Predicting MicroRNA Target Genes and Identifying Hub Genes in IIA Stage Colon Cancer Patients Using Bioinformatics Analysis

ceRNA network-regulated COL1A2 high expression correlates with poor prognosis and immune infiltration in colon adenocarcinoma

Collagen type I α 2 (COL1A2) is a major component of collagen type I. Recently, abnormal COL1A2 expression has been reported in human cancers. However, the specific role and mechanism of COL1A2 in colon adenocarcinoma (COAD) remain unclear. We performed the pan-cancer analysis of COL1A2 expression in 33 types of human cancers from TIMER database and integrated data combined TCGA with GTEx. The prognostic values of COL1A2 for 17 cancer types of interest were estimated from GEPIA database. The results showed that COL1A2 was significantly upregulated in COAD tissues and that higher COL1A2 expression predicted unfavorable prognosis for patients with COAD. Next, COL1A2-related functional pathways in COAD were analyzed with TCGA data using R package. Additionally, we constructed a ceRNA network that LINC00638/hsa-miR-552-3p axis served as a potential regulatory pathway of COL1A2 in COAD. Furthermore, our findings showed that COL1A2 positively associated with immune infiltration and that tumor immune escape might be involved in COL1A2-mediated carcinogenesis in COAD. For the first time, we constructed a ceRNA prediction network of COL1A2 and explored the association of COL1A2 with tumor immune microenvironment remodeling. The findings may advance our understanding of the pathogenesis mechanism in COAD and paves the way for further cancer therapeutics.

Pilot proteomic study of locally advanced rectal cancer before and after neoadjuvant chemoradiotherapy indicates high metabolic activity in non-responders' tumor tissue

PURPOSE

In the search for candidate predictive biomarkers to evaluate response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer, only a few studies report proteomic profiles of tumor tissue before and after nCRT. The aim of our study was to determine differentially expressed proteins between responders and non-responders before and after the therapy in order to identify candidate molecules for prediction and follow-up of response to nCRT.

EXPERIMENTAL DESIGN

The study has included tissue sections of rectal tumor and non-tumor mucosa from five responders and five non-responders taken before and after nCRT from patients with locally advanced rectal cancer. Extracted proteins were analyzed by LC-MS/MS analysis followed by a set of bioinformatics analyses.

RESULT

Proteomics analysis provided a mean of approximately 1050 protein identifications per sample. A comparison of proteomic profiles between responders and non-responders has identified 18 differentially expressed proteins. Pathway analysis demonstrated high metabolic activity in non-responders' tumors before nCRT, indicating the presence of intrinsic chemoradioresistance in these subjects. Two proteins associated with poor prognosis in colorectal cancer, ADAM10 and CAD, were identified as candidate predictive biomarkers as they were present in non-responders only.

CONCLUSIONS AND CLINICAL RELEVANCE

Shortlisted proteins from our study should be further validated as candidate biomarkers for response to routinely applied nCRT protocols.

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.

miRNA Profile and Bioinformatic Analysis for Diagnosis in Patients with Stage IIIA Colon Cancer

Early diagnosis is a critical factor in deciding the outcome of colon cancer, as is the case with other types of cancers. Recent scientific developments have enabled the use of biomarkers for diagnosis and for designing treatment strategies for various cancer types. Further, identification of potential targets of these biomarkers will facilitate a better understanding of molecular processes. The aim of this study is to analyze microRNA expression profile, and through bioinformatic analyses determine the cellular processes of potential target genes and understand their molecular mechanism in stage IIIA colon cancer patients. The microRNA expression profiles of both normal and tumor tissues of seven patients were analyzed using the Affymetrix microarray system. The target genes were identified by performing a KEGG pathway analysis on eight miRNAs (hsa-miR-362-3p, hsa-miR-34c-5p, hsa-miR-34c-3p, hsa-miR-34a-3p, hsa-miR-19b-1-3p, hsa-miR-371a-5p, hsa-miR-941 ad hsa-miR-7-5p), which were selected through an array scan by using DIANA-miRPath v.3 bioinformatic analysis tool. Biological pathway and cellular component analyses were performed on 30 genes targeted by miRNAs using FunRich Gene Enrichment tool. These analyses indicated that the genes targeted by these eight miRNAs played a role in either cell communication (53%), signal transduction (60%) or apoptosis (20%) in stage IIIA colon cancer. Taken together, these data suggest that these miRNAs can be used as biomarkers in Stage IIIA colon cancer.

Validation of Novel Prognostic Biomarkers for Early-Stage Clear-Cell, Endometrioid and Mucinous Ovarian Carcinomas Using Immunohistochemistry

Early-stage (I and II) ovarian carcinoma patients generally have good prognosis. Yet, some patients die earlier than expected. Thus, it is important to stratify early-stage patients into risk groups to identify those in need of more aggressive treatment regimens. The prognostic value of 29 histotype-specific biomarkers identified using RNA sequencing was evaluated for early-stage clear-cell (CCC), endometrioid (EC) and mucinous (MC) ovarian carcinomas (n = 112) using immunohistochemistry on tissue microarrays. Biomarkers with prognostic significance were further evaluated in an external ovarian carcinoma data set using the web-based Kaplan-Meier plotter tool. Here, we provide evidence of aberrant protein expression patterns and prognostic significance of 17 novel histotype-specific prognostic biomarkers [10 for CCC (ARPC2, CCT5, GNB1, KCTD10, NUP155, RPL13A, RPL37, SETD3, SMYD2, TRIO), three for EC (CECR1, KIF26B, PIK3CA), and four for MC (CHEK1, FOXM1, KIF23, PARPBP)], suggesting biological heterogeneity within the histotypes. Combined predictive models comprising the protein expression status of the validated CCC, EC and MC biomarkers together with established clinical markers (age, stage, CA125, ploidy) improved the predictive power in comparison with models containing established clinical markers alone, further strengthening the importance of the biomarkers in ovarian carcinoma. Further, even improved predictive powers were demonstrated when combining these models with our previously identified prognostic biomarkers PITHD1 (CCC) and GPR158 (MC). Moreover, the proteins demonstrated improved risk prediction of CCC-, EC-, and MC-associated ovarian carcinoma survival. The novel histotype-specific prognostic biomarkers may not only improve prognostication and patient stratification of early-stage ovarian carcinomas, but may also guide future clinical therapy decisions.