A comprehensive analysis of tumor microenvironment-related genes in colon cancer

Correlation evaluation between cancer microenvironment related genes and prognosis based on intelligent medical internet of things

The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients' symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.

Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer. Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential. Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups. Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer.

Comprehensive Analysis of Microsatellite-Related Transcriptomic Signature and Identify Its Clinical Value in Colon Cancer

Background

Microsatellite has been proved to be an important prognostic factor and a treatment reference in colon cancer. The transcriptome profile and tumor microenvironment of different microsatellite statuses are different. Metastatic colon cancer patients with microsatellite instability-high (MSI-H) are sensitive to immune checkpoint inhibitors (ICIs), but not fluorouracil. Efforts have been devoted to identify the predictive factors of immunotherapy.

Methods

We analyzed the transcriptome profile of different microsatellite statuses in colon cancer by using single-cell and bulk transcriptome data from publicly available databases. The immune cells in the tumor microenvironment were analyzed by the ESTIMATION algorithm. The microsatellite-related gene signature (MSRS) was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression based on the differentially expressed genes (DEGs) and its prognostic value and predictive value of response to immunotherapy were assessed. The prognostic value of the MSRS was also validated in another cohort.

Results

The MSI-H cancers cells were clustered differentially in the dimension reduction plot. Most of the immune cells have a higher proportion in the tumor immune microenvironment, except for CD56 bright natural killer cells. A total of 238 DEGs were identified. Based on the 238 DEGs, a neural network was constructed with a Kappa coefficient of 0.706 in the testing cohort. The MSRS is a favorable prognostic factor of overall survival, which was also validated in another cohort (GSE39582). Besides, MSRS is correlated with tumor mutation burden in MSI-H colon cancer. However, the MSRS is a barely satisfactory factor in predicting immunotherapy with the area under the curve (AUC) of 0.624.

Conclusion

We developed the MSRS, which is a robust prognostic factor of overall survival in spite of a barely satisfactory immunotherapy predictor. Further studies may need to improve the predictive ability.

Establishment and verification of a prognostic tumor microenvironment-based and immune-related gene signature in colon cancer

Background

Gastrointestinal malignant cancers affect many sites in the intestinal tract, including the colon. In this study, we purposed to improve prognostic predictions for colon cancer (CC) patients by establishing a novel biosignature of immune-related genes (IRGs) based on the tumor microenvironment (TME).

Methods

Using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, we calculated the stromal and immune scores of every CC patient extracted from The Cancer Genome Atlas (TCGA). We then identified 4 immune-related messenger RNA (mRNA) biosignatures through a Cox and least absolute shrinkage and selection operator (LASSO) univariate analysis, and a Cox multivariate analysis. Relationships between tumor immune infiltration and the risk score were evaluated through the CIBERSORT algorithm and Tumor Immune Estimation Resource (TIMER) database.

Results

Our studies showed that individuals who had a high immune score (P=0.017) and low stromal score (P=0.041) had a favorable overall survival (OS) rate. By comparing high/low scores cohort, 220 differentially expressed genes (DEGs) were determined. Then an immune-related four-mRNA biosignature, including PDIA2, NAFTC1, VEGFC, and CD1B was identified. Kaplan-Meier, calibration, and receiver operating characteristic (ROC) curves verified the model's performance. By using univariate and multivariate Cox analyses, we found each biosignature was an independent risk factor for assessing a CC patient's survival. Three external GEO cohorts validated its good efficiency in estimating OS among individuals with CC. Moreover, the signature was also related to infiltration of several cells of the immune system in the tumor microenvironment.

Conclusions

The resultant model in our study included 4 IRGs associated with the TME. These IRGs can be utilized as an auxiliary variable to estimate and help improve the prognosis of individuals with CC.