Dataset Information

Development of a Novel Immune Subtyping System Expanded with Immune Landscape and an 11-Gene Signature for Predicting Prostate Cancer Survival.

ABSTRACT:

Background

Nearly half of patients with prostate cancer will develop metastasis. Immunotherapy is currently a promising strategy for treating metastatic prostate cancer. This study aimed to construct an immune subtyping system and provide a more comprehensive understanding of tumor microenvironment.

Methods

Data were downloaded from TCGA database and cBioPortal database. Consensus clustering was used to identify immune subtypes. Immune features were scored by ESTIMATE and CIBERSORT. Efficacy of different subtypes in immunotherapy was predicted by TIDE tool. Immune landscape was delineated through "monocle." Coexpressed gene modules were identified by weighted correlation network analysis. Univariate Cox regression analysis and LASSO analysis were applied to construct a prognostic model.

Results

Four immune subtypes (IS1 to IS4) were identified. Prognosis, mutation patterns, expression of immune genes, immune biomarkers, immunohistochemical biomarkers, and prediction efficacy of immunotherapy were significantly different among four immune subtypes. Five coexpressed gene modules were identified and an 11-gene prognostic model was constructed based on the modules.

Conclusions

The study developed a novel immune subtyping system and an 11-gene prognostic model of prostate cancer, which could guide personalized treatment and immunotherapy for patients with prostate cancer.

SUBMITTER: Li N

PROVIDER: S-EPMC8866019 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Development of a Novel Immune Subtyping System Expanded with Immune Landscape and an 11-Gene Signature for Predicting Prostate Cancer Survival.

Li Nan N Yu Kai K Lin Zhong Z Zeng Dingyuan D

Journal of oncology 20220216

<h4>Background</h4>Nearly half of patients with prostate cancer will develop metastasis. Immunotherapy is currently a promising strategy for treating metastatic prostate cancer. This study aimed to construct an immune subtyping system and provide a more comprehensive understanding of tumor microenvironment.<h4>Methods</h4>Data were downloaded from TCGA database and cBioPortal database. Consensus clustering was used to identify immune subtypes. Immune features were scored by ESTIMATE and CIBERSOR ...[more]

PMID: 35222640

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Project description:Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients. Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules. Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan-Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups. Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.

Dataset Information

Development of a Novel Immune Subtyping System Expanded with Immune Landscape and an 11-Gene Signature for Predicting Prostate Cancer Survival.

Background

Methods

Results

Conclusions

Publications

Development of a Novel Immune Subtyping System Expanded with Immune Landscape and an 11-Gene Signature for Predicting Prostate Cancer Survival.

Similar Datasets

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