A Cholesterol Homeostasis-Related Gene Signature Predicts Prognosis of Endometrial Cancer and Correlates With Immune Infiltration
Ontology highlight
ABSTRACT:
SUBMITTER: Chen Y
PROVIDER: S-EPMC8591263 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The tumor metabolic reprogramming contributes to the progression and prognosis of cervical cancer (CC). However, the potential remodeling mechanisms of tumor metabolism in the immune microenvironment of CC remain largely unknown. In this study, we first performed microarray analysis to identify differential metabolic gene expression. A novel 5-metabolic-related genes (MRGs) signature comprising P4HA1, P4HA2, ABL2, GLTP, and CYP4F12 was established to better predict prognosis of CC using LASSO-Cox regression analysis. This signature could reveal the metabolic features and monitor the immune status of tumor microenvironment (TME). Among them, P4HA2 was significantly upregulated in CC tissues and negatively correlated with CD8+T cells. Knockdown of P4HA2 inhibited lipid droplets (LDs) accumulation and cancer cells invasion. Moreover, P4HA2 knockdown significantly suppressed PD-L1 expression. This study provides a new and feasible method for evaluating the prognosis of CC and explores the potential value to navigate metabolic pathways to enhance anti-tumor immunity and immunotherapy.
| S-EPMC8264424 | biostudies-literature
Project description:To explore novel therapeutic targets, develop a gene signature and construct a prognostic nomogram of bladder cancer (BCa). Transcriptome data and clinical traits of BCa were downloaded from UCSC Xena database and Gene Expression Omnibus (GEO) database. We then used the method of Single sample Gene Set Enrichment analysis (ssGSEA) to calculate the infiltration abundances of 24 immune cells in eligible BCa samples. By weighted correlation network analysis (WGCNA), we identified turquoise module with strong and significant association with the infiltration abundance of immune cells which were associated with overall survival of BCa patients. Subsequently, we developed an immune cell infiltration-related gene signature based on the module genes (MGs) and immune-related genes (IRGs) from the Immunology Database and Analysis Portal (ImmPort). Then, we tested the prognostic power and performance of the signature in both discovery and external validation datasets. A nomogram integrated with signature and clinical features were ultimately constructed and tested. Five prognostic immune cell infiltration-related module genes (PIRMGs), namely FPR1, CIITA, KLRC1, TNFRSF6B, and WFIKKN1, were identified and used for gene signature development. And the signature showed independent and stable prognosis predictive power. Ultimately, a nomogram consisting of signature, age and tumor stage was constructed, and it showed good and stable predictive ability on prognosis. Our prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for BCa. Further researches are needed to verify the clinical effectiveness of this nomogram and these biomarkers.
| S-EPMC8370985 | biostudies-literature
Project description:Increasing numbers of biomarkers have been identified in various cancers. However, biomarkers associated with endometrial carcinoma (EC) remain largely to be explored. In the current research, we downloaded the RNA-seq data and corresponding clinicopathological features from the Cancer Genome Atlas (TCGA) database. We conducted an expression analysis, which resulted in RILPL2 as a novel diagnostic biomarker in EC. The dysregulation of RILPL2 in EC was also validated in multiple datasets. The correlations between clinical features and RILPL2 expression were assessed by logistic regression analysis. Then, Kaplan-Meier analysis, univariate and multivariate Cox regression analysis were performed to estimate prognostic values of RILPL2 in the TCGA cohort, which revealed that increased level of RILPL2 was remarkably associated with better prognosis and could act as an independent prognostic biomarker in patients with EC. Moreover, correlation analysis of RILPL2 and tumor-infiltrating immune cells (TIICs) indicated that RILPL2 might play a critical role in regulating immune cell infiltration in EC and is related to immune response. Besides, high methylation level was a significant cause of low RILPL2 expression in EC. Subsequently, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were conducted to explore the RILPL2-involved underlying oncogenic mechanisms, and the results indicated that RILPL2 mainly regulated cell cycle. In conclusion, our findings provided evidence that downregulation of RILPL2 in EC is an indicator of adverse prognosis and RILPL2 may act as a promising target for the therapeutics of EC.
| S-EPMC8171931 | biostudies-literature
Project description:Gastric cancer (GC) is one of the leading causes of cancer-related deaths and shows high levels of heterogeneity. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis can arise in response to H. pylori, a primary carcinogen, and also in response to chemotherapy drugs. However, the prognostic evaluation of GC to pyroptosis is insufficient. Consensus clustering by pyroptosis-related regulators was used to classify 618 patients with GC from four GEO cohorts. Following Cox regression with differentially expressed genes, our prognosis model (PS-score) was built by LASSO-Cox analysis. The TCGA-STAD cohort was used as the validation set. ESTIMATE, CIBERSORTx, and EPIC were used to investigate the tumor microenvironment (TME). Immunotherapy cohorts by blocking PD1/PD-L1 were used to investigate the treatment response. The subtyping of GC based on pyroptosis-related regulators was able to classify patients according to different clinical traits and TME. The difference between the two subtypes identified in this study was used to develop a prognosis model which we named "PS-score." The PS-score could predict the prognosis of patients with GC and his/her overall survival time. A low PS-score implies greater inflammatory cell infiltration and better response of immunotherapy by PD1/PD-L1 blockers. Our findings provide a foundation for future research targeting pyroptosis and its immune microenvironment to improve prognosis and responses to immunotherapy.
| S-EPMC8226259 | biostudies-literature
Project description:BackgroundGlioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear.MethodsIn this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort.ResultsIn this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated.ConclusionWe established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma.
| S-EPMC8653573 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.Materials and methodsWe downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.ResultsA total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.ConclusionThe novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.
| S-EPMC10172511 | biostudies-literature
Project description:IntroductionGastric cancer (GC) is the fifth most prevalent cancer globally, with the third highest case fatality rate. Neutrophil extracellular traps (NETs) are a reticulated structure of DNA, histones, and antimicrobial peptides produced by active neutrophils that trap pathogens. Even though NETs are associated with poorer recurrence-free survival (RFS) and overall survival (OS), the specifics of this interaction between NETs and cancer cells are yet unknown.MethodsThe keywords "neutrophil extracellular traps and gastric cancer" were used in the GEO database for retrieval, and the GSE188741 dataset was selected to obtain the NETs-related gene. 27 NETs-related genes were screened by univariate Cox regression analysis (p < 0.05). 27 NETs-related genes were employed to identify and categorize NETs-subgroups of GC patients under the Consensus clustering analysis. 808 GC patients in TCGA-STAD combined with GES84437 were randomly divided into a training group (n = 403) and a test group (n = 403) at a ratio of 1:1 to validate the NETs-related signature.ResultsBased on Multivariate Cox regression and LASSO regression analysis to develop a NETs-related prognosis model. We developed a very specific nomogram to improve the NETs-clinical score's usefulness. Similarly, we also performed a great result in pan-cancer study with NETs-score. Low NETs scores were linked to higher MSI-H (microsatellite instability-high), mutation load, and immune activity. The cancer stem cell (CSC) index and chemotherapeutic treatment sensitivity were also connected to the NET score. Our comprehensive analysis of NETs in GC suggests that NETs have a role in the tumor microenvironment, clinicopathological features, and prognosis.DiscussionThe NETs-score risk model provides a basis for better prognosis and therapy outcomes in GC patients.
| S-EPMC10447905 | biostudies-literature
Project description:Background: Long noncoding RNAs (lncRNAs) have been discovered to play a regulatory role in genomic instability (GI), which participates in the carcinogenesis of various cancers, including hepatocellular carcinoma (HCC). We endeavored to establish a GI-derived lncRNA signature (GILncSig) as a potential biomarker and explore its impact on immune infiltration and prognostic significance. Methods: Combining expression and somatic mutation profiles from The Cancer Genome Atlas database, we identified GI-related lncRNAs and conducted functional analyses on co-expressed genes. Based on Cox regression analysis, a GILncSig was established in the training cohort (n = 187), and an independent testing patient cohort (n = 183) was used to validate its predictive ability. Kaplan-Meier method and receiver operating characteristic curves were adopted to evaluate the performance. The correlation between GI and immune infiltration status was investigated based on the CIBERSORT algorithm and single sample gene set enrichment analysis. In addition, a comprehensive nomogram integrating the GILncSig and clinicopathological variables was constructed to efficiently assess HCC patient prognosis in clinical applications. Results: A total of 88 GI-related lncRNAs were screened out and the functional analyses indicated diversified effects on HCC progression. The GILncSig was established using four independent lncRNAs (AC116351.1, ZFPM2-AS1, AC145343.1, and MIR210HG) with significant prognostic value (p < 0.05). Following evaluation with the GILncSig, low-risk patients had significantly better clinical outcomes than high-risk patients in the training cohort (p < 0.001), which was subsequently validated in the independent testing cohort. High-risk group exhibited more immunocyte infiltration including B cells memory, macrophages M0 and neutrophils and higher expression of HLA gene set and immune checkpoint genes. Compared to existing HCC signatures, the GILncSig showed better prognosis predictive performance [area under the curve (AUC) = 0.709]. Furthermore, an integrated nomogram was constructed and validated to efficiently and reliably evaluate HCC patient prognosis (3-years survival AUC = 0.710 and 5-years survival AUC = 0.707). Conclusion: The GILncSig measuring GI and impacting immune infiltration serves as a potential biomarker and independent predictor of HCC patient prognosis. Our results highlight further investigation of GI and HCC molecular mechanisms.
| S-EPMC8645863 | biostudies-literature
Project description:With high morbidity and mortality, colon cancer (CC) is considered as one of the most often diagnosed cancers around the world. M7G-related lncRNA may provide a regulatory function in the formation of CC, but the principle of regulation is still unclear. The purpose of this research was to establish a novel signature that may be used to predict survival and tumour immunity in CC patients. Data about CC in TCGA was collected for analysis, coexpression analysis and univariate Cox analysis were used to screen prognostic m7G-related lncRNAs. A consensus clustering analysis based on prognostic m7G-related lncRNAs was applied, and a prognosis model based on least absolute shrinkage and selection operator (LASSO) regression analysis was established. Independent prognostic analysis, nomogram, PCA, clinicopathological correlation analysis, TMB, survival analysis, immune correlation analysis, qRT-PCR and clinical therapeutic compound prediction were also applied. 90 prognostic m7G-related lncRNAs were found, GO and KEGG analysis showed that prognostic m7G-related lncRNAs were mainly related to cell transcription and translation. The results of the consensus clustering analysis revealed substantial disparities in survival prognosis and tumour immune infiltration between two clusters. We built a risk model with 21 signature m7G-related lncRNAs, patients in the high-risk group had a considerably poorer prognosis than those in the low-risk group. Independent prognostic analysis confirmed that patients' prognosis was linked to their tumour stage and risk score. PCA, subgroups with distinct clinicopathological characteristics were studied for survival, multi-index ROC curve, c-index curve, the survival analysis of TMB, and model comparison tested the reliability of risk model. A tumour immunoassay revealed a substantial difference in immune infiltration between high-risk and low-risk individuals. Five chemicals were eliminated, and qRT-PCR indicated that the four lncRNAs were expressed differently. Overall, m7G-related lncRNA is closely related to colon cancer and the 21 signature lncRNAs risk model can efficiently evaluate the prognosis of CC patients, which has a possible positive consequence for the future diagnosis and therapy of CC.
| S-EPMC9277109 | biostudies-literature
Project description:Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.
| S-EPMC9406778 | biostudies-literature