Dataset Information

Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer.

ABSTRACT: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune score and lncRNA expression (p < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (p < 0.05) were identified which demonstrated an ability to stratify patients into the low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group in the training set, valid set, and total set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four-IRL signature in predicting the one-, two-, and three-year survival rates was larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four IRLs in the development of CC were ascertained preliminarily.

SUBMITTER: Zheng J

PROVIDER: S-EPMC7683128 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer.

Zheng Jianfeng J Cao Benben B Zhang Xia X Niu Zheng Z Tong Jinyi J

BioMed research international 20201112

Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune ...[more]

PMID: 33274204

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Project description:Increasing evidence has demonstrated that pyroptosis, a type of inflammatory programmed cell death, plays an important role in the pathogenesis and progression of gastric cancer. However, it remains unclear whether pyroptosis-related long non-coding RNAs (lncRNAs) can be used to predict the diagnosis and prognosis of gastric adenocarcinoma. This study aimed to evaluate and test the role of the lncRNA signature associated with pyroptosis as a prognostic tool for stomach adenocarcinoma (STAD) and to ascertain their immune value. Relative RNA-sequencing data were extracted from The Cancer Genome Atlas database (TCGA), and data preprocessing was performed for STAD. Pearson correlation analysis was used to determine whether lncRNAs were significantly correlated with pyroptosis based on 23 genes related to pyroptosis. Univariate Cox regression and least absolute shrinkage and selection operator(LASSO) analyses were both adopted to select features and establish the pyroptosis-related lncRNA (PRL) prognostic signature. Kaplan-Meier(KM) survival analysis of the different risk groups was conducted according to the risk scores. We further examined the functional enrichment, tumor microenvironment, and landscape of mutation status among the different risk groups, and these analyses further explained the reasons for the differences in the prediction as well as survival value of the different risk groups. Four lncRNAs, including HAND2-AS1, LINC01354, RP11-276H19.1, and PGM5-AS1, were involved in the PRL signature and used to split STAD patients into two risk groups. Overall survival time(OS) was significantly higher in the low-risk group than in the high-risk group in both the training and validation groups. Functional enrichment analysis was further employed to analyze differentially expressed genes in high- and low-risk groups to identify potential molecular functions and pathways associated with pyroptosis in the gastric cancer microenvironment. Protein-protein interaction (PPI) and Friends analysis identified hub genes that may play a key role in differentially expressed genes in high- and low-risk groups. In addition, there were remarkable discrepancies between the different risk groups in the tumor stage (P < 0.01) and histologic grade (P < 0.05). Furthermore, drug-susceptibility testing indicated potential sensitive chemotherapeutic drugs for each risk group. This study is the first to establish and validate STAD-associated PRLs that can effectively guide the prognosis and the immune microenvironment in STAD patients and provide evidence for the development of molecularly targeted therapies related to pyroptosis.

Project description:BackgroundThe incidence of malignant melanoma accounts for only approximately 5% of skin malignant tumors, however, it accounts for 75% of its mortality. Long-chain non-coding RNA (lncRNA) has a wide range of functional activities. Disorders of lncRNAs may lead to the occurrence and development of melanoma, and may also be related to immunotherapy.MethodsThe transcriptomic data of primary and metastatic melanoma patients and 331 immune-related genes were downloaded from skin cutaneous melanoma (SKCM) in the The Cancer Genome Atlas (TCGA) database. On this basis, 460 immunologically relevant lncRNAs were identified by constructing a co-expression network of immunogenic genes and lncRNAs in primary and metastatic melanoma patients. Prognostic genes were screened using univariate Cox regression analysis. ROC analysis was performed to evaluate the robustness of the prognostic signature.ResultsUnivariate correlation analysis showed that only 3 of the 23 immune-related lncRNAs were at high risk and the rest were at low risk. Signatures of 7 immune-related lncRNAs were identified by multivariate correlation analysis. The clinical correlation analysis showed that the 7 immune-related lncRNAs were associated with the clinical stage of primary and metastatic melanoma. Principal component analysis (PCA) showed that only 7 immune-related lncRNA signals divided tumor patients into high-risk and low-risk groups, while the low-risk group was enriched in the immune system process M13664 and immune response M19817 sets. PPI interaction network analysis showed that 11 G protein-coupled receptors and 6 corresponding ligands in the 2 gene sets affected the tumor microenvironment and were negatively related to the risk of the 7 immune-related lncRNAs. The tumor microenvironment immune cell infiltration analysis also supported the finding that anti-tumor immunity in the low-risk group was stronger than in the high-risk group.ConclusionsThese results indicate that characteristics of the 7 immune-related lncRNAs have prognostic value for melanoma patients and can be used as potential immunotherapy targets.

Project description:BACKGROUND Cervical cancer is one of the most common malignances among women globally. This study aimed to construct a novel immune-related signature to predict the prognosis and immune infiltration of cervical cancer. MATERIAL AND METHODS Transcriptomic profiles and corresponding clinical information of cervical cancer patients were obtained from The Cancer Genome Atlas (TCGA) database and GEO database. The hub immune-related genes were screened and selected using Cox regression analysis and LASSO regression analysis. A novel signature was established based on the expression levels and corresponding coefficients of the selected hub immune-related genes. Kaplan-Meier survival curve and ROC curve illustrated the prognostic value of this novel signature in cervical cancer. The predictive accuracy and stability of this novel signature were confirmed in the validation cohort, internal testing set and external testing set. Then, a nomogram was constructed to predict individual survival probability of cervical cancer patient. The association between the risk scores of novel signature and immune infiltration was investigated through single-sample gene set enrichment analysis (ssGSEA). RESULTS Ten hub immune-related genes (TFRC, SPP1, CAMP, CSF2, TUBB3, ZAP70, CHIT1, LEPR, DLL4, and DES) were selected to construct a novel signature. The risk score of this novel signature could be an independent prognostic factor in cervical cancer, which divided patients into high-risk and low-risk groups. The patients in high-risk groups showed significantly worse overall survival rates than those in low-risk groups in all training and validation cohorts (all P<0.05). A nomogram model was constructed based on the risk score of the novel signature and other clinical characteristics, which achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities (concordance index=0.813). Furthermore, gene enrichment analysis revealed that the novel signature was closely related with immunology. The novel signature was negatively correlated with the infiltration of most immune cell types, especially T cell subsets (P<0.001). CONCLUSIONS The novel signature could comprehensively predict the prognosis and immune infiltration of cervical cancer. It may provide new insights for the precise treatment in cervical cancer.

Dataset Information

Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer.

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Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer.

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