To explore the prognostic characteristics of colon cancer based on tertiary lymphoid structure-related genes and reveal the characteristics of tumor microenvironment and drug prediction

Abstract

In order to construct a prognostic evaluation model of TLS features in COAD and better realize personalized precision medicine in COAD. Colon adenocarcinoma (COAD) is a common malignant tumor of the digestive system. At present, there is no effective prognostic marker to predict the prognosis of patients. Tertiary lymphoid structure (TLS) affects cancer progression by regulating immune microenvironment. Mining COAD biomarkers based on TLS-related genes helps to improve the prognosis of patients. In order to construct a prognostic evaluation model of TLS features in COAD and better realize personalized precision medicine in COAD. The mRNA expression data and clinical information of COAD and adjacent tissues were downloaded from the Cancer Genome Atlas database. The differentially expressed TLS-related genes of COAD relative to adjacent tissues were obtained by differential analysis. TLS gene co-expression analysis was used to mine genes highly related to TLS, and the intersection of the two was used to obtain candidate genes. Univariate, LASSO, and multivariate Cox regression analysis were performed on candidate genes to screen prognostic markers to construct a risk assessment model. The differences of immune characteristics were evaluated by ESTIMATE, ssGSEA and CIBERSORT in high and low risk groups of prognostic model. The difference of genomic mutation between groups was evaluated by tumor mutation burden score. Screening small molecule drugs through the GDSC library. Finally, a nomogram was drawn to evaluate the clinical value of the prognostic model. Seven TLS-related genes ADAM8, SLC6A1, PAXX, RIMKLB, PTH1R, CD1B, and MMP10 were screened to construct a prognostic model. Survival analysis showed that patients in the high-risk group had significantly lower overall survival rates. Immune microenvironment analysis showed that patients in the high-risk group had higher immune indicators, indicating higher immunity. The genomic mutation patterns of the high-risk and low-risk groups were significantly different, especially the KRAS mutation frequency was significantly higher in the high-risk group. Drug sensitivity analysis showed that the low-risk group was more sensitive to Erlotinib, Savolitinib and VE _ 822, which may be used as a potential drug for COAD treatment. Finally, the nomogram constructed by pathological features combined with RiskScore can accurately evaluate the prognosis of COAD patients. This study constructed and verified a TLS model that can predict COAD. More importantly, it provides a reference standard for guiding the prognosis and immunotherapy of COAD patients.

Keywords: Colon adenocarcinoma; Drug sensitivity; Immune; Prognosis; Tertiary lymphoid structures; Tumor microenvironment.

Figure 1

Flowchart of this study.

Figure 2

Construction and verification of colon cancer prognosis model. (A) Based on the intersection of differentially expressed genes and TLS highly related genes, candidate genes were screened. (B) Screening the best penalty parameter (λ) of lasso cox regression model. (C) lasso cox regression analysis of gene coefficient spectrum. (D) Multi-factor cox regression analysis forest map. (E) 7 characteristic gene expression thermograms of prognostic models between high and low risk groups. (F) RiskScore distribution map of patients. (G) Survival state distribution map. (H) PCA distribution map of the characteristic genes of the high and low risk group model. (I) Survival analysis of high and low risk group samples. (J) ROC curve of TCGA training set samples. (K) ROC curve of validation set samples.

Figure 3

Prognostic model to evaluate the reliability of COAD immune components. (A) Enrichment difference heat map of immune components and immune cells between high and low risk groups. The immune score (B), matrix score (C), ESTIMATE (D) score and tumor purity (E) were differentially expressed in the high and low risk groups. (F) CIBERSORT visualized the infiltration difference of immune cells between high and low risk groups.

Figure 4

Analysis of genomic mutation frequency in high and low risk groups. (A) Statistics of high-frequency mutation genes, mutation sites, and mutation types in the low-risk group. (B) Statistical map of high frequency mutation genes, mutation sites and mutation types in high risk group. (C) The waterfall diagram of the mutation frequency top20 gene in the low-risk group. (D) Waterfall map of top20 gene mutation frequency in high-risk group. (E) Co-mutation and mutually exclusive mutation map of top20 gene in low-risk group. (F) The co-mutation and mutually exclusive mutation of the top20 gene in the high-risk group.

Figure 5

Difference evaluation of small molecule drug sensitivity of samples. (A) Violin plot showed that the IC50 of Erlotinib in the treatment of CC was different in the high and low risk groups. A violin plot (B) showed the difference in IC50 of CC treated with Savolitinib between the high-and low-risk groups. (C) Violin plot showed the difference of IC50 of drug VE _ 822 treated CC in high and low risk groups.

Figure.6

Column diagram construction and evaluation. (A,B): univariate Cox regression analysis (A) and multivariate Cox regression analysis (B) forest plot of clinical traits, risk score and OS. (C) Nomogram for predicting 1-, 3-, and 5-year OS in CC patients by combining 7-feature gene risk score and other clinical factors. (D–F): The nomogram was used to predict the correction curve of 1-year (D), 3-year (E), and 5-year (F) survival rates.