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Immune infiltration and immune gene signature predict the response to fluoropyrimidine-based chemotherapy in colorectal cancer patients.

ABSTRACT: Fluoropyrimidine-based chemotherapy is an essential component of systemic chemotherapy for colorectal cancer (CRC). The immune response is implicated in chemotherapy-induced cytotoxicity. Here, we reported an immune risk (Imm-R) model for prognostic prediction in patients receiving fluoropyrimidine-based chemotherapy. Gene expression profiles and corresponding clinical information were collected from four data sets and divided into training set (n = 183) and validation set (validation set1: n = 34; validation set2: n = 99). The composition of 22 tumor-infiltrating immune cells (TIICs) populations was characterized with the CIBERSORT deconvolution algorithm. A prognostic Imm-R model for predicting overall survival was established by performing least absolute shrinkage and selection operator (LASSO) penalized COX regression analysis. T follicular helper cells and M0 macrophages were associated with better survival, while eosinophils were associated with worse survival. TIICs signature was constructed based on the above three immune cell types. Furthermore, a Imm-R model was created by integrating TIICs signature with immune-related genes (IRGs), which effectively in distinguishing CRC patients with poorer prognosis. The Imm-R model was associated with activation of the TGF-beta signaling and suppression of DNA damage. Results of this research provide new insights into the role of immunity for in fluoropyrimidine-based chemotherapy as well as a useful tools to predict the outcome of CRC patients receiving fluoropyrimidine-based chemotherapy.

SUBMITTER: Mo X

PROVIDER: S-EPMC7575007 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTumor-infiltrating immune cells (TIICs) are associated with chemotherapy response. This study aimed to explore the prognostic value of a TIIC-related tumor microenvironment score (TMEscore) in patients with colorectal cancer (CRC) who underwent chemotherapy and construct a TMEscore-related gene signature to determine its predictive value.MethodsGene profiles of patients who underwent fluoropyrimidine-based chemotherapy were collected, and their TIIC fractions were calculated and clustered. Differentially expressed genes (DEGs) between clusters were used to calculate the TMEscore. The association between the TMEscore, chemotherapy response, and survival rate was analyzed. Machine learning methods were used to identify key TMEscore-related genes, and a gene signature was constructed to verify the predictive value.ResultsTwo clusters based on the TIIC fraction were identified, and the TMEscore was calculated based on the DEGs of the two clusters. The TMEscore was higher in patients who responded to chemotherapy than in those who did not, and was associated with the survival rate of patients who underwent chemotherapy. Three machine learning methods, support vector machine (SVM), decision tree (DT), and Extreme Gradient Boosting (XGBoost), identified three TMEscore-related genes (ADH1C, SLC26A2, and NANS) associated with the response to chemotherapy. A TMEscore-related gene signature was constructed, and three external cohorts validated that the gene signature could predict the response to chemotherapy. Five datasets and clinical samples showed that the expression of the three TMEscore-related genes was increased in tumor tissues compared to those in control tissues.ConclusionsThe TIIC-based TMEscore was associated with the survival of CRC patients who underwent fluoropyrimidine-based chemotherapy, and predicted the response to chemotherapy. The TMEscore-related gene signature had a better predictive value for response to chemotherapy than for survival.

Project description:BackgroundInflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature.MethodsThe inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan-Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry.ResultsThe IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients' samples revealed the same result.ConclusionThe novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.

Project description:The benefit of adjuvant chemotherapy in the early stages of colorectal cancer (CRC) is still disappointing and the prediction of treatment outcome quite difficult. Recently, through a transcriptomic approach, we evidenced a role of PNN and KCNQ1OT1 gene expression in predicting response to fluoropyrimidine-based adjuvant chemotherapy in stage III CRC patients. Thus, the aim of this study was to validate in an independent cohort of stages IIIII CRC patients our previous findings. PNN and KCNQ1OT1 mRNA expression levels were evaluated in 74 formalin-fixed paraffin-embedded tumor and matched normal mucosa samples obtained by stages IIIII CRC patients treated with fluoropyrimidine-based adjuvant chemotherapy. PININ, the protein encoded by PNN, was immunohistochemically evaluated in 15 tumor and corresponding normal mucosa samples, selected on the basis of a low, medium, or high mRNA expression tumor/mucosa ratio. PNN and KCNQ1OT1 mRNA mean expression levels were significantly higher in tumor compared with normal tissues. Patients with high PNN or KCNQ1OT1 tumor mRNA levels according to ROC-based cutoffs showed a shorter disease-free survival (DFS) compared with patients with low tumor mRNA gene expression. Also, patients with tumor mRNA expression values of both genes below the identified cutoffs had a significantly longer DFS compared with patients with the expression of one or both genes above the cutoffs. In a representative large cohort of stages IIIII CRC untreated patients retrieved from GEO datasets, no difference in DFS was observed between patients with high and low PNN or KCNQ1OT1 gene expression levels. These data confirm our previous findings and underscore the relevance of PNN and KCNQ1OT1 expression in predicting DFS in early stages of CRC treated with fluoropyrimidine-based adjuvant chemotherapy. If further validated in a prospective case series, both biomarkers could be used to identify patients who benefit from this treatment and to offer alternative chemotherapy regimens to potential unresponsive patients. In relation to the suggested biological role of PNN and KCNQ1OT1 in CRC, they might also be exploited as potential therapeutic targets.

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Immune infiltration and immune gene signature predict the response to fluoropyrimidine-based chemotherapy in colorectal cancer patients.

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