The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer

Belotti, Yuri; Lim, Elaine; Lim, Chwee Teck

doi:10.3390/cancers14020404

Cited by 14 publications

(9 citation statements)

References 78 publications

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“…In addition, more meta-analyses have shown that potentially functional polymorphisms of IL12A and IL12B are thought to increase the risk of malignancies such as gastric, lung, and cervical cancers ( 43 – 45 ). TLL1 is significantly upregulated in OC patients and has been suggested as a prognostic marker in OC patients ( 46 , 47 ), and the same results were obtained in our study. Zou et al.…”

Section: Discussionsupporting

confidence: 92%

Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer

et al. 2023

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BackgroundOvarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response.Methods15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients.ResultsIn the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.

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Section: Discussionsupporting

confidence: 92%

Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer

et al. 2023

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“…Other genes, while not yet described in cervical cancer, have been found to be prognostic in ovarian cancer ( RGS11 [ 31 ], CHAD and CBLN2 [ 32 ], NETO1 [ 33 ], HSPE1 [ 34 ], and BIRC6 , which Lnc-TTC27–9 is intronic to [ 35 ]). The expression of SH3BP5 is reduced in ovarian cancer samples compared to normal tissue and that silencing of Sab protein expression may lead to chemo-resistance [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

Archer

Seffernick

Sun

et al. 2022

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The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

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“…Other genes, while not yet described in cervical cancer, have been found to be prognostic in ovarian cancer (RGS11 [30], CHAD and CBLN2 [31], NETO1 [32], HSPE1 [33], and BIRC6 which Lnc-TTC27-9 is intronic to [34]). Expression of SH3BP5 is reduced in ovarian cancer samples compared to normal tissue and that silencing of Sab protein expression may lead to chemo-resistance [35].…”

Section: Resultsmentioning

confidence: 99%

Ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

Archer¹,

Seffernick²,

Sun³

et al. 2022

Preprint

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Stage of cancer is a discrete ordinal response that indicates aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with stage of cervical cancer, to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients has been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which is capable of fitting cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA data. We demonstrate use of this package through application to TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional dataset and effectively performs variable selection.

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The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer

Cited by 14 publications

References 78 publications

Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer

Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

Ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

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