CSMD3 is Associated with Tumor Mutation Burden and Immune Infiltration in Ovarian Cancer Patients

Lü, Nan; Liu, Jinhui; Xu, Mengting; Liang, J.; Wang, Yichun; Wu, Zhipeng; Xing, Yan; Diao, Feiyang

doi:10.2147/ijgm.s335592

Cited by 28 publications

(24 citation statements)

References 53 publications

(57 reference statements)

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“…The mutation frequencies were distinctly different between the two subtypes. Lu et al (2021) ’s study pointed out that the overall survival of OC patients with CSMD3 mutation was inferior to those with wild-type CSMD3 and CSMD3 mutation was highly correlated with increased TMB. Cheasley et al (2021) performed a comprehensive genomic analysis of low-grade serous ovarian carcinoma patients and found that MACF1 had an 11% mutation frequency as a putative novel driver gene, which could be translated into an improved therapeutic path.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Chen

Luo

et al. 2022

Front. Genet.

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“…To the best of our knowledge, most lncRNAs we identified here are novel GIRlncRNAs for NSCLC, while some GIRlncRNAs were already reported in lung adenocarcinoma (Geng et al, 2021;Peng et al, 2021;Wu G. et al, 2021). Notably, lncRNA AC023824, AC025419.1, AC079949.2, LINC02412, and LINC00337 were verified as risk factors associated with the OS of LUAD patients (Li R. et al, 2019;Song et al, 2020;Shao et al, 2021;Wu G. et al, 2021;Wu Y. et al, 2021). LINC01600 and LINC02434 were reported as predictors for the prognosis of PCa and HNSCC patients, respectively (Xu et al, 2020;Jiang et al, 2021).…”

Section: Discussionmentioning

confidence: 72%

“…Functional analysis revealed that these lncRNAs were mainly enriched in nucleoside or ribonucleoside metabolism, cell-cycle checkpoint, nuclear membrane enveloping, and tumorigenesis, which are involved in maintaining genomic instability ( Sieber et al, 2003 ; Deng, 2006 ; Aird and Zhang, 2015 ; Lim et al, 2016 ). TP53 and CSMD3 were the two most frequently mutated genes in NSCLC ( Liu et al, 2012 ), and their mutation status was closely associated with high TMB causing genomic instability and poor clinical prognosis ( Zhang et al, 2017 ; Bernard et al, 2020 ; Lu et al, 2021 ; Wen et al, 2021 ). We further conducted hierarchical clustering analysis and differential analysis of mutation counts and found that GIRlncRNA-clustering GU-like patients were burdened with a higher TMB than GS-like patients.…”

Section: Discussionmentioning

confidence: 99%

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

et al. 2022

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Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

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“…SETD2 mutations have been associated with high TMB, microsatellite instability and favorable outcomes with ICI [ 73 ]. Further gene co-alterations found with high prevalence in one or more BRAF functional classes have been associated with higher TMB, such as TTN , CSMD3 , USH2A and RYR2 ones [ 74 , 75 , 76 , 77 ]. Together with a different distribution of DNA damage response gene alterations, which has been associated with enhanced ICI efficacy, these features suggest a potentially promising role of immunotherapy in selected patients [ 78 ].…”

Section: Discussionmentioning

confidence: 99%

Genomic Landscape, Clinical Features and Outcomes of Non-Small Cell Lung Cancer Patients Harboring BRAF Alterations of Distinct Functional Classes

Federico

Giglio

Gelsomino

et al. 2022

Cancers

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Background: In non-small cell lung cancer (NSCLC), BRAF class 1 alterations are effectively targeted by BRAF inhibitors. Conversely, targeted therapies have very low or absent activity in patients carrying class 2 and 3 alterations. The spectrum of BRAF alterations in NSCLC patients, and their accompanying clinical features, genomic landscape and treatment outcomes have been poorly reported. Patients and methods: We identified BRAF alterations of defined functional class across different tumors through a systematic review. Then, we selected NSCLC patients carrying BRAF alterations, according to the systematic review, in the cBioPortal (cBioPortal cohort) to collect and analyze clinical, biomolecular and survival data. Finally, we identified NSCLC patients carrying BRAF non-V600 mutations enrolled in POPLAR and OAK trials (POPLAR/OAK cohort), extracting clinical and survival data for survival analyses. Results: 100 different BRAF non-V600 alterations were identified through the systematic review. In the cBioPortal cohort (n = 139), patients harboring class 2 and 3 alterations were more frequently smokers and had higher tumor mutational burden compared to those carrying class 1 alterations. The spectrum of most frequently co-altered genes was significantly different between BRAF alterations classes, including SETD2, STK11, POM121L12, MUC16, KEAP1, TERT, TP53 and other genes. In the POPLAR/OAK cohort, patients carrying non-V600 BRAF alterations were characterized by poor prognosis compared to BRAF wild-type patients. Conclusions: Different classes of BRAF alterations confer distinctive clinical features, biomolecular signature and disease behavior to NSCLC patients. Non-V600 alterations are characterized by poor prognosis, but key gene co-alterations involved in cancer cell survival and immune pathways may suggest their potential sensitivity to tailored treatments.

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CSMD3 is Associated with Tumor Mutation Burden and Immune Infiltration in Ovarian Cancer Patients

Cited by 28 publications

References 53 publications

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

Genomic Landscape, Clinical Features and Outcomes of Non-Small Cell Lung Cancer Patients Harboring BRAF Alterations of Distinct Functional Classes

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