The Generation and Validation of a 20-Genes Model Influencing the Prognosis of Colorectal Cancer

Prognostic Risk Model of Immune-Related Genes in Colorectal Cancer

Purpose

We focused on immune-related genes (IRGs) derived from transcriptomic studies, which had the potential to stratify patients’ prognosis and to establish a risk assessment model in colorectal cancer.

Summary

This article examined our understanding of the molecular pathways associated with intratumoral immune response, which represented a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of colorectal cancer. More and more evidence shows that IRGs play an important role in tumors. We have used data analysis to screen and identify immune-related molecular biomarkers of colon cancer. We selected 18 immune-related prognostic genes and established models to assess prognostic risks of patients, which can provide recommendations for clinical treatment and follow-up. Colorectal cancer (CRC) is a leading cause of cancer-related death in human. Several studies have investigated whether IRGs and tumor immune microenvironment (TIME) could be indicators of CRC prognoses. This study aimed to develop an improved prognostic signature for CRC based on IRGs to predict overall survival (OS) and provide new therapeutic targets for CRC treatment. Based on the screened IRGs, the Cox regression model was used to build a prediction model based on 18-IRG signature. Cox regression analysis revealed that the 18-IRG signature was an independent prognostic factor for OS in CRC patients. Then, we used the TIMER online database to explore the relationship between the risk scoring model and the infiltration of immune cells, and the results showed that the risk model can reflect the state of TIME to a certain extent. In short, an 18-IRG prognostic signature for predicting CRC patients’ survival was firmly established.

Identification of candidate biomarkers and therapeutic drugs of colorectal cancer by integrated bioinformatics analysis

Most colorectal cancer (CRC) patients are diagnosed with advanced stages and low prognosis. We aimed to identify potential diagnostic and prognostic biomarkers, as well as active small molecules of CRC. Microarray data (GSE9348, GSE35279, and GSE106582) were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by the GEO2R platform. Common DEGs were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Cytoscape software was used to construct protein-protein interaction networks and identify hub genes. Hub genes were evaluated by Kaplan-Meier survival analysis in the GEPIA database and validated in two independent microarray data (GSE74602 and GSE83889). Common DEGs were used to select active small molecules by the connectivity map database. A total of 166 DEGs were identified as common DEGs. GO analysis demonstrated that common DEGs were significantly enriched in the apoptotic process, cell proliferation, and cell adhesion. KEGG analysis indicated that the most enriched pathways were the PI3K-Akt signaling pathway and extracellular matrix-receptor interaction. COL1A2, THBS2, TIMP1, and CXCL8 significantly upregulated in colorectal tumor. High expressions of COL1A2, THBS2, and TIMP1 were associated with poor survival, while high expressions of CXCL8 were associated with better survival. We selected 11 small molecules for CRC therapy. In conclusion, we found key dysregulated genes associated with CRC and potential small molecules to reverse them. COL1A2, THBS2, TIMP1, and CXCL8 may act as diagnostic and prognostic biomarkers of CRC.

Integrated analysis of competing endogenous RNA networks revealing five prognostic biomarkers associated with colorectal cancer

Colorectal cancer (CRC) is a common malignant tumor with high morbidity and mortality around the world. The aim of this study was to determine the genes significantly associated with the overall survival (OS) of CRC patients and predict their function in the competing endogenous RNA (ceRNA) regulation networks. We constructed the lncRNA-miRNA-mRNA networks according to the differentially expressed RNAs from the The Cancer Genome Atlas data sets of 561 CRC patients. Twelve differentially expressed messenger RNAs from the ceRNA networks were selected through a univariate Cox proportional hazards regression. Then, these genes were analyzed by using multivariate Cox proportional hazards stepwise regression to construct a prognostic model in which five genes (tensin 1, clusterin, proteolipid protein 1, epiregulin, and transcription factor Spi-B) were included. The Kaplan-Meier risk survival analysis showed that this five-gene signature independently predicted a 5-year overall survival in CRC patients (P < 0.001). Furthermore, significance was verified according to two irrelevant Gene Expression Omnibus (GEO) data sets (GSE38832 and GSE39582). The verified five-gene model of CRC can be used to predict patient prognosis and will inform postoperational evaluation and follow-up strategies.

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers.

Materials and methods

One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein–protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored.

Results

Ninety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein–protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1, TXNRD1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states.

Conclusion

This study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.

Comparative analysis of immune function, hemorheological alterations and prognosis in colorectal cancer patients with different traditional Chinese medicine syndromes

OBJECTIVE

The present study investigates the differences in immune function, hemorheological alterations and prognostic evaluation in colorectal cancer (CRC) patients with different traditional Chinese medicine (TCM) syndromes.

METHODS

A total of 128 patients, diagnosed as stage II and III of CRC, were recruited. They were assigned into three TCM syndromes: deficiency syndrome, excess syndrome, and syndrome of intermingled deficiency and excess, and another 53 healthy individuals were selected as the control. Flow cytometry was used to determine the peripheral blood lymphocyte subsets (the levels of CD+3, CD+4, CD+8, NK cells, and the ratios of CD+4/CD+8, Th1/Th2 and Tc1/Tc2). Whole blood viscosity (WBV), plasma viscosity (PV), hematocrit (Hct), erythrocyte sedimentation rate (ESR), plasma fibrinogen concentration (PFC) were measured using a fully-automatic blood rheological instrument. The univariate analysis and Cox regression analysis were conducted to evaluate the prognosis of CRC patients with different TCM syndromes.

RESULTS

Compared with healthy individuals, CRC patients with three different syndromes had lower levels of CD+3, CD+4, NK cells, and ratios of CD+4/CD+8, Th1/Th2 and Tc1/Tc2, but higher level of CD+8, WBV, PV, Hct, ESR and PFC. Besides, patients with excess syndrome showed the highest levels of CD3+, CD4+ and NK cells, and ratios of CD+4/CD+8, Th1/Th2 and Tc1/Tc2, but the lowest level of CD+8 among three syndromes, and those with deficiency syndrome showed an opposite trend. Compared with patients with excess syndrome, those with deficiency syndrome showed decreased WBV, PV, Hct, ESR and PFC. The pathological type, surgical approach, tumor node metastasis (TNM) stage, liver metastasis, TCM treatment time and different TCM syndromes were independent factors of prognostic survival in CRC patients except perioperative blood transfusion volume.

CONCLUSIONS

Taken together, we conclude that patients with TCM deficiency syndrome has lower immune function and poorer prognosis while patients with TCM excess syndrome has higher immune function and better prognosis of CRC.