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Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer.

ABSTRACT: Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, p < 0.05) and CIN25 (C-index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi-squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p-value (p < 0.1) in both chi-squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti-tumour drugs has changed significantly (p < 0.05).

SUBMITTER: Zhang X

PROVIDER: S-EPMC9258710 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer.

Zhang Xi X Kong Weikaixin W Gao Miaomiao M Huang Weiran W Peng Chao C Huang Zhuo Z Xie Zhengwei Z Guo Hongyan H

Journal of cellular and molecular medicine 20220623 13

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In tes ...[more]

PMID: 35735060

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Project description:BackgroundAccording to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV.MethodsIn this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model.ResultsTwo subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples.ConclusionsAnalysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Project description:PANoptosis, an interplay between pyroptosis, apoptosis, and necroptosis, is deeply involved in cancer development and immunity. However, the influence of PANoptosis in hepatocellular carcinoma (HCC) remains to be further investigated. The differentially expressed PANoptosis-related genes (PANRGs) was screened in The Cancer Genome Atlas (TCGA) database. Accordingly, mutation, bioinformatics, and consensus clustering analyses were performed. Then, a prognostic risk model was developed by least absolute shrinkage and selection operator (LASSO) Cox regression. Furthermore, the prognostic value, immunity correlation and therapeutic response prediction ability of risk model were explored. A total of 18 PANRGs were differently expressed in the TCGA-HCC cohort and were mainly involved in cancer- and cell death-related signal pathways. Using unsupervised clustering method, we identified two PANRGs-mediated clustering patterns. The remarkable differences between the two clusters on overall survival (OS) and clinical features were demonstrated respectively. Based on the five-gene prognostic risk model, the calculated PANRG-scores were used to categorize the subgroups as high- and low-risk. Notably, the high-risk subgroup had a dismal prognosis and exhibited much lower immune infiltration levels of mast cells, nature killer cells and pDCs, but higher levels of aDCs, iDCs and Treg cells than those in the low-risk subgroup. Furthermore, we constructed a reliable nomogram combining clinical traits and PANRG-score to predict the OS of HCC patients. The significantly negative correlation between PANoptosis and tumor mutation burden (TMB), ferroptosis were revealed. In drug sensitivity analysis, the high-risk subgroup had a considerably lower TIDE score, suggesting a preferable response to immunotherapy, and may be more sensitive to Tipifarnib, Imatinib, Doxorubicin, and Gemcitabine. The upregulated mRNA expressions of FADD were validated in 16 paired HCC tissues of Guangxi cohort. Based on PANoptosis-related genes, an integrated risk signature was constructed to provide a roadmap for patient stratification and predict HCC patient's prognosis. The patients with the higher PANRG-score may carry a dismal survival and relatively low immune infiltration, but a potential better immunotherapy response. Therefore, future HCC therapy perspectives should emphasize the setting of PANoptosis to achieve a personalized, practicable and effective therapeutic regimen.

Project description:BackgroundA novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset.MethodsThis prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells in vitro.ResultsWe constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells in vitro, and increased the percentage of apoptotic W038 cells.ConclusionsThe analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene GBP1P1 might serve as prognostic biomarker for OC.

Project description:IntroductionCellular senescence is a cellular response to stress, including the activation of oncogenes, and is characterized by irreversible proliferation arrest. Restricted studies have provided a relationship between cellular senescence and immunotherapy for esophageal cancer.MethodsIn the present study, we obtained clinical sample of colon cancer from the TCGA database and cellular senescence-related genes from MSigDB and Genecard datasets. Cellular senescence-related prognostic genes were identified by WGCNA, COX, and lasso regression analysis, and a cellular senescence-related risk score (CSRS) was calculated. We constructed a prognostic model based on CSRS. Validation was performed with an independent cohort that GSE53625. Three scoring systems for immuno-infiltration analysis were performed, namely ssGSEA analysis, ESTIMATE scores and TIDE scores.ResultFive cellular senescence-related genes, including H3C1, IGFBP1, MT1E, SOX5 and CDHR4 and used to calculate risk score. Multivariate regression analysis using cox regression model showed that cellular senescence-related risk scores (HR=2.440, 95% CI=1.154-5.159, p=0.019) and pathological stage (HR=2.423, 95% CI=1.119-5.249, p=0.025) were associated with overall survival (OS). The nomogram model predicts better clinical benefit than the American Joint Committee on Cancer (AJCC) staging for prognosis of patients with esophageal cancer with a five-year AUC of 0.946. Patients with high CSRS had a poor prognosis (HR=2.93, 95%CI=1.74-4.94, p<0.001). We observed differences in the distribution of CSRS in different pathological staging and therefore performed a subgroup survival analysis finding that assessment of prognosis by CSRS independent of pathological staging. Comprehensive immune infiltration analysis and functional enrichment analysis suggested that patients with high CSRS may develop immunotherapy resistance through mechanisms of deacetylation and methylation.DiscussionIn summary, our study suggested that CSRS is a prognostic risk factor for esophageal cancer. Patients with high CSRS may have worse immunotherapy outcomes.

Project description:BackgroundFour RNA adenosine modifications, including m6A, m1A, alternative polyadenylation, and adenosine-to-inosine RNA editing, have been identified as potentially valuable in influencing colorectal carcinogenesis, immune infiltration, and response to drug therapy. However, the regulatory mechanisms and clinical significance of these four RNA modifications in ovarian cancer (OC) remain unknown.MethodsWe comprehensively described the transcriptional and genetic modifications of 26 RNA modification "writers" in OC and assessed the expression patterns. We identified two RNA modification subtypes using an unsupervised clustering approach. Subsequently, using differentially expressed genes (DEGs) in both subtypes, we calculated RNA modification "writer" scores (RMW scores) to characterize the RNA modifications of single OC patients. RMW score-related gene expression was investigated by qRT-PCR. We explored the correlation between RMW score and clinical features, immune infiltration, and drug sensitivity. We drew a nomogram to more intuitively and accurately describe the application value of the RMW score.ResultsWe found that molecular alterations in "writers" are strongly related to prognostic and immune-infiltrating features in OC patients. We identified two different clusters of RNA modifications. According to the immune infiltration characteristics in the two RNA modification isoforms, cluster A and cluster B can correspond to "hot" and "cold" tumors, respectively. With the median RMW score, we classified the patients into high- and low-score subgroups. A low RMW score was associated with good patient prognosis and lower immune infiltration. In addition, a low RMW score equated with a higher cancer stem cell index and a lower tumor mutation burden, which to some extent affected the sensitivity of patients to therapeutic drugs. Seven RMW score-related gene expressions were investigated by qRT-PCR in three OC cell lines. Compared to previously known models, our established RMW score has higher accuracy in predicting patient survival.ConclusionA comprehensive analysis of four RNA modification patterns in OC reveals their potential value in OC prognosis, immune microenvironment, and drug sensitivity. These results could deepen our knowledge of RNA modification and yield fresh insights for new personalized therapeutic strategies.

Project description:Hepatocellular carcinoma (HCC) has a high degree of malignancy and a poor prognosis. Immune infiltration-related genes have shown good predictive value in the prognosis of many solid tumours. In this study, we established and verified prognostic biomarkers consisting of immune infiltration-related genes in HCC. Gene expression data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differential gene expression analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to screen prognostic immune infiltration-related genes and to construct a risk scoring model. Kaplan-Meier (KM) survival plots and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic performance of the risk scoring model in the TCGA-HCC cohort. In addition, a nomogram model with a risk score was established, and its predictive performance was verified by ROC analysis and calibration plot analysis in the TCGA-HCC cohort. Gene set enrichment analysis (GSEA) identified pathways and biological processes that may be enriched in the high-risk group. Finally, immune infiltration analysis was used to explore the characteristics of the tumour microenvironment related to the risk score. We identified 17 immune infiltration-related genes with prognostic value and constructed a risk scoring model. ROC analysis showed that the risk scoring model can accurately predict the 1-year, 3-year, and 5-year overall survival (OS) of HCC patients in the TCGA-HCC cohort. KM analysis showed that the OS of the high-risk group was significantly lower than that of the low-risk group (P < 0.001). The nomogram model effectively predicted the OS of HCC patients in the TCGA-HCC cohort. GSEA indicated that the immune infiltration-related genes may be involved in biological processes such as amino acid and lipid metabolism, matrisome and small molecule transportation, immune system regulation, and hepatitis virus infection. Immune infiltration analysis showed that the level of immune cell infiltration in the high-risk group was low, and the risk score was negatively correlated with infiltrating immune cells. Our prognostic model based on immune infiltration-related genes in HCC could help the prognostic assessment of HCC patients and provide potential targets for HCC inhibition.

Dataset Information

Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer.

Publications

Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer.

Similar Datasets

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