<em>miR-19a</em> targeting <em>CLCA4</em> to regulate the proliferation, migration, and invasion of colorectal cancer cells

Construction of a new prognostic model for colorectal cancer based on bulk RNA-seq combined with The Cancer Genome Atlas data

Background

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths, and improving the prognosis of CRC patients is an urgent concern. The aim of this study was to explore new immunotherapy targets to improve survival in CRC patients.

Methods

We analyzed CRC-related single-cell data GSE201348 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs). Subsequently, we performed differential analysis on the rectum adenocarcinoma (READ) and colon adenocarcinoma (COAD) transcriptome sequencing data [The Cancer Genome Atlas (TCGA)-CRC queue] and clinical data downloaded from TCGA database. Subgroup analysis was performed using CIBERSORTx and cluster analysis. Finally, biomarkers were identified by one-way cox regression as well as least absolute shrinkage and selection operator (LASSO) analysis.

Results

In this study, we analyzed CRC-related single-cell data GSE201348, and identified 5,210 DEGs. Subsequently, we performed differential analysis on the TCGA-CRC queue database, and obtained 4,408 DEGs. Then, we categorized the cancer samples in the sequencing data into three groups (k1, k2, and k3), with significant differences observed between the k1 and k2 groups via survival analysis. Further differential analysis on the samples in the k1 and k2 groups identified 1,899 DEGs. A total of 77 DEGs were selected among those DEGs obtained from three differential analyses. Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS).

Conclusions

To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC.

miR-17-92a-1 cluster host gene: a key regulator in colorectal cancer development and progression

Colorectal cancer (CRC), recognized among the five most prevalent malignancies and most deadly cancers, manifests multifactorial influences stemming from environmental exposures, dietary patterns, age, and genetic predisposition. Although substantial progress has been made in comprehending the etiology of CRC, the precise genetic components driving its pathogenesis remain incompletely elucidated. Within the expansive repertoire of non-coding RNAs, particular focus has centered on the miR-17-92a-1 cluster host gene (MIR17HG) and its associated miRNAs, which actively participate in diverse cellular processes and frequently exhibit heightened expression in various solid tumors, notably CRC. Therefore, the primary objective of this research is to undertake an extensive inquiry into the regulatory mechanisms, structural features, functional attributes, and potential diagnostic and therapeutic implications associated with this cluster in CRC. Furthermore, the intricate interplay between this cluster and the development and progression of CRC will be explored. Our findings underscore the upregulation of the miR-17-92a-1 cluster host gene (MIR17HG) and its associated miRNAs in CRC compared to normal tissues, thus implying their profound involvement in the progression of CRC. Collectively, these molecules are implicated in critical oncogenic processes, encompassing metastatic activity, regulation of apoptotic pathways, cellular proliferation, and drug resistance. Consequently, these findings shed illuminating insights into the potential of MIR17HG and its associated miRNAs as promising targets for therapeutic interventions in the management of CRC.

Weighted gene co-expression network analysis for hub genes in colorectal cancer

BACKGROUND

This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA).

METHODS

Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established.

RESULTS

After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC.

CONCLUSIONS

CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Decoding the interaction between miR-19a and CBX7 focusing on the implications for tumor suppression in cancer therapy

Cancer is a complex and multifaceted disease characterized by uncontrolled cell growth, genetic alterations, and disruption of normal cellular processes, leading to the formation of malignant tumors with potentially devastating consequences for patients. Molecular research is important in the diagnosis and treatment, one of the molecular mechanisms involved in various cancers is the fluctuation of gene expression. Non-coding RNAs, especially microRNAs, are involved in different stages of cancer. MicroRNAs are small RNA molecules that are naturally produced within cells and bind to the 3'-UTR of target mRNA, repressing gene expression by regulating translation. Overexpression of miR-19a has been reported in human malignancies. Upregulation of miR-19a as a member of the miR-17-92 cluster is key to tumor formation, cell proliferation, survival, invasion, metastasis, and drug resistance. Furthermore. bioinformatics and in vitro data reveal that the miR-19a-3p isoform binds to the 3'UTR of CBX7 and was identified as the miR-19a-3p target gene. CBX7 is known as a tumor suppressor. This review initially describes the regulation of mir-19a in multiple cancers. Accordingly, the roles of miR-19 in affecting its target gene expression CBX7 in carcinoma also be discussed.

Epigenetic and metabolic reprogramming in inflammatory bowel diseases: diagnostic and prognostic biomarkers in colorectal cancer

BACKGROUND AND AIM

"Inflammatory bowel disease" (IBD) is a chronic, relapsing inflammatory disease of the intestinal tract that typically begins at a young age and might transit to colorectal cancer (CRC). In this manuscript, we discussed the epigenetic and metabolic change to present a extensive view of IBDs transition to CRC. This study discusses the possible biomarkers for evaluating the condition of IBDs patients, especially before the transition to CRC.

RESEARCH APPROACH

We searched "PubMed" and "Google Scholar" using the keywords from 2000 to 2022.

DISCUSSION

In this manuscript, interesting titles associated with IBD and CRC are discussed to present a broad view regarding the epigenetic and metabolic reprogramming and the biomarkers.

CONCLUSION

Epigenetics can be the main reason in IBD transition to CRC, and Hypermethylation of several genes, such as VIM, OSM4, SEPT9, GATA4 and GATA5, NDRG4, BMP3, ITGA4 and plus hypomethylation of LINE1 can be used in IBD and CRC management. Epigenetic, metabolisms and microbiome-derived biomarkers, such as Linoleic acid and 12 hydroxy 8,10-octadecadienoic acid, Serum M2-pyruvate kinase and Six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK and ADCY5) expression are valuable biomarkers for early detection and transition to CRC condition. Some miRs, such as miR-31, miR-139-5p, miR -155, miR-17, miR-223, miR-370-3p, miR-31, miR -106a, miR -135b and miR-320 can be used as biomarkers to estimate IBD transition to CRC condition.

A novel enterocyte-related 4-gene signature for predicting prognosis in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a fatal disease, and its cases are constantly increasing worldwide. Further, the therapeutic and management strategies for patients with COAD are still unsatisfactory due to the lack of accurate patient classification and prognostic models. Therefore, our study aims to identify prognostic markers in patients with COAD and construct a cell subtype-specific prognostic model with high accuracy and robustness.

Methods

Single-cell transcriptomic data of six samples were retrieved from the Gene expression omnibus (GEO) database. The cluster annotation and cell-cell communication analysis identified enterocytes as a key player mediating signal communication networks. A four-gene signature prognostic model was constructed based on the enterocyte-related differentially expressed genes (ERDEGs) in patients with COAD of the Cancer Genome Atlas cohort. The prognostic model was validated on three external validation cohorts from the GEO database. The correlation between immune cell infiltration, immunotherapy response, drug sensitivity, and the four-gene signature prognostic model was investigated. Finally, immunohistochemistry (IHC) was performed to determine the expression of the four genes.

Results

We found that the proportion of epithelial cells was obviously large in COAD samples. Further analysis of epithelial cells showed that the activity of the enterocytes was highest in the cell-cell communication network. Based on enterocyte data, 30 ERDEGs were identified and a 4-gene prognostic model including CPM, CLCA4, ELOVL6, and ATP2A3 was developed and validated. The risk score derived from this model was considered as an independent variable factor to predict overall survival. The patients were divided into high- and low-risk groups based on the median riskscore value. The correlation between immune cell infiltration, immunotherapy response, immune status, clinical characteristics, drug sensitivity, and risk score was analyzed. IHC confirmed the expression of signature genes in tissues from normal individuals, patients with polyps, and COAD.

Conclusion

In this study, we constructed and validated a novel four-gene signature prognostic model, which could effectively predict the response to immunotherapy and overall survival in patients with COAD. More importantly, this model provides useful insight into the management of colon cancer patients and aids in designing personalized therapy.