Identification of Immune-Related Key Genes in Ovarian Cancer Based on WGCNA

Quan, Qingli; Xiong, Xinxin; Wu, Shao-Guo; Yu, Mei

doi:10.3389/fgene.2021.760225

Cited by 26 publications

(20 citation statements)

References 36 publications

(45 reference statements)

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“…Weighted gene co-expression network analysis (WGCNA) is a widely used method in screening candidate biomarkers and exploring the relationships between gene sets and external biological clinical traits ( Langfelder and Horvath, 2008 ). Multivarious research has utilized this algorithm to identify hub genes involved in the pathogenesis of OC ( Zeleznik et al, 2020 ; Chang et al, 2021 ; Quan et al, 2021 ). Another notable bioinformatics tool based on support vector regression modeling, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), was developed to deconvolute cell types and dissect the cellular components at the transcription level ( Newman et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Chen

Luo

et al. 2022

Front. Genet.

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Background: Immunotherapy is a promising strategy for ovarian cancer (OC), and this study aims to identify biomarkers related to CD8+ T cell infiltration to further discover the potential therapeutic target.Methods: Three datasets with OC transcriptomic data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Two immunotherapy treated cohorts were obtained from the Single Cell Portal and Mariathasan’s study. The infiltration fraction of immune cells was quantified using three different algorithms, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and microenvironment cell populations counter (MCPcounter), and single-sample GSEA (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the co-expression modules and related genes. The nonnegative matrix factorization (NMF) method was proposed for sample classification. The mutation analysis was conducted using the “maftools” R package. Key molecular markers with implications for prognosis were screened by univariate COX regression analysis and K-M survival analysis, which were further determined by the receiver operating characteristic (ROC) curve.Results: A total of 313 candidate CD8+ T cell-related genes were identified by taking the intersection from the TCGA-OV and GSE140082 cohorts. The NMF clustering analysis suggested that patients in the TCGA-OV cohort were divided into two clusters and the Cluster 1 group showed a worse prognosis. In contrast, Cluster 2 had higher amounts of immune cell infiltration, elevated ssGSEA scores in immunotherapy, and a higher mutation burden. CSMD3, MACF1, PDE4DIP, and OBSCN were more frequently mutated in Cluster 1, while SYNE2 was more frequently mutated in Cluster 2. CD38 and CXCL13 were identified by univariate COX regression analysis and K-M survival analysis in the TCGA-OV cohort, which were further externally validated in GSE140082 and GSE32062. Of note, patients with lower CXCL13 expression showed a worse prognosis and the CR/PR group had a higher expression of CXCL13 in two immunotherapy treated cohorts.Conclusion: OC patients with different CD8+ T cell infiltration had distinct clinical prognoses. CXCL13 might be a potential therapeutic target for the treatment of OC.

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Section: Introductionmentioning

confidence: 99%

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Chen

Luo

et al. 2022

Front. Genet.

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“…KLRK1 is a receptor expressed by NK cells and cytotoxic T lymphocytes; binds non-covalently to DAP10 signaling protein to provide co-stimulatory or activation signals to T and NK cells ( 59 , 60 ). Both FASLG and KLRC4-KLRK1 are involved in the apoptosis of NK and T cells and cytotoxicity ( 61 , 62 ). SLAMF7 homologous interactions regulate NK cell cytolytic activity.…”

Section: Discussionmentioning

confidence: 99%

Natural killer cell-related prognosis signature characterizes immune landscape and predicts prognosis of HNSCC

et al. 2022

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BackgroundHead and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and immunotherapeutic outcomes. Natural killer (NK) cells play essential roles in malignancies’ development, diagnosis, and prognosis. The purpose of this study was to establish a reliable signature based on genes related to NK cells (NRGs), thus providing a new perspective for assessing immunotherapy response and prognosis of HNSCC patients.MethodsIn this study, NRGs were used to classify HNSCC from the TCGA-HNSCC and GEO cohorts. The genes were evaluated using univariate cox regression analysis based on the differential analysis of normal and tumor samples in TCGA-HNSCC conducted using the “limma” R package. Thereafter, we built prognostic gene signatures using LASSO-COX analysis. External validation was carried out in the GSE41613 cohort. Immunity analysis based on NRGs was performed via several methods, such as CIBERSORT, and immunotherapy response was evaluated by TIP portal website.ResultsWith the TCGA-HNSCC data, we established a nomogram based on the 17-NRGs signature and a variety of clinicopathological characteristics. The low-risk group exhibited a better effect when it came to immunotherapy.Conclusions17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research.

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“…Although evidence on its role in OC was still poor, an early study suggested that it could affect the progression by regulation the ER in OC (28). KCNA3 has been identified as a key immune-related gene in ovarian cancer, and moreover, its overexpression was associated with disease stage and superior survival (29). Other key genes, including NLGN1 (30) and GALR2 (31), were identified in the cancers, but their roles in ovarian cancer, especially in platinum sensitivity were unreported before.…”

Section: Discussionmentioning

confidence: 99%

Establish of an Initial Platinum-Resistance Predictor in High-Grade Serous Ovarian Cancer Patients Regardless of Homologous Recombination Deficiency Status

Nie

Guo

et al. 2022

Front. Oncol.

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BackgroundsOvarian cancer (OC) is still the leading aggressive and lethal disease of gynecological cancers, and platinum-based regimes are the standard treatments. However, nearly 20%–30% of patients with OC are initial platinum resistant (IPR), and there is a lack of valid tools to predict whether they will be primary platinum resistant or not prior to chemotherapy.MethodsTranscriptome data from The Cancer Genome Atlas (TCGA) was downloaded as the training data, and transcriptome data of GSE15622, GSE102073, GSE19829, and GSE26712 were retrieved from Gene Expression Omnibus (GEO) as the validation cohorts. Differentially expressed genes (DEGs) were selected between platinum-sensitive and platinum-resistant patients from the training cohort, and multiple machine-learning algorithms [including random forest, XGboost, and least absolute shrinkage and selection operator (LASSO) regression] were utilized to determine the candidate genes from DEGs. Then, we applied logistic regression to establish the IPR signature based on the expression. Finally, comprehensive clinical, genomic, and survival feature were analyzed to understand the application value of the established IPR signature.ResultsA total of 532 DEGs were identified between platinum-resistant and platinum-sensitive samples, and 11 of them were shared by these three-machine learning algorithms and utilized to construct an IPR prediction signature. The area under receiver operating characteristic curve (AUC) was 0.841 and 0.796 in the training and validation cohorts, respectively. Notably, the prediction capacity of this signature was stable and robust regardless of the patients’ homologous recombination deficiency (HRD) and mutation burden status. Meanwhile, the genomic feature was concordant between samples with high- or low-IPR signature, except a significantly higher prevalence of gain at Chr19q.12 (regions including CCNE1) in the high-IPR signature samples. The efficacy of prediction of platinum resistance of IPR signature successfully transferred to the precise survival prediction, with the AUC of 0.71, 0.72, and 0.66 to predict 1-, 3-, and 5-year survival, respectively. At last, we found a significantly different tumor-infiltrated lymphocytes feature, including lower abundance of CD4+ naive T cells in the samples with high-IPR signature. A relatively lower tumor immune dysfunction and exclusion (TIDE) value and more sensitivity to multiple therapies including Gefitinib may suggest the potency to transfer from platinum-based therapy to immunotherapy or target therapies in patients with high-IPR signature.ConclusionOur study established an IPR signature based on the expression of 11 genes that could stably and robustly distinguish OC patients with IPR and/or poor outcomes, which may guide therapeutic regimes tailoring.

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Identification of Immune-Related Key Genes in Ovarian Cancer Based on WGCNA

Cited by 26 publications

References 36 publications

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Natural killer cell-related prognosis signature characterizes immune landscape and predicts prognosis of HNSCC

Establish of an Initial Platinum-Resistance Predictor in High-Grade Serous Ovarian Cancer Patients Regardless of Homologous Recombination Deficiency Status

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