Construction of a novel methylation-related prognostic model for colorectal cancer based on microsatellite status

Comparative analyses of the prognosis, tumor immune microenvironment, and drug treatment response between left-sided and right-sided colon cancer by integrating scRNA-seq and bulk RNA-seq data

BACKGROUND

In this study, we compared the prognosis, tumor immune microenvironment (TIM), and drug treatment response between left-sided (LCC) and right-sided (RCC) colon cancer to predict outcomes in patients with LCC and RCC.

METHODS

Based on identified differentially expressed genes and using single-cell RNA sequencing data, we constructed and validated a prognostic model for LCC and RCC patients in the TCGA-COAD cohort and GSE103479 cohort. Moreover, we compared the differences of TIM characteristics and drug treatment response between LCC and RCC patients.

RESULTS

We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance. The RCC patients with higher risk scores were significantly associated with greater metastasis (P = 2.6×10-5), N stage (P = 0.012), advanced pathological stage (P = 1.4×10-4), and more stable microsatellite status (P = 0.007) but not T stage (P = 0.200). For LCC patients, the risk scores were not significantly associated with tumor stage and microsatellite status (P > 0.05). Additionally, immune infiltration by CD8 and regulatory T cells and M0, M1, and M2 macrophages differed significantly between LCC and RCC patients (P < 0.05). APC and TP53 mutations were significantly more common in LCC patients (P < 0.05). In contrast, KRAS, SYNE1, and MUC16 mutations were significantly more common in RCC patients (P < 0.05). In addition, tumor mutation burden values were significantly higher in RCC patients than in LCC patients (P = 5.9×10-8). Moreover, the expression of immune checkpoint targets was significantly higher in RCC patients than in LCC patients (P < 0.05), indicating that RCC patients maybe more sensitive to immunotherapy. However, LCC and RCC patients did not differ significantly in their sensitivity to eight selected chemicals or target drugs (P > 0.05). The average half-maximal inhibitory concentrations for camptothecin, teniposide, vinorelbine, and mitoxantrone were significantly lower in low-risk than in high-risk RCC patients (P < 0.05), indicating that the lower risk score of RCC patients, the more sensitive they were to these four drugs.

CONCLUSIONS

We investigated the differences in prognosis, TIM, and drug treatment response between LCC and RCC patients, which may contribute to accurate colon cancer prognosis and treatment of colon cancer.

Comprehensive analysis of transient receptor potential channels-related signature for prognosis, tumor immune microenvironment, and treatment response of colorectal cancer

BACKGROUND

Transient receptor potential channels (TRPC) play critical regulatory functions in cancer occurrence and progression. However, knowledge on its role in colorectal cancer (CRC) is limited. In addition, neoadjuvant treatment and immune checkpoint inhibitors (ICIs) have increasing roles in CRC management, but not all patients benefit from them. In this study, a TRPC related signature (TRPCRS) was constructed for prognosis, tumor immune microenvironment (TIME), and treatment response of CRC.

METHODS

Data on CRC gene expression and clinical features were retrospectively collected from TCGA and GEO databases. Twenty-eight TRPC regulators (TRPCR) were retrieved using gene set enrichment analysis. Different TRPCR expression patterns were identified using non-negative matrix factorization for consensus clustering, and a TRPCRS was established using LASSO. The potential value of TRPCRS was assessed using functional enrichment analysis, tumor immune analysis, tumor somatic mutation analysis, and response to preoperative chemoradiotherapy or ICIs. Moreover, an external validation was conducted using rectal cancer samples that received preoperative chemoradiotherapy at Fujian Cancer Hospital (FJCH) via qRT-PCR.

RESULTS

Among 834 CRC samples in the TCGA and meta-GEO cohorts, two TRPCR expression patterns were identified, which were associated with various immune infiltrations. In addition, 266 intersected genes from 5564 differentially expressed genes (DEGs) between two TRPC subtypes, 4605 DEGs between tumor tissue and adjacent non-tumor tissue (all FDR< 0.05, adjusted P< 0.001), and 1329 prognostic related genes (P< 0.05) were identified to establish the TRPCRS, which was confirmed in the TCGA cohort, two cohorts from GEO, and one qRT-PCR cohort from FJCH. According to the current signature, the high-TRPC score group had higher expressions of PD-1, PD-L1, and CTLA4, lower TIDE score, and improved response to anti-PD-1 treatment with better predictive ability. Compared to the high-TRPC score group, the low-TRPC score group comprised an immunosuppressive phenotype with increased infiltration of neutrophils and activated MAPK signaling pathway, but was more sensitive to preoperative chemoradiotherapy and associated with improved prognosis.

CONCLUSIONS

The current TRPCRS predicted the prognosis of CRC, evaluated the TIME in CRC, and anticipated the response to immune therapy and neoadjuvant treatment.

Exploring immune-related signatures for predicting immunotherapeutic responsiveness, prognosis, and diagnosis of patients with colon cancer

The present study focused on identifying the immune-related signatures and exploring their performance in predicting the prognosis, immunotherapeutic responsiveness, and diagnosis of patients with colon cancer. Firstly, the immunotherapeutic response-related differential expressed genes (DEGs) were identified by comparing responders and non-responders from an anti-PD-L1 cohort using the edgeR R package. Then, the immunotherapeutic response related DEGs was intersected with immune-related genes (IRGs) to obtain the immunotherapeutic response and immune-related genes (IRIGs). Then, an immunotherapeutic response and immune-related risk score (IRIRScore) model consisting of 6 IRIGs was constructed using the univariable Cox regression analysis and multivariate Cox regression analysis based on the COAD cohort from the cancer genome atlas (TCGA) database, which was further validated in two independent gene expression omnibus database (GEO) datasets (GSE39582 and GSE17536) and anti-PD-L1 cohort. A nomogram with good accuracy was established based on the immune-related signatures and clinical factors (C-index = 0.75). In the training dataset and GSE39582, higher IRIRScore was significantly associated with higher TMN and advanced pathological stages. Based on the anti-PD-L1 cohort, patients who were sensitive to immunotherapy had significantly lower risk score than non-responders. Furthermore, we explored the immunotherapy-related signatures based on the training dataset. Kaplan-Meier curve revealed a high level of T cells regulatory (Tregs) was significantly related to poor overall survival (OS), while a high level of T cells CD4 memory resting was significantly related to better OS. Besides, the TMB value of patients in the high-risk group was significantly higher than those in a low-risk group. Moreover, patients in the high-risk group had significantly higher expression levels of immune checkpoint inhibitors. In addition, the immune-related signatures were applied to establish prediction models using the random forest algorithm. Among them, TDGF1 and NRG1 revealed excellent diagnostic predictive performance (AUC >0.8). In conclusion, the current findings provide new insights into immune-related immunotherapeutic responsiveness, prognosis, and diagnosis of colon cancer.