Integrated Analysis of Gene Expression and Metabolite Data Reveals Candidate Molecular Markers in Colorectal Carcinoma

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

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.

Introduction of long non-coding RNAs to regulate autophagy-associated therapy resistance in cancer

Autophagy is a lysosomal degradation pathway that depends on various evolutionarily conserved autophagy-related genes (ATGs). Dysregulation of autophagy plays an important role in the occurrence and development of cancer. Chemotherapy, targeted therapy, radiotherapy, and immunotherapy are important treatment options for cancer, which can significantly improve the survival rate of cancer patients. However, the occurrence of therapy resistance results in therapeutic failure and poor prognosis of cancer. Accumulating studies have found that long non-coding RNAs (lncRNAs) are well known as crucial regulators to control autophagy through regulating ATGs and autophagy-associated signaling pathways, including the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway, ultimately mediating chemoresistance and radioresistance. Taken together, this review systematically summarizes and elucidates the pivotal role of lncRNAs in cancer chemoresistance and radioresistance via regulating autophagy. Understanding the specific mechanism of which may provide autophagy-related therapeutic targets for cancer in the future.

Fusobacterium nucleatum promotes colon cancer progression by changing the mucosal microbiota and colon transcriptome in a mouse model

BACKGROUND

Fusobacterium nucleatum (F. nucleatum) has long been known to cause opportunistic infections and has recently been implicated in colorectal cancer (CRC), which has attracted broad attention. However, the mechanism by which it is involved in CRC development is not fully understood.

AIM

To explore its potential causative role in CRC development, we evaluated the colon pathology, mucosa barrier, colon microbiota and host transcriptome profile after F. nucleatum infection in an azoxymethane/dextran sulfate sodium salt (AOM/DSS) mouse model.

METHODS

Three groups of mice were compared to reveal the differences, i.e., the control, AOM/DSS-induced CRC and AOM/DSS-FUSO infection groups.

RESULTS

Both the AOM/DSS and AOM/DSS-FUSO groups exhibited a significantly reduced body weight and increased tumor numbers than the control group, and AOM/DSS mice with F. nucleatum infection showed the highest tumor formation ratio among the three groups. Moreover, the colon pathology was the most serious in the AOM/DSS-FUSO group. We found that the structure of the colon microbiota changed considerably after F. nucleatum infection; striking differences in mucosal microbial population patterns were observed between the AOM/DSS-FUSO and AOM/DSS groups, and inflammation-inducing bacteria were enriched in the mucosal microbiota in the AOM/DSS-FUSO group. By comparing intestinal transcriptomics data from AOM vs AOM/DSS-FUSO mice, we showed that transcriptional activity was strongly affected by dysbiosis of the gut microbiota. The most microbiota-sensitive genes were oncogenes in the intestine, and the cyclic adenosine monophosphate signaling pathway, neuroactive ligand–receptor interaction, PPAR signaling pathway, retinol metabolism, mineral absorption and drug metabolism were highly enriched in the AOM/DSS-FUSO group. Additionally, we showed that microbial dysbiosis driven by F. nucleatum infection enriched eight taxa belonging to Proteobacteria, which correlates with increased expression of oncogenic genes.

CONCLUSION

Our study demonstrated that F. nucleatum infection altered the colon mucosal microbiota by enriching pathogens related to the development of CRC, providing new insights into the role of F. nucleatum in the oncogenic microbial environment of the colon.