Dataset Information

A novel immune-related RNA-binding proteins signature to predict survival and therapeutic responses in prostate cancer.

ABSTRACT:

Background

The present study aims to identify immune-related RBPs signature to predict prognosis and therapy response in prostate cancer.

Methods

Differentially expressed RBPs were compared and visualized using R packages. Immune-related RBPs were selected by Pearson correlation analysis. The prognostic immune-related RBPs were identified using the Kaplan-Meier method and LASSO regression. A multivariable Cox regression model was used to construct immune-related RBPs signature.

Results

We constructed a prognostic predictive risk model of prostate cancer containing ten immune-related RBP genes. We found that high-risk prostate cancer patients presented poorer prognosis, higher tumor immune cell infiltration, higher rates of genomic alterations, and were more sensitive to targeted and immunotherapy than the low-risk group.

Conclusions

The immune-related RBPs' signature is an independent prognostic marker that could help screen patients with advanced prostate cancer who are better suited for targeted and immunotherapy.

SUBMITTER: Wang Z

PROVIDER: S-EPMC8830953 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

A novel immune-related RNA-binding proteins signature to predict survival and therapeutic responses in prostate cancer.

Wang Zicheng Z Zhang Yifei Y Jiang Changqin C Gong Binbin B

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico 20220210 7

<h4>Background</h4>The present study aims to identify immune-related RBPs signature to predict prognosis and therapy response in prostate cancer.<h4>Methods</h4>Differentially expressed RBPs were compared and visualized using R packages. Immune-related RBPs were selected by Pearson correlation analysis. The prognostic immune-related RBPs were identified using the Kaplan-Meier method and LASSO regression. A multivariable Cox regression model was used to construct immune-related RBPs signature.<h4 ...[more]

PMID: 35143037

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Project description:PurposeThis study aimed to construct a prognostic signature consisting of immune-related RNA-binding proteins (RBPs) to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) effectively.MethodsThe transcriptome and clinical data of HNSCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. First, we ascertained the immunological differences in HNSCC, through single-sample gene set enrichment analysis, stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) deconvolution algorithm. Then we used univariate proportional hazards (Cox) regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to screen immune-related RBPs and acquire the risk score of each sample. Subsequently, we further investigated the difference in prognosis, immune status, and tumor mutation burden in high- and low-risk groups. Finally, the efficacy of immunotherapy was measured by the tumor immune dysfunction and exclusion (TIDE) score.ResultsWe derived 15 immune-related RBPs, including FRMD4A, ASNS, RAB11FIP1, FAM120C, CFLAR, CTTN, PLEKHO1, SELENBP1, CHCHD2, NPM3, ATP2A3, CFDP1, IGF2BP2, NQO1, and DENND2D. There were significant differences in the prognoses of patients in the high- and low-risk groups in the training set (p < 0.001) and the validation set (p < 0.01). Furthermore, there were statistical differences between the high-risk group and low-risk group in immune cell infiltration and pathway and tumor mutation load (p < 0.001). In the end, we found that patients in the low-risk group were more sensitive to immunotherapy (p < 0.001), and then we screened 14 small-molecule chemotherapeutics with higher sensitivity to the high-risk group (p < 0.001).ConclusionThe study constructed a prognostic signature of HNSCC, which might guide clinical immunotherapy in the future.

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Dataset Information

A novel immune-related RNA-binding proteins signature to predict survival and therapeutic responses in prostate cancer.

Background

Methods

Results

Conclusions

Publications

A novel immune-related RNA-binding proteins signature to predict survival and therapeutic responses in prostate cancer.

Similar Datasets

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