Dataset Information

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer.

ABSTRACT:

Background

To rummage autophagy-related prognostic, diagnostic, and therapeutic biomarkers in cervical cancer (CC).

Methods

The RNA-sequence and clinical information were from the TCGA and GTEx databases. We operated Cox regression to determine signatures related to overall survival (OS) and recurrence-free survival (RFS) respectively. The diagnostic and therapeutic effectiveness of prognostic biomarkers were further explored.

Results

We identified nine (VAMP7, MTMR14, ATG4D, KLHL24, TP73, NAMPT, CD46, HGS, ATG4C) and three risk signatures (SERPINA1, HSPB8, SUPT20H) with prognostic values for OS and RFS respectively. Six risk signatures (ATG4C, ATG4D, CD46, TP73, SERPINA1, HSPB8) were selected for qPCR. We screened five prognostic signatures(ATG4C, CD46, HSPB8, MTMR14, NAMPT) with diagnostic function through the GEO database. Correlation between our models and treatment targets certificated the prognostic score provided a reference for precision medicine.

Conclusions

We constructed OS and RFS prognostic models in CC. Autophagy-related risk signatures might serve as diagnostic and therapeutic biomarkers.

SUBMITTER: Meng D

PROVIDER: S-EPMC8268251 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer.

Meng Dan D Jin Hua H Zhang Xing X Yan Wenjing W Xia Qianqian Q Shen Siyuan S Xie Shuqian S Cui Mengjing M Ding Bo B Gu Yun Y Wang Shizhi S

Cancer cell international 20210708 1

<h4>Background</h4>To rummage autophagy-related prognostic, diagnostic, and therapeutic biomarkers in cervical cancer (CC).<h4>Methods</h4>The RNA-sequence and clinical information were from the TCGA and GTEx databases. We operated Cox regression to determine signatures related to overall survival (OS) and recurrence-free survival (RFS) respectively. The diagnostic and therapeutic effectiveness of prognostic biomarkers were further explored.<h4>Results</h4>We identified nine (VAMP7, MTMR14, ATG4 ...[more]

PMID: 34238288

Similar Datasets

Project description:Despite recent advances in surgical and multimodal therapies, the overall survival (OS) of advanced colorectal cancer (CRC) patients remains low. Thus, discerning sensitive prognostic biomarkers to give the optimistic treatment for CRC patients is extremely critical. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play an important role in CRC progression. Nonetheless, few studies have focused on the impact of m6A-related lncRNAs on the prognosis, tumor microenvironment (TME) and treatment of CRC. In this study, 1707 m6A-related lncRNAs were identified through Pearson correlation analysis and Weighted co-expression network analysis (WGCNA) using The Cancer Genome Atlas (TCGA) cohort. Then, 28 m6A-related prognostic lncRNAs were screened by univariate Cox regression analysis, followed by identifying two clusters by consensus clustering analysis. A prognostic model consisted of 8 lncRNA signatures was constructed by the least absolute shrinkage and selection operator (LASSO). Kaplan-Meier curve analysis and a nomogram were performed to investigate the prognostic ability of this model. The risk score of prognostic model act as an independent risk factor for OS rate. Functional enrichment analysis indicated that lncRNA signatures related tumor immunity. The low-risk group characterized by increased microsatellite instability-high (MSI-H), mutation burden, and immunity activation, indicated favorable odds of OS. Moreover, the lncRNA signatures were significantly associated with the cancer stem cell (CSC) index and drug sensitivity. In addition, 3 common immune genes shared by the lncRNA signatures were screened out. We found that these immune genes were widely distributed in 2 cell types of TME. Finally, a ceRNA network was constructed to identify ZEB1-AS1 regulatory axis in CRC. We found that ZEB1-AS1 was significantly overexpressed in tumor tissues, and was related to the metastasis of EMT and the chemoresistance of 5-Fu in CRC. Therefore, our study demonstrated the important role of m6A-related lncRNAs in TME remodeling. Moreover, these results illustrated the levels of ZEB1-AS1 might be valuable for predicting the progression and prognosis of CRC, and further provided a new target for the diagnosis and treatment of CRC patients.

Dataset Information

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer.

Background

Methods

Results

Conclusions

Publications

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets