using the MultiMiR package. Gene ontology (GO) completed using the Enrichr website, and the protein-protein interaction (PPI) network for these genes drawn using STRING and Cytoscape software.

Results

The findings introduced DEGs including CTNNB1, PAK2, MAP2K1, PNPLA6, IGF1R, FOXL2, DKK3, LAMA4, PABPN1, and GDPD5, and DEmiRs linked to AD (miR-106A, miR-1826, miR-1253, miR-10B, miR-18B, miR-101-2, miR-761, miR-199A1, miR-379 and miR-668), (miR-720, miR-218-2, miR-25, miR-602, miR-1226, miR-548K, miR-H1, miR-410, miR-548F2, miR-181A2), (miR-1470, miR-651, miR-544, miR-1826, miR-195, miR-610, miR-599, miR-323, miR-587 and miR-340), and (miR-1282, miR-1914, miR-642, miR-1323, miR-373, miR-323, miR-1322, miR-612, miR-606 and miR-758) in cerebellum, frontal cortex, temporal cortex, and entorhinal cortex, respectively.

Conclusions

The majority of the genes and miRNAs identified by our findings may be employed as biomarkers for prediction, diagnosis, or therapy response monitoring.

Guanylate cyclase-C Signaling Axis as a theragnostic target in colorectal cancer: a systematic review of literature

Introduction

Colorectal cancer (CRC) is a devastating disease that affects millions of people worldwide. Recent research has highlighted the crucial role of the guanylate cyclase-C (GC-C) signaling axis in CRC, from the early stages of tumorigenesis to disease progression. GC-C is activated by endogenous peptides guanylin (GU) and uroguanylin (UG), which are critical in maintaining intestinal fluid homeostasis. However, it has been found that these peptides may also contribute to the development of CRC. This systematic review focuses on the latest research on the GC-C signaling axis in CRC.

Methods

According to the aim of the study, a systematic literature search was conducted on Medline and PubMed databases. Ultimately, a total of 40 articles were gathered for the systematic review.

Results

Our systematic literature search revealed that alterations in GC-C signaling compartments in CRC tissue have demonstrated potential as diagnostic, prognostic, and therapeutic markers. This research highlights a potential treatment for CRC by targeting the GC-C signaling axis. Promising results from recent studies have explored the use of this signaling axis to develop new vaccines and chimeric antigen receptors that may be used in future clinical trials.

Conclusion

The findings presented in this review provide compelling evidence that targeting the GC-C signaling axis may be an advantageous approach for treating CRC.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

Comprehensive analysis and validation reveal potential MYCN regulatory biomarkers associated with neuroblastoma prognosis

Neuroblastoma (NB) is an embryonic malignant tumor that occurs in the sympathetic nervous system. The treatment results of patients in the high-risk group are poor, and relapse and treatment failure can occur even with multiple combination treatments. The proto-oncogene MYCN is a BHLH Transcription Factor used as an independent prognostic factor for NB. The proportion of MYCN amplification in tumor tissues of high-risk patients reaches 40-50%. Hence, exploring new MYCN target genes is a meaningful approach in developing treatment for high-risk NB patients. The microarray datasets were obtained from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and miRPathDB were used for enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and for modular analysis. The miRNet and NetworkAnalyst databases were used to predict and construct gene-miRNA and gene-TFs networks. The R2 database was used for expression, correlation, and prognostic analyses. The diagnostic value of the biomarkers was predicted by ROC analysis, and RT-qPCR was used to validate the identified hub genes. Finally, using specific MYCN siRNA and overexpressing plasmids, the correlation between the identified hub genes and MYCN was investigated. Our results showed that FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are novel biomarkers that affect the prognosis of the NB patients. In addition, FBXO9, RNF19B, and TRIM36 were preliminarily confirmed as potential target genes of MYCN. Overall, FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are expected to become novel biomarkers for the treatment of high-risk NB patients.Communicated by Ramaswamy H. Sarma.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Identification of COX4I2 as a hypoxia-associated gene acting through FGF1 to promote EMT and angiogenesis in CRC

Background

Current evidence suggests that the hypoxic tumor microenvironment further aggravates tumor progression, leading to poor therapeutic outcomes. There is as yet no biomarker capable of evaluating the hypoxic state of the tumor. The cytochrome c oxidase (COX) subunit is crucial to the mitochondrial respiratory chain.

Methods

We investigated the potential oncogenic role of COX subunit 4 isoform 2 gene (COX4I2) in colorectal cancer (CRC) by least absolute shrinkage and selection operator (LASSO) and COX regression analysis to examine whether COX4I2 overexpression can predict colorectal cancer (CRC) prognosis. The association of COX4I2 levels with clinical features and its biological actions were evaluated both in vitro and in vivo.

Results

Our analysis showed that elevated COX4I2 levels were correlated with poor clinical outcomes. We also observed that that COX4I2 may be involved in epithelial-mesenchymal transition, activation of cancer-related fibroblasts and angiogenesis in relation to fibroblast growth factor 1.

Conclusions

The COX4I2 level may be a predictor of outcome in CRC and may represent a novel target for treatment development.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s11658-022-00380-2.

An analysis of the significance of the Tre2/Bub2/CDC 16 (TBC) domain protein family 8 in colorectal cancer

The TBC (Tre-2/Bub2/Cdc16, TBC) structural domain is now considered as one of the factors potentially regulating tumor progression. However, to date, studies on the relationship between TBC structural domains and tumors are limited. In this study, we identified the role of TBC1 domain family member 8 (TBC1D8) as an oncogene in colorectal cancer (CRC) by least absolute shrinkage and selection operator (LASSO) and Cox regression analysis, showing that TBC1D8 may independently predict CRC outcome. Functional enrichment and single-cell analysis showed that TBC1D8 levels were associated with hypoxia. TBC1D8 levels were also positively correlated with M2 macrophage infiltration, which may have a complex association with hypoxia. Taken together, these results show that the TBC1D8 gene is involved in colorectal carcinogenesis, and the underlying molecular mechanisms may include hypoxia and immune cell infiltration.

An Analysis Regarding the Association Between Connexins and Colorectal Cancer (CRC) Tumor Microenvironment

Background

Gap junctions, as one of the major ways to maintain social connections between cells, are now considered as one of the potential regulators of tumor metastasis. However, to date, studies on the relationship between gap junctions and colorectal cancer (CRC) are limited.

Methods

We synthesized connexins-coding gene expression data from public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatics analysis was performed using R software and several database resources such as MEXPRESS database, Gene Set Cancer Analysis (GSCA) database, Human Protein Atlas (HPA) database, Tumor Immune Single Cell Hub (TISCH) database, Search Tool for Retrieval of Gene Interaction Relationships (STRING), and Cytoscape software, etc., to investigate the biological mechanisms that may be involved in connexins. Immunofluorescence and immunohistochemical staining were used to validate the expression and localization of GJA4.

Results

We found that CRC patients can be divided into two connexin clusters and that patients in cluster C1 had shorter survival than in cluster C2. The infiltration of M1 macrophages and NK cells was lower in cluster C1, while the levels of M2 macrophages and immune checkpoints were higher, indicating an immunosuppressed state in cluster C1. In addition, the epithelial–mesenchymal transition (EMT) phenotype was significantly activated in cluster C1. We observed that GJA4 was up-regulated in colorectal cancer tissues, which was related to poor prognosis. It was mainly expressed in fibroblasts, but the expression levels in normal intestinal epithelial cells were low. Finally, we found that GJA4 was associated with M2 macrophages and may be a potential immunosuppressive factor.

Conclusion

We found that there is a significant correlation between abnormal connexins expression and patients’ prognosis, and connexins play an important role in stromal-tumor interactions. Connexins, especially GJA4, can help enhance our understanding of tumor microenvironment (TME) and may guide more effective immunotherapeutic strategies.

Expression Profiles and Prognostic Value of FABPs in Colorectal Adenocarcinomas

Colorectal cancer (CRC) is one of the world's leading causes of cancer-related deaths; thus, it is important to detect it as early as possible. Obesity is thought to be linked to a large rise in the CRC incidence as a result of bad dietary choices, such as a high intake of animal fats. Fatty acid-binding proteins (FABPs) are a set of molecules that coordinate intracellular lipid responses and are highly associated with metabolism and inflammatory pathways. There are nine types of FABP genes that have been found in mammals, which are FABP1-7, FABP9, and FABP12. Each FABP gene has its own roles in different organs of the body; hence, each one has different expression levels in different cancers. The roles of FABP family genes in the development of CRC are still poorly understood. We used a bioinformatics approach to examine FABP family gene expression profiles using the Oncomine, GEPIA, PrognoScan, STRING, cBioPortal, MetaCore, and TIMER platforms. Results showed that the FABP6 messenger (m)RNA level is overexpressed in CRC cells compared to normal cells. The overexpression of FABP6 was found to be related to poor prognosis in CRC patients' overall survival. The immunohistochemical results in the Human Protein Atlas showed that FABP1 and FABP6 exhibited strong staining in CRC tissues. An enrichment analysis showed that high expression of FABP6 was significantly correlated with the role of microRNAs in cell proliferation in the development of CRC through the insulin-like growth factor (IGF) signaling pathway. FABP6 functions as an intracellular bile-acid transporter in the ileal epithelium. We looked at FABP6 expression in CRC since bile acids are important in the carcinogenesis of CRC. In conclusion, high FABP6 expression is expected to be a potential biomarker for detecting CRC at the early stage.