Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification.
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ABSTRACT:
SUBMITTER: Yavartanoo M
PROVIDER: S-EPMC8584987 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundColorectal cancer is the fourth most common cancer and the second leading cause of cancer-related death in the USA. The aim of this study was to establish a tumor gene signature based on tumor stromal cell and autophagy for predicting the risk of recurrence in patients with colorectal cancer.MethodsWe used "Rtsne" and "xCell" R packages to estimate autophagy and stroma status, respectively. The discovery cohort used microarray gene expression data retrieved from the GSE39582 dataset. The Cox regression model and Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify prognostic genes and to construct an autophagy-stroma-based gene signature. Moreover, external validation was conducted using GSE17538, GSE38832, TCGA database, and patient data obtained from the First Hospital of China Medical University (CMU).ResultsThe LASSO model identified three genes (TNS1, TAGLN, and SFRP4) which were used to develop a risk stratification gene signature. The autophagy-stroma-based gene signature was identified as an independent prognostic factor by multivariate analysis (p = 0.0023). The results were validated in GSE17538 (p=0.0062), GSE38832 (p=0.028), TCGA (p=0.046) database, and patient data obtained from the First Hospital of China Medical University (CMU) (p=0.027).ConclusionWe have established and verified a feasible prognostic model of colorectal cancer based on autophagy and stromal cell characteristics of patients. The model can be used to evaluate recurrence risk of cancer patients, and the hub genes in the model provide potential targets for targeted colorectal cancer treatment.
| S-EPMC8180295 | biostudies-literature
Project description:BackgroundIncreasing evidences have found that the clinical importance of the interaction between hypoxia and immune status in gastric cancer microenvironment. However, reliable prognostic signatures based on combination of hypoxia and immune status have not been well-established. This study aimed to develop a hypoxia-immune-based gene signature for risk stratification in gastric cancer.MethodsHypoxia and immune status was estimated with transcriptomic profiles for a discovery cohort from GEO database using the t-SNE and ESTIMATE algorithms, respectively. The Cox regression model with the LASSO method was applied to identify prognostic genes and to develop a hypoxia-immune-based gene signature. The TCGA cohort and two independent cohorts from GEO database were used for external validation.ResultsLow hypoxia status (p < 0.001) and high immune status (p = 0.005) were identified as favorable factors for patients' overall survival. By using the LASSO model, four genes, including CXCR6, PPP1R14A and TAGLN, were identified to construct a gene signature for risk stratification. In the discovery cohort (n = 357), patients with low risk yielded better outcomes than those with high risk regarding overall survival across and within TNM stage subgroups. Multivariate analysis identified the hypoxia-immune-based gene signature as an independent prognostic factor (p < 0.001). A nomogram integrating the gene signature and known risk factors yielded better performance and net benefits in calibration and decision curve analyses. Similar results were validated in the TCGA (n = 321) and two independent GEO (n = 300 and n = 136, respectively) cohorts.ConclusionsThe hypoxia-immune-based gene signature represents a promising tool for risk stratification tool in gastric cancer. It might serve as a prognostic classifier for clinical decision-making regarding individualized prognostication and treatment, and follow-up scheduling.
| S-EPMC7226948 | biostudies-literature
Project description:Background: Glioma is a highly heterogeneous disease, causing the prognostic prediction a challenge. Pyroptosis, a programmed cell death mediated by gasdermin (GSDM), is characterized by cell swelling and the release of inflammatory factors. Pyroptosis occurs in several types of tumor cells, including gliomas. However, the value of pyroptosis-related genes (PRGs) in the prognosis of glioma remains to be further clarified. Methods: In this study, mRNA expression profiles and clinical data of glioma patients were acquired from TCGA and CGGA databases, and one hundred and eighteen PRGs were obtained from the Molecular Signatures Database and GeneCards. Then, consensus clustering analysis was performed to cluster glioma patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to establish a polygenic signature. Functional verification of the pyroptosis-related gene GSDMD was achieved by gene knockdown and western blotting. Moreover, the immune infiltration status between two different risk groups were analyzed through the "gsva" R package. Results: Our results demonstrated that the majority of PRGs (82.2%) were differentially expressed between lower-grade gliomas (LGG) and glioblastoma (GBM) in the TCGA cohort. In univariate Cox regression analysis, eighty-three PRGs were shown to be associated with overall survival (OS). A five-gene signature was constructed to divide patients into two risk groups. Compared with patients in the low-risk group, patients in the high-risk group had obviously shorter OS (p < 0.001). Also, we found that the high-risk group showed a higher infiltrating score of immune cells and immune-related functions. Risk score was an independent predictor of OS (HR > 1, p < 0.001). Furthermore, knockdown of GSDMD decreased the expression of IL-1β and cleaved caspase-1. Conclusion: Our study constructed a new PRGs signature, which can be used to predict the prognosis of glioma patients. Targeting pyroptosis might serve as a potential therapeutic strategy for glioma.
| S-EPMC9968976 | biostudies-literature
Project description:Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy.
| S-EPMC8843853 | biostudies-literature
Project description:ObjectiveDue to the molecular heterogeneity of gastric cancer, only minor patients respond to immunotherapeutic schemes. This study is aimed at developing an immune-based gene signature for risk stratification and immunotherapeutic efficacy assessment in gastric cancer.MethodsAn immune-based gene signature was developed in gastric cancer by LASSO method in the training set. The predictive performance was validated in the external datasets. KEGG pathways related to risk scores were assessed by GSEA. Based on multivariate Cox regression analysis, a nomogram was established. Sensitivity to chemotherapy drugs was evaluated between high- and low-risk samples. The relationships of risk scores with infiltration levels of immune cells, stromal scores, immune scores, immune cell subgroups, and overall response to anti-PD-L1 therapy were determined.ResultsOur results showed that high risk scores were indicative of undesirable survival outcomes both in the training set (p < 0.0001) and the validation set (p = 0.002). Moreover, this signature could independently predict patients' survival (HR: 2.656 (1.919-3.676) and p < 0.001). Subgroup analysis confirmed the sensitivity of this signature in predicting prognosis (all p < 0.05). Cancer-related pathways were primarily enriched in high-risk samples, such as MAPK and TGF-β pathways (p < 0.05). By incorporating stage and the risk score, we established a nomogram for predicting one-, three-, and five-year survival probability. Patients with high-risk scores were more sensitive to chemotherapy drugs (p < 0.05). There was heterogeneity in immune cells between high- and low-risk samples (p < 0.05). Samples with progressive disease exhibited the highest risk score, and those with complete response had the lowest risk score (p < 0.05).ConclusionThis immune-based gene signature might be representative of a promising prognostic classifier for predicting risk stratification and immunotherapeutic efficacy in gastric cancer, assisting personalized therapy and follow-up plan.
| S-EPMC8602949 | biostudies-literature
Project description:Background Colon cancer (CC) is one of the most common cancers with high morbidity globally. Ubiquitination is involved in the characterization of multiple biological processes, and some ubiquitinated enzymes are associated with the prognosis of CC. However, the prognostic model associated with ubiquitination-related genes (URGs) for CC is unavailable. Methods Gene expression data, somatic mutations, transcriptome profiles, microsatellite instability status (MSI) status, and clinical information for CC were obtained from The Cancer Genome Atlas (TCGA) dataset. Seven URGs were used for establishing a prognostic prediction model, which was constructed and validated in GSE17538. Besides, genomic variance analysis (GSVA) was used to explore further the differences in biological pathway activation status between the high-risk and low-risk groups. Finally, the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm analysis were used to characterize the cellular infiltration in the microenvironment. Results A seven-URG prognostic signature was established, based on which patients in the training and test groups could be divided into high-risk and low-risk groups. The results demonstrated that the model has a solid ability to predict the prognosis of CC patients. Conclusions We established a prognostic prediction model for CC based on ubiquitination. Then we analyzed the genetic characteristics associated with ubiquitination and the tumor microenvironment (TME) cell infiltration in CC. These results are worthy of exploring new clinical treatment strategies for CC.
| S-EPMC9641125 | biostudies-literature
Project description:BackgroundSome patients with high-risk gastrointestinal stromal tumor (GIST) experience disease progression after complete resection and adjuvant therapy. It is of great significance to distinguish these patients among those with high-risk GIST. Radiomics has been demonstrated as a promising tool to predict various tumors prognosis.MethodsFrom January 2006 to December 2018, a total of 100 high-risk GIST patients (training cohort: 60; validation cohort: 40) from Guangdong Provincial People's Hospital with preoperative enhanced computed tomography (CT) images were enrolled. The radiomics features were extracted and a risk score was built using least absolute shrinkage and selection operator-Cox model. The clinicopathological factors were analyzed and a nomogram was established with and without radiomics risk score. The concordance index (C-index), calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomograms.ResultsWe selected 11 radiomics features associated with recurrence or metastasis. The risk score was calculated and significantly associated with disease-free survival (DFS) in both the training and validation group. Cox regression analysis showed that Ki67 was an independent risk factor for DFS [P=0.004, hazard ratio 4.615, 95% confidence interval (CI): 1.624-13.114]. The combined radiomics nomogram, which integrated the radiomics risk score and significant clinicopathological factors, showed good performance in predicting DFS, with a C-index of 0.832 (95% CI: 0.761-0.903), which was better than the clinical nomogram (C-index 0.769, 95% CI: 0.679-0.859) in training cohort. The calibration curves and the DCA plot suggested satisfying accuracy and clinical utility of the model.ConclusionsThe CT-based radiomics nomogram, combined with the clinicopathological factors and risk score, has good potential to assess the recurrence or metastasis of patients with high-risk GIST.
| S-EPMC10932689 | biostudies-literature
Project description:BackgroundInflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation-related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients.MethodsThe RNA-sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations-counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis.ResultsA two-IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low-risk group had more anti-tumor immune cells infiltrated while patient with high-risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in validation set. Time-dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone.ConclusionThe IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival.
| S-EPMC9972108 | biostudies-literature
Project description:Emerging evidence suggested that mitophagy may play an important role in the progression of hepatocellular carcinoma (HCC), whereas the association between mitophagy-related genes and HCC patients' prognosis remains unknown. In this study, we aimed to investigate the potential prognostic values of mitophagy-related genes (MRGs) on HCC patients at the genetic level. According to median immunoscore, we categorized HCC patients from TCGA cohort into two immune score groups, while 39 differential expression MRGs were identified. By using univariate analysis, we screened out 18 survival-associated MRGs, and then, the least absolute shrinkage and selection operator (LASSO) analysis was applied to construct a prognosis model that consisted of 9 MRGs (ATG7, ATG9A, BNIP3L, GABARAPL1, HTRA2, MAP1LC3B2, TFE3, TIGAR, and TOMM70). In our prognostic model, overall survival in the high and low-risk groups was significantly different (P < 0.001), and the respective areas under the curve (AUC) of our prognostic model were 0.686 for 3-year survival in the TCGA cohort and 0.776 for 3-year survival in the ICGC cohort. Moreover, we identified the risk score as the independent factor for predicting the HCC patients' prognosis by using single and multifactor analyses, and a nomogram was also constructed for future clinical application. Further functional analyses showed that the immune status between two risk groups was significantly different. Our findings may provide a novel mitophagy-related gene signature, and these will be better used for prognostic prediction in HCC, thus improving patient outcome.
| S-EPMC8560278 | biostudies-literature
Project description:BackgroundDue to the phenotypic and molecular diversity of hepatocellular carcinomas (HCC), it is still a challenge to determine patients' prognosis. We aim to identify new prognostic markers for resected HCC patients.Methods274 patients were retrospectively identified and samples collected from Zhongshan hospital, Fudan University. We analyzed the gene expression patterns of tumors and compared expression patterns with patient survival times. We identified a "9-gene signature" associated with survival by using the coefficient and regression formula of multivariate Cox model. This molecular signature was then validated in three patients cohorts from internal cohort (n = 69), TCGA (n = 369) and GEO dataset (n = 80).ResultsWe identified 9-gene signature consisting of ZC2HC1A, MARCKSL1, PTGS1, CDKN2B, CLEC10A, PRDX3, PRKCH, MPEG1 and LMO2. The 9-gene signature was used, combined with clinical parameters, to fit a multivariable Cox model to the training cohort (concordance index, ci = 0.85), which was successfully validated (ci = 0.86 for internal cohort; ci = 0.78 for in silico cohort). The signature showed improved performance compared with clinical parameters alone (ci = 0.70). Furthermore, the signature predicted patient prognosis than previous gene signatures more accurately. It was also used to stratify early-stage, HBV or HCV-infected patients into low and high-risk groups, leading to significant differences in survival in training and validation (P < 0.001).ConclusionsThe 9-gene signature, in which four were upregulated (ZC2HC1A, MARCKSL1, PTGS1, CDKN2B) and five (CLEC10A, PRDX3, PRKCH, MPEG1, LMO2) were downregulated in HCC with poor prognosis, stratified HCC patients into low and high risk group significantly in different clinical settings, including receiving adjuvant transarterial chemoembolization and especially in early stage disease. This new signature should be validated in prospective studies to stratify patients in clinical decisions.
| S-EPMC6582497 | biostudies-literature