Dataset Information

A new TGF-β risk score predicts clinical and immune landscape in colorectal cancer patients.

ABSTRACT:

Background

Aberrant TGF-β signaling pathway can lead to invasive phenotype of colorectal cancer (CRC), resulting in poor prognosis. It is pivotal to develop an effective prognostic factor on the basis of TGF-β-related genes to accurately identify risk of CRC patients.

Methods

We performed differential analysis of TGF-β-related genes in CRC patients from databases and previous literature to obtain TGF-β-related differentially expressed genes (TRDEGs). LASSO-Cox regression was utilized to build a CRC prognostic feature model based on TRDEGs. The model was validated using two GEO validation sets. Wilcoxon rank-sum test was utilized to test correlation of model with clinical factors. ESTIMATE algorithm and ssGSEA and tumor mutation burden (TMB) analysis were used to analyze immune landscape and mutation burden of high-risk (HR) and low-risk (LR) groups. CellMiner database was utilized to identify therapeutic drugs with high sensitivity to the feature genes.

Results

We established a six-gene risk prognostic model with good predictive accuracy, which independently predicted CRC patients' prognoses. The HR group was more likely to experience immunotherapy benefits due to higher immune infiltration and TMB. The feature gene TGFB2 could inhibit the efficacy of drugs such as XAV-939, Staurosporine, and Dasatinib, but promote the efficacy of drugs such as CUDC-305 and by-product of CUDC-305. Similarly, RBL1 could inhibit the drug action of Fluphenazine and Imiquimod but promote that of Irofulven.

Conclusion

A CRC risk prognostic signature was developed on basis of TGF-β-related genes, which provides a reference for risk and further therapeutic selection of CRC patients.

SUBMITTER: Tang B

PROVIDER: S-EPMC11368510 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Publications

A new TGF-β risk score predicts clinical and immune landscape in colorectal cancer patients.

Tang Bing B Liu Binggang B Zeng Zhiyao Z

Annals of gastroenterological surgery 20240521 5

<h4>Background</h4>Aberrant TGF-β signaling pathway can lead to invasive phenotype of colorectal cancer (CRC), resulting in poor prognosis. It is pivotal to develop an effective prognostic factor on the basis of TGF-β-related genes to accurately identify risk of CRC patients.<h4>Methods</h4>We performed differential analysis of TGF-β-related genes in CRC patients from databases and previous literature to obtain TGF-β-related differentially expressed genes (TRDEGs). LASSO-Cox regression was utili ...[more]

PMID: 39229560

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Project description:BACKGROUND:Currently, the measurement of immune cells in previous studies is usually subjective, and no immune-based prognostic model has been established for chordoma. In this study, we sought to simultaneously measure tumor-infiltrating lymphocyte (TIL) subtypes in chordoma samples using an objective method and develop an immune risk score (IRS) model for survival prediction. METHODS:Multiplexed quantitative immunofluorescence staining was used to determine the TIL levels in the tumoral and stromal subareas of 114 spinal chordoma specimens (54 in the training and 60 in the validation cohort) for programmed death-1 (PD-1), CD3, CD8, CD20 (where CD is cluster of differentiation), and FOXP3. Flow cytometry was performed to validate the immunofluorescence assay for lymphocyte measurement on an additional five fresh chordoma specimens. Subsequently, the IRS model was built using the least absolute shrinkage and selection operator (LASSO) Cox regression method. RESULTS:Flow cytometry and quantitative immunofluorescence showed similar lymphocytic percentages and TIL subpopulation proportions in the fresh tumor specimens. With the training data, the LASSO model identified four immune features for IRS construction: tumoral FOXP3, tumoral PD-1, stromal FOXP3, and stromal CD8. In both cohorts, a high IRS was significantly associated with tumoral programmed cell death-1 ligand 1 expression, Enneking inappropriate tumor resection, and surrounding muscle invasion by tumor. Multivariate Cox regression and stratified analysis in the two cohorts revealed that the IRS was an independent predictor and could effectively separate patients with similar Enneking staging into different risk subgroups, with significantly different survival rates. Further receiver operating characteristic analysis found that the IRS classifier had a better prognostic value than the traditional clinicopathological factors and compensated for the deficiency of Enneking staging for outcome prediction. More importantly, a nomogram based on the IRS and clinical predictors showed adequate performance in estimating disease recurrence and survival of patients. CONCLUSIONS:These data support the use of the IRS signature as a reliable prognostic tool in spinal chordoma and may facilitate individualized therapy decision making for patients.

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Dataset Information

A new TGF-β risk score predicts clinical and immune landscape in colorectal cancer patients.

Background

Methods

Results

Conclusion

Publications

A new TGF-β risk score predicts clinical and immune landscape in colorectal cancer patients.

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