Identification of hub genes and outcome in colon cancer based on bioinformatics analysis

Identification of key biomarker genes in liver hepatocellular carcinoma and kidney renal clear cell carcinoma progression: A shared high-throughput screening and molecular docking method with potentials for targeted therapeutic interventions

BACKGROUND AND OBJECTIVES

Liver Hepatocellular Carcinoma (LIHC) and Kidney Renal Clear Cell Carcinoma (KIRC) are leading causes of cancer death worldwide, but their early detections remain hindered by a lack of genetic markers. Our study aims to find prospective biomarkers that could serve as prognostic indicators for efficient drug candidates for KIRC and LIHC treatment.

METHODS

To detect differentially expressed genes (DEGs), four datasets were used: GSE66271 and GSE213324 for KIRC, and GSE135631 and GSE202853 for LIHC. Visualization of DEGs was done using heatmaps, volcano plots, and Venn diagrams. Hub genes were identified via PPI analysis and the cytoHubba plugin in Cytoscape. Their expression was evaluated using box plots, stage plots, and survival plots for prognostic assessment via GEPIA2. Molecular docking with PyRx's AutoDock Vina identified optimal binding interactions between compounds and proteins. Pharmacokinetic and toxicity analyses reinforced the drug-likeness and safety of these compounds.

RESULTS

In this study, 47 DEGs were identified, with the top 10 hub genes being TOP2A, BUB1, PTTG1, CCNB2, NUSAP1, KIF20A, BIRC5, RRM2, NDC80 and CDC45, chosen for their high MCC scores. Data mining revealed a correlation between TOP2A expression and clinical survival outcomes in KIRC and LIHC patients. Docking studies of the TOP2A structure identified a promising compound from Andrographis paniculata with high binding energy and interactions with TOP2A. Pharmacokinetic and toxicity assessments support its potential as a drug candidate.

CONCLUSION

Our study emphasizes TOP2A's prognostic significance in KIRC and LIHC and recognizes Andrographis paniculata compound as potential therapeutics, offering prospective for enhanced treatment and patient outcomes in these cancers.

Discovery of LINC01614 associated with the SPP1 gene in colorectal cancer

Colorectal cancer (CRC) is a prevalent malignancy worldwide, driven by complex molecular mechanisms. This study aims to elucidate the role of lncRNAs within TGF-β pathway, a crucial signaling pathway in CRC progression, focusing specifically on their interaction with the SPP1 gene. We employed a multi-faceted approach, starting with comprehensive in silico analyses to identify candidate lncRNAs potentially involved in TGF-β pathway regulation. These candidates were further validated through experimental RT-qPCR assays, comparing lncRNA expression profiles in CRC tissues to adjacent normal samples. Our findings revealed novel lncRNA candidates with significant associations with SPP1 in CRC, highlighting their potential regulatory roles in the TGF-β pathway. This integrative study underscores the importance of combining computational predictions with laboratory experimentation to uncover complex regulatory networks in cancer, providing insights into new therapeutic targets and diagnostic biomarkers for CRC.

Integrated bioinformatics approach to unwind key genes and pathways involved in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the fifth leading cause of death in India. Until now, the exact pathogenesis concerning CRC signaling pathways is largely unknown; however, the diseased condition is believed to deteriorate with lifestyle, aging, and inherited genetic disorders. Hence, the identification of hub genes and therapeutic targets is of great importance for disease monitoring.

OBJECTIVE

Identification of hub genes and targets for identification of candidate hub genes for CRC diagnosis and monitoring.

MATERIALS AND METHODS

The present study applied gene expression analysis by integrating two profile datasets (GSE20916 and GSE33113) from NCBI-GEO database to elucidate the potential key candidate genes and pathways in CRC. Differentially expressed genes (DEGs) between CRC (195 CRC tissues) and healthy control (46 normal mucosal tissue) were sorted using GEO2R tool. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using Cluster Profiler in Rv. 3.6.1. Moreover, protein-protein interactions (PPI), module detection, and hub gene identification were accomplished and visualized through the Search Tool for the Retrieval of Interacting Genes, Molecular Complex Detection (MCODE) plug-in of Cytoscape v3.8.0. Further hub genes were imported into ToppGene webserver for pathway analysis and prognostic expression analysis was conducted using Gene Expression Profiling Interactive Analysis webserver.

RESULTS

A total of 2221 DEGs, including 1286 up-regulated and 935down-regulated genes mainly enriched in signaling pathways of NOD-like receptor, FoxO, AMPK signalling and leishmaniasis. Three key modules were detected from PPI network using MCODE. Besides, top 20 high prioritized hub genes were selected. Further, prognostic expression analysis revealed ten of the hub genes, namely IL1B, CD44, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, MMP9, CREB1, STAT1, vascular endothelial growth factor (VEGFA), CDC5 L, Ataxia-telangiectasia mutated (ATM + and CDH1 to be differently expressed in normal and cancer patients.

CONCLUSION

The present study proposed five novel therapeutic targets, i.e., ATM, GAPDH, CREB1, VEGFA, and CDH1 genes that might provide new insights into molecular oncogenesis of CRC.

GNG4, as a potential predictor of prognosis, is correlated with immune infiltrates in colon adenocarcinoma

The tumour microenvironment (TME) and immunosuppression play an important role in colon cancer (CC) metastasis, which seriously affects the prognosis of CC. G protein subunit gamma 4 (GNG4) has been shown to participate in tumour progression and the tumour mutation burden (TMB) in colorectal cancer. However, the effect of GNG4 on the CC TME and immunology remains elusive. Weighted gene coexpression network analysis (WGCNA) was employed for screening aberrantly expressed genes associated with the immune score, and GNG4 was then selected through prognostic and immune correlation analysis. Based on RNA sequencing data obtained from the TCGA and GEO databases, the expression pattern and immune characteristics of GNG4 were comprehensively examined using a pan-cancer analysis. Upregulation of GNG4 was linked to an adverse prognosis and immune inhibitory phenotype in CC. Pan-cancer analysis demonstrated higher GNG4 expression in tumours than in paired normal tissue in human cancers. GNG4 expression was closely related to prognosis, TMB, immune checkpoints (ICPs), microsatellite instability (MSI) and neoantigens. GNG4 promoted CC cell proliferation, migration and invasion and participated in immune regulation in the TME. Significantly, GNG4 expression was found to negatively correlate with tumour-infiltrating immune cells, ICP, TMB and MSI in CC. GNG4 expression predicted the immunotherapy response in the IMvigor210 cohort, suggesting that GNG4 could be used as a potential biomarker in CC for prognostication and immunology. Moreover, the expression of GNG4 predicted the immunotherapy response of ICB in CC.

Robust microarray data feature selection using a correntropy based distance metric learning approach

Classification of high-dimensional microarray data is a challenge in bioinformatics and genetic data processing. One of the challenging issues of feature selection is the presence of outliers. The Euclidean distance metric is sensitive to outliers. In this study, a distance metric learning based feature selection approach that uses the correntropy function as the discrimination metric is proposed. For this purpose, the metric learning problem is formulated as an optimization problem and solved using the Lagrange method. The output of the approach signifies the most important and robust features. After feature selection, different classification methods such as SVM, decision trees, and NN classifiers are used to investigate the classification accuracy of the proposed method as well as precision, recall, and F-measure. Experiments are carried out on 13 high-dimensional datasets and show that the proposed method outperforms the previous models in terms of accuracy and robustness.

LINC01589 serves as a potential tumor-suppressor and immune-related biomarker in endometrial cancer: A review

Currently, increasing attention is being paid to biomarkers in endometrial cancer. Immune infiltration of the tumor microenvironment has been shown to significantly affect the overall survival (OS) of uterine corpus endometrial carcinoma (UCEC) patients. LINC01589 is a long non-coding RNA (lncRNA) that is rarely reported in cancer and is assumed to play a role in immune regulation. We therefore evaluated the role of LINC01589 in UCEC using the Cancer Genome Atlas (TCGA) database. We analyzed the expression of LINC01589 using the gene expression profiles of LINC01589 in the UCEC projects in TCGA. Comparisons between the differentially expressed genes (DEGs) of the cancer and adjacent normal tissues of the UCEC projects revealed that LINC01589 expression was decreased in UCEC tissues. A multivariate cox regression analysis indicated that LINC01589 upregulation could serve as an independent prognostic factor for survival. Furthermore, there was a positive correlation between LINC01589 expression and B cell, T cell, NK cell, monocytic lineage, and myeloid dendritic cell infiltration in UCEC patients. In addition, 5 clusters of hub genes were detected by comparison of different expression levels of LINC01589 in the UCEC groups. The analysis of the reactome pathway using gene set enrichment analysis (GSEA) revealed immune-related pathways, including CD22-mediated B cell receptor (BCR) regulation and antigen-activated BCRs, leading to the generation of second messengers and complement cascade pathways that were significantly enriched in the high LINC01589 expression group. Thus, LINC01589 may serve as a prognostic biomarker, as it is associated with immune infiltration in UCEC.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

The Somatic Mutation Landscape of UDP-Glycosyltransferase (UGT) Genes in Human Cancers

The human UDP-glycosyltransferase (UGTs) superfamily has a critical role in the metabolism of anticancer drugs and numerous pro/anti-cancer molecules (e.g., steroids, lipids, fatty acids, bile acids and carcinogens). Recent studies have shown wide and abundant expression of UGT genes in human cancers. However, the extent to which UGT genes acquire somatic mutations within tumors remains to be systematically investigated. In the present study, our comprehensive analysis of the somatic mutation profiles of 10,069 tumors from 33 different TCGA cancer types identified 3427 somatic mutations in UGT genes. Overall, nearly 18% (1802/10,069) of the assessed tumors had mutations in UGT genes with huge variations in mutation frequency across different cancer types, ranging from over 25% in five cancers (COAD, LUAD, LUSC, SKCM and UCSC) to less than 5% in eight cancers (LAML, MESO, PCPG, PAAD, PRAD, TGCT, THYM and UVM). All 22 UGT genes showed somatic mutations in tumors, with UGT2B4, UGT3A1 and UGT3A2 showing the largest number of mutations (289, 307 and 255 mutations, respectively). Nearly 65% (2260/3427) of the mutations were missense, frame-shift and nonsense mutations that have been predicted to code for variant UGT proteins. Furthermore, about 10% (362/3427) of the mutations occurred in non-coding regions (5' UTR, 3' UTR and splice sites) that may be able to alter the efficiency of translation initiation, miRNA regulation or the splicing of UGT transcripts. In conclusion, our data show widespread somatic mutations of UGT genes in human cancers that may affect the capacity of cancer cells to metabolize anticancer drugs and endobiotics that control pro/anti-cancer signaling pathways. This highlights their potential utility as biomarkers for predicting therapeutic efficacy and clinical outcomes.

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan–Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.

Identification of key regulators associated with colon cancer prognosis and pathogenesis

Colon cancer (CC) is the fourth deadliest cancer in the world. New insights into prognostication might be helpful to define the optimal adjuvant treatments for patients in routine clinical practice. Here, a microarray dataset with 30 primary tumors and 30 normal samples was analyzed using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG, ChEA and X2K were used to analyze DEGs-related Gene Ontology, pathways, transcription factors (TFs) and kinases, respectively. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of DEGs was determined through MCODE and CytoHubba plugins, and the expression of hub genes was verified using GEPIA. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 233 and 373 genes were upregulated and downregulated in CC, respectively. GO analysis showed that the upregulated DEGs were mainly involved in mitotic nuclear division and cell division. Top 10 hub genes were identified, including AURKB, CDK1, DLGAP5, AURKA, CCNB2, CCNB1, BUB1B, CCNA2, KIF20A and BUB1. Whereas, FOMX1, E2F7, E2F1, E2F4 and AR were identified as top 5 TFs in CC. Moreover, CDK1, CDC2, MAPK14, ATM and CK2ALPHA was identified as top 5 kinases in CC. miRNAs analysis showed that Hsa-miR-215-5p hsa-miR-193b-3p, hsa-miR-192-5p and hsa-miR-16-5p could target the largest number of CC genes. Taken together, CC-related genes, especially the hub genes, TFs, and metabolites might be used as novel biomarkers for CC, as well as for diagnosis and guiding therapeutic strategies for CC.

Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes

Simple Summary

We developed a predictive approach using different machine learning methods to identify a number of genes that can potentially serve as novel diagnostic colon cancer biomarkers.

Abstract

Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

Multimerin-1 and cancer: a review

Multimerin-1 (MMRN1) is a platelet protein with a role in haemostasis and coagulation. It is also present in endothelial cells (ECs) and the extracellular matrix (ECM), where it may be involved in cell adhesion, but its molecular functions and protein–protein interactions in these cellular locations have not been studied in detail yet. In recent years, MMRN1 has been identified as a differentially expressed gene (DEG) in various cancers and it has been proposed as a possible cancer biomarker. Some evidence suggest that MMRN1 expression is regulated by methylation, protein interactions, and non-coding RNAs (ncRNAs) in different cancers. This raises the questions if a functional role of MMRN1 is being targeted during cancer development, and if MMRN1’s differential expression pattern correlates with cancer progression. As a result, it is timely to review the current state of what is known about MMRN1 to help inform future research into MMRN1’s molecular mechanisms in cancer.

GNG4 Promotes Tumor Progression in Colorectal Cancer

Colorectal cancer is a common digestive system tumor, which lacks effective therapeutic targets and biomarkers to accurately determine the prognosis. Sequencing data, immunohistochemistry, and Kaplan–Meier analysis were used to explore GNG4 clinical significance in colorectal cancer. And, through in vitro experiments, the effects of GNG4 on cell behaviors were investigated. The results showed that the mRNA and protein expression levels of GNG4 in patients with colorectal cancer were significantly higher than in normal people. The patients with high GNG4 expression had a worse prognosis than patients with low GNG4 expression. The in vitro experiments presented that after downregulating the expression of GNG4, proliferation, migration, and invasion of SW-620 colon cancer cells were all significantly reduced, apoptosis was significantly increased, and the cell cycle was blocked in the S phase. In summary, GNG4 may be of importance in the therapy of the colorectal cancer; therefore, targeting GNG4 may have certain clinical value in the treatment of colorectal cancer.

Key genes affecting the progression of nasopharyngeal carcinoma identified by RNA-sequencing and bioinformatic analysis

Background: The present work was conducted to screen the potential biomarkers affecting nasopharyngeal carcinoma (NPC) progression through RNA-sequencing (RNA-seq), bioinformatic analysis and functional experiments.

Materials and Methods: Six normal samples and five NPC clinical samples were collected for RNA-seq analysis. The expression levels in both groups were determined through student’s t-test. We identified genes of P < 0.01 as the differentially expressed genes (DEGs). In addition, gene set enrichment analysis (GSEA) was conducted. Afterwards, STRING V10 database was employed to extract protein interactions among the DEGs. Later, we established a protein-protein interaction (PPI) network, and used the Cytoscape software for network visualization. qRT-PCR was conducted to verify hub genes from clinical samples. Then, the function of CXCL10 in cell proliferation, apoptosis, invasion and migration was evaluated.

Results: A total of 2024 DEGs were identified, among which, 1449 were down-regulated and 575 were up-regulated. The PPI was constructed, and the hub genes including Insulin Like Growth Factor 1 (IGF1), C-X-C Motif Chemokine Ligand 10 (CXCL10), Interleukin 13 (IL13), Intercellular Adhesion Molecule 1 (ICAM1), G Protein Subunit Gamma Transducin 1 (GNGT1), Matrix Metallopeptidase 1 (MMP1), Neurexin 1 (NRXN1) and Matrix Metallopeptidase 3 (MMP3) were obtained. The expression levels of CXCL10, IGF1, MMP3, MMP1, ICAM1, and IL-13 were significantly up-regulated in tumor tissues. High expression levels of CXCL10, MMP3 and ICAM1 predicted poor prognosis of NPC patients. CXCL10 silencing suppressed NPC cell proliferation and migration.

Conclusions: CXCL10 may serve as a potential key gene affecting NPC genesis and progression.

The Expression Profiles and Deregulation of UDP-Glycosyltransferase (UGT) Genes in Human Cancers and Their Association with Clinical Outcomes

The human UDP-glycosyltransferase (UGTs) superfamily has 22 functional enzymes that play a critical role in the metabolism of small lipophilic compounds, including carcinogens, drugs, steroids, lipids, fatty acids, and bile acids. The expression profiles of UGT genes in human cancers and their impact on cancer patient survival remains to be systematically investigated. In the present study, a comprehensive analysis of the RNAseq and clinical datasets of 9514 patients from 33 different TCGA (the Genome Cancer Atlas) cancers demonstrated cancer-specific UGT expression profiles with high interindividual variability among and within individual cancers. Notably, cancers derived from drug metabolizing tissues (liver, kidney, gut, pancreas) expressed the largest number of UGT genes (COAD, KIRC, KIRP, LIHC, PAAD); six UGT genes (1A6, 1A9, 1A10, 2A3, 2B7, UGT8) showed high expression in five or more different cancers. Kaplan-Meier plots and logrank tests revealed that six UGT genes were significantly associated with increased overall survival (OS) rates [UGT1A1 (LUSC), UGT1A6 (ACC), UGT1A7 (ACC), UGT2A3 (KIRC), UGT2B15 (BLCA, SKCM)] or decreased OS rates [UGT2B15 (LGG), UGT8 (UVM)] in specific cancers. Finally, differential expression analysis of 611 patients from 12 TCGA cancers identified 16 UGT genes (1A1, 1A3, 1A6, 1A7, 1A8, 1A9, 1A10, 2A1, 2A3, 2B4, 2B7, 2B11, 2B15, 3A1, 3A2, UGT8) that were up/downregulated in at least one cancer relative to normal tissues. In conclusion, our data show widespread expression of UGT genes in cancers, highlighting the capacity for intratumoural drug metabolism through the UGT conjugation pathway. The data also suggests the potentials for specific UGT genes to serve as prognostic biomarkers or therapeutic targets in cancers.

Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications

INTRODUCTION

The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier.

METHODS

Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features.

RESULTS

Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes.

CONCLUSION

In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.

Contribution of endothelial cell-derived transcriptomes to the colon cancer based on bioinformatics analysis

Colon tumor endothelial cells (CTECs) plays substantial roles to induce immune invasion, angiogenesis and metastasis. Thus, identification of the CTECs-derived transcriptomes could be helpful for colon cancer diagnosis and potential therapy.

METHODS

By analysis of CTECs-derived gene expression profiling dataset, we identified differentially expressed genes (DEGs) between CTECs and colon normal endothelial cells (CNECs). In addition, we identified the significant pathways and protein-protein interaction (PPI) network that was significantly associated with the DEGs. Furthermore, we identified hub genes whose expression was significantly associated with prognosis and immune cell infiltrations in colon cancer. Finally, we identified the significant correlations between the prognostic hub genes and immune-inhibitory markers in colon cancer.

RESULTS

We identified 362 DEGs in CTECs relative to the CNECs, including117 up-regulated genes and 245 down-regulated genes in the CTECs. In addition, we identified significantly up-regulated pathways in CTECs that were mainly involved in cancer and immune regulation. Furthermore, we identified hub genes (such as SPARC, COL1A1, COL1A2 and IGFBP3) that are associated with prognosis and immune cells infiltrations in colon cancer. Interestingly, we found that prognosis-associated hub genes (SPARC, COL1A1, COL1A2 and IGFBP3) are positively correlated with immune-inhibitory markers of various immunosuppressive cells, including TAM, M2 macrophage, Tregs and T cell exhaustion. Finally, our findings revealed that prognosis-associated upregulated hub genes are positively correlated with immune checkpoint markers, including PD-L1 and PD-L2 and the immunosuppressive markers including TGFB1 and TGFBR1.

CONCLUSIONS

The identification of CTECs-specific transcriptomes may provide crucial insights into the colon tumor microenvironment that mediates the development of colon cancer.

A New Oxaliplatin Resistance-Related Gene Signature With Strong Predicting Ability in Colon Cancer Identified by Comprehensive Profiling

Numerous colon cancer cases are resistant to chemotherapy based on oxaliplatin and suffer from relapse. A number of survival- and prognosis-related biomarkers have been identified based on database mining for patients who develop drug resistance, but the single individual gene biomarker cannot attain high specificity and sensitivity in prognosis prediction. This work was conducted aiming to establish a new gene signature using oxaliplatin resistance-related genes to predict the prognosis for colon cancer. To this end, we downloaded gene expression profile data of cell lines that are resistant and not resistant to oxaliplatin from the Gene Expression Omnibus (GEO) database. Altogether, 495 oxaliplatin resistance-related genes were searched by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. As suggested by functional analysis, the above genes were mostly enriched into cell adhesion and immune processes. Besides, a signature was built based on four oxaliplatin resistance-related genes selected from the training set to predict the overall survival (OS) by stepwise regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. Relative to the low risk score group, the high risk score group had dismal OS (P < 0.0001). Moreover, the area under the curve (AUC) value regarding the 5-year OS was 0.72, indicating that the risk score was accurate in the prediction of OS for colon cancer patients (AUC >0.7). Additionally, multivariate Cox regression suggested that the signature constructed based on four oxaliplatin resistance-related genes predicted the prognosis for colon cancer cases [hazard ratio (HR), 2.77; 95% CI, 2.03–3.78; P < 0.001]. Finally, external test sets were utilized to further validate the stability and accuracy of oxaliplatin resistance-related gene signature for prognosis of colon cancer patients. To sum up, this study establishes a signature based on four oxaliplatin resistance-related genes for predicting the survival of colon cancer patients, which sheds more light on the mechanisms of oxaliplatin resistance and helps identify colon cancer cases with a dismal prognostic outcome.

Prognostic biomarkers and therapeutic targets in oral squamous cell carcinoma: a study based on cross-database analysis

Background

Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC.

Methods

Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature.

Results

Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively.

Conclusion

These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00181-1.

MicroRNA‑642a‑5p inhibits colon cancer cell migration and invasion by targeting collagen type I α1

The aim of the present study was to explore the mechanism by which microRNA (miR)‑642a‑5p regulates the migration and invasion of colon cancer cells via collagen type I α1 (COL1A1). The characteristics of miR‑642a‑5p and COL1A1 were analysed through bioinformatics. Cancer and normal tissues were collected from patients with colon cancer. miR‑642a‑5p‑ and COL1A1‑overexpressing cell lines were constructed by transfection. A dual‑luciferase reporter assay was used to verify the targeting of COL1A1 by miR‑642a‑5p. Cell Counting Kit‑8, wound healing and Transwell assays were used to detect cell viability, migration and invasion, respectively. Protein and mRNA expression levels were examined by western blotting and reverse transcription‑quantitative PCR, respectively. The results revealed that miR‑642a‑5p expression was significantly upregulated and COL1A1 expression was downregulated in patients with colon cancer. Low levels of miR‑642a‑5p and high levels of COL1A1 were associated with a poor prognosis in patients with colon cancer. miR‑642a‑5p directly targeted the 3'‑untranslated region of COL1A1 and inhibited COL1A1 expression. Overexpression of miR‑642a‑5p inhibited cell viability, migration, invasion and epithelial mesenchymal transition. Overexpression of COL1A1 promoted cell viability, migration, invasion and EMT, and partially reversed the inhibitory effects of miR‑642a‑5p on colon cancer cells. In conclusion, miR‑642a‑5p inhibited colon cancer cell migration, invasion and EMT by regulating COL1A1.

Identifications of Candidate Genes Significantly Associated With Rectal Cancer by Integrated Bioinformatics Analysis

INTRODUCTION

Rectal cancer ranks as the eighth in cancer-related morbidity and the tenth in the cancer-related mortality. A few studies have explored several biomarkers for colorectal cancer. However, there is still a great need for us to excavate novel biomarkers with effective and efficient diagnostic and prognostic values to discover the etiology and pathogenesis of rectal cancer separately. Therefore, we aimed to identify more novel candidate genes that were significantly associated with rectal cancer through integrated bioinformatics analysis.

METHODS

We analyzed the gene expression profiles of GSE15781 and GSE20842 from Gene Expression Omnibus database to identify differentially expressed genes between normal rectal tissue and rectal cancer tissue.

RESULTS

We searched for core genes, carried out survival analysis and analyzed the expressions of core genes. We found that 142 genes were significantly upregulated, and 229 genes were significantly downregulated in all 3 independent studies. In KEGG analysis, the upregulated genes were significantly enriched in cytokine-cytokine receptor interaction, IL-17 signaling pathway, cell cycle, etc. The downregulated genes were primarily enriched in nitrogen metabolism, mineral absorption and pentose and glucuronate interconversions. Inhibin subunit beta B (INHBB) expressed markedly higher in rectal cancer tissues compared with normal tissues, and claudins (CLDN) 23 expressed significantly lower in rectal cancer tissues.

CONCLUSION

In conclusion, we discovered that INHBB could provide a great significant diagnostic and prognostic values for rectal cancer.

Molecular heterogeneity of guanine nucleotide binding-protein γ subunit 4 in left- and right-sided colon cancer

Molecular heterogeneity determines the differences in the pathological features, prognosis and survival after relapse when comparing left-sided colon cancer (LCC) and right-sided colon cancer (RCC). At present, the discrepancy in the underlying molecular events between the two types of colon cancer has not been thoroughly investigated. The present study aimed to explore novel targets to predict the disease stage and prognosis of LCC and RCC. Expression analysis of guanine nucleotide binding-protein γ subunit 4 (GNG4) was performed using the Gene Expression Profiling Interactive Analysis (GEPIA) and Oncomine databases. Survival and association analyses were performed using GEPIA and the colon adenocarcinoma dataset from The Cancer Genome Atlas database. GNG4-positive cells in a tissue microarray were examined using immunohistochemistry. According to the GNG4 expression data from Caucasian patients included in the TCGA dataset, GNG4 was highly expressed and positively associated with pathological stage and overall survival (OS) rates in colon cancer. GNG4 expression was higher in LCC than in RCC. Patients with LCC with high GNG4 expression exhibited higher pathological stage and lower survival rates, whereas this was not observed in patients with RCC. In addition, the clinical tissues used in the microarray showed that GNG4 expression was increased in Chinese patients with LCC compared with that in patients with RCC. Consistently, GNG4 expression was negatively associated with OS in patients with LCC, but not in patients with RCC. However, no association was observed between GNG4 expression and the disease stage of colon cancer in both patients with LCC and RCC. Overall, the molecular heterogeneity of GNG4 in LCC and RCC suggests that GNG4 may be used as a diagnostic and prognostic biomarker in patients with LCC.

Important players in carcinogenesis as potential targets in cancer therapy: an update

The development of cancer is a problem that has accompanied mankind for years. The growing number of cases, emerging drug resistance, and the need to reduce the serious side effects of pharmacotherapy are forcing scientists to better understand the complex mechanisms responsible for the initiation, promotion, and progression of the disease. This paper discusses the modulation of the particular stages of carcinogenesis by selected physiological factors, including: acetylcholine (ACh), peroxisome proliferator-activated receptors (PPAR), fatty acid-binding proteins (FABPs), Bruton's tyrosine kinase (Btk), aquaporins (AQPs), insulin-like growth factor-2 (IGF-2), and exosomes. Understanding their role may contribute to the development of more effective and safer therapies based on new binding sites.

Identification of hub genes involved in apparent metabolizable energy of chickens

Feed efficiency is one of the most economically significant traits in a breeding program. Apparent metabolizable energy is the most used method to evaluate energy utilization for feed efficiency. The purpose of this study was to identify candidate genes of chickens with divergent apparent metabolizable energy by bioinformatics analysis. The gene expression profile of duodenal of the highest and lowest apparent metabolizable energy-ranked birds were analyzed. Differentially expressed genes were picked out using GEO2R tool. Gene ontology and pathway analysis were performed using bioinformatics tools. Cytoscape software was used to visualize protein-protein network. There were 201 DEGs, including 99 up-regulated genes enriched in metabolic pathways, Cellular senescence and Focal adhesion, and 102 down-regulated genes enriched in metabolic pathways, Regulation of actin cytoskeleton, Neuroactive ligand-receptor interaction, Calcium signaling pathway and Focal adhesion. Two important modules were detected and pathway enrichment analysis showed that they were mainly associated with Focal adhesion, Regulation of actin cytoskeleton and RNA transport. Fifteen hub genes were selected and among them, ITGA8, CDC42 and GSK3B might be the core genes related to apparent metabolizable energy of chickens. These hub genes can be used as biomarkers for apparent metabolizable energy and feed efficiency in breeding program of chickens.

Identification of key genes and pathways in syphilis combined with diabetes: a bioinformatics study

We noticed that syphilis patients seem to be more susceptible to diabetes and the lesions often involve the kidneys, but the pathogenesis is not yet completely understood. In this study, microarray analysis was performed to investigate the dysregulated expressed genes (DEGs) in rabbit model of syphilis combined with diabetes. A total of 1045 genes were identified to be significantly differentially expressed, among which 571 were up-regulated and 474 were down-regulated (≥ 2.0fold, p < 0.05). Using the database visualization and integration discovery for the Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis. The downregulated DEGs were significantly enriched for biosynthesis of antibiotics, carbon metabolism and protein digestion, while the upregulated DEGs were mainly enriched for cancer and PI3K-Akt signaling pathway. Molecular Complex Detection (MCODE) plugins were used to visualize protein-protein interaction (PPI) network of DEGs and Screening for hub genes and gene modules. ALB, FN1, CASP3, MMP9, IL8, CTGF, STAT3, IGF1, VCAM-1 and HGF were filtrated as the hub genes according to the degree of connectivity from the PPI network. To the best of our knowledge, this study is the first to comprehensively identify the expression patterns of dysregulated genes in syphilis combined with diabetes, providing a basis for revealing the underlying pathogenesis of syphilis combined with diabetes and exploring the goals of therapeutic intervention.

MicroRNAs Associated With Colon Cancer: New Potential Prognostic Markers and Targets for Therapy

MicroRNAs (miRNAs) are a kind of non-coding RNA (ncRNA) that regulate the expression of target genes and play a role in the occurrence and development of cancers. Colon cancer (COAD) is the second most common cause of cancer-related mortality. However, the prognostic value of miRNAs in COAD is still confusing. In this study, we obtain miRNAs and messenger RNAs (mRNAs) expression profiles of COAD from the Cancer Genome Atlas (TCGA) database. After preliminary data screening and preprocessing, we acquire the expression data of 894 miRNAs and 17,019 mRNAs. Then, compared with the normal samples, 39 upregulated miRNAs and 54 downregulated miRNAs are identified by differential expression analysis. Furthermore, we obtain 1,487 upregulated mRNAs and 2,847 downregulated mRNAs. We confirm nine key miRNAs related to the survival rate of COAD patients. Moreover, by using bioinformatics methods, we get 461 common genes from both the target genes of these nine key miRNAs and differentially expressed mRNAs. Through analyzing the protein-protein interaction (PPI) network of these 461 common genes and survival analysis, we confirm five hub genes as promising biomarkers for COAD prognosis. It is worth mentioning that no previous reports have found that PGR and KCNB1 are related to COAD. We expect these key miRNAs and hub genes will provide a new way for the study of COAD.