Development of a Novel Immune Infiltration-Based Gene Signature to Predict Prognosis and Immunotherapy Response of Patients With Cervical Cancer.
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ABSTRACT:
SUBMITTER: Yu S
PROVIDER: S-EPMC8446628 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND Cervical cancer is one of the most common malignances among women globally. This study aimed to construct a novel immune-related signature to predict the prognosis and immune infiltration of cervical cancer. MATERIAL AND METHODS Transcriptomic profiles and corresponding clinical information of cervical cancer patients were obtained from The Cancer Genome Atlas (TCGA) database and GEO database. The hub immune-related genes were screened and selected using Cox regression analysis and LASSO regression analysis. A novel signature was established based on the expression levels and corresponding coefficients of the selected hub immune-related genes. Kaplan-Meier survival curve and ROC curve illustrated the prognostic value of this novel signature in cervical cancer. The predictive accuracy and stability of this novel signature were confirmed in the validation cohort, internal testing set and external testing set. Then, a nomogram was constructed to predict individual survival probability of cervical cancer patient. The association between the risk scores of novel signature and immune infiltration was investigated through single-sample gene set enrichment analysis (ssGSEA). RESULTS Ten hub immune-related genes (TFRC, SPP1, CAMP, CSF2, TUBB3, ZAP70, CHIT1, LEPR, DLL4, and DES) were selected to construct a novel signature. The risk score of this novel signature could be an independent prognostic factor in cervical cancer, which divided patients into high-risk and low-risk groups. The patients in high-risk groups showed significantly worse overall survival rates than those in low-risk groups in all training and validation cohorts (all P<0.05). A nomogram model was constructed based on the risk score of the novel signature and other clinical characteristics, which achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities (concordance index=0.813). Furthermore, gene enrichment analysis revealed that the novel signature was closely related with immunology. The novel signature was negatively correlated with the infiltration of most immune cell types, especially T cell subsets (P<0.001). CONCLUSIONS The novel signature could comprehensively predict the prognosis and immune infiltration of cervical cancer. It may provide new insights for the precise treatment in cervical cancer.
| S-EPMC10066621 | biostudies-literature
Project description:BackgroundExcept for B7-CD28 family members, more novel immune checkpoints are being discovered. They are closely associated with tumor immune microenvironment and regulate the function of many immune cells. Various cancer therapeutic studies targeting these novel immune checkpoints are currently in full swing. However, studies concerning novel immune checkpoints phenotypes and clinical significance in lung adenocarcinoma (LUAD) are still limited.MethodsWe enrolled 1883 LUAD cases from nine different cohorts. The samples from The Cancer Genome Atlas (TCGA) were used as a training set, whereas seven microarray data cohorts and an independent cohort with 102 qPCR data were used for validation. The immune profiles and potential mechanism of the system were also explored.ResultsAfter univariate Cox proportional hazards regression and stepwise multivariable Cox analysis, a novel immune checkpoints-based system (LTA, CD160, and CD40LG) were identified from the training set, which significantly stratified patients into high- and low-risk groups with different survivals. Furthermore, this system has been well validated in different clinical subgroups and multiple validation cohorts. It also acted as an independent prognostic factor for patients with LAUD in different cohorts. Further exploration suggested that high-risk patients exhibited distinctive immune cells infiltration and suffered an immunosuppressive state. Additionally, this system is closely linked to various classical immunotherapy biomarkers.Conclusionwe constructed a novel immune checkpoints-based system for LUAD, which predicts prognosis and immunotherapeutic implications. We believe that these findings will not only aid in clinical management but will also shed some light on screening appropriate patients for immunotherapy.
| S-EPMC9310422 | biostudies-literature
Project description:Background:Inflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature. Methods:The inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan-Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry. Results:The IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients' samples revealed the same result. Conclusion:The novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.
| S-EPMC8668229 | biostudies-literature
Project description:BackgroundThe immune microenvironment is of great significance in cervical cancer. However, there is still a lack of systematic research on the immune infiltration environment of cervical cancer.MethodsWe obtained cervical cancer transcriptome data and clinical information from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, evaluated the immune microenvironment of cervical cancer, determined immune subsets, constructed an immune cell infiltration scoring system, screened key immune-related genes, and performed single-cell data analysis and cell function analysis of key genes.ResultsWe combined the TCGA and GEO data sets and obtained three different immune cell populations. We obtained two gene clusters, extracted 119 differential genes, and established an immune cell infiltration (ICI) scoring system. Finally, three key genes, IL1B, CST7, and ITGA5, were identified, and single-cell sequencing data were mined to distribute these key genes in different cell types. By up-regulating CST7 and down-regulating IL1B and ITGA5, cervical cancer cells' proliferation ability and invasion ability were successfully reduced.ConclusionWe conducted a comprehensive assessment of the state of the tumor immune microenvironment in cervical cancer, constructed the ICI scoring system, and identified the ICI scoring system as a potential indicator of susceptibility to immunotherapy for cervical cancer, identifying key genes suggesting that IL1B, CST7, and ITGA5 play an essential role in cervical cancer.
| S-EPMC10037308 | biostudies-literature
Project description:Background: Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor immunity formation and patient-tailored treatment optimization of lung adenocarcinoma (LUAD) is still unclear. Methods: RNA sequencing and survival data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database for model training. The patients with LUAD in GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081 were used for validation. The proofed cuproptosis-related genes were extracted from the previous studies. The Pearson correlation was applied to select cuproptosis-related lncRNAs. We chose differentially expressed cuproptosis-related lncRNAs in the tumor and normal tissues and allowed them to go to a Cox regression and a LASSO regression for a lncRNA signature that predicts the LUAD prognosis. Kaplan-Meier estimator, Cox model, ROC, tAUC, PCA, nomogram predictor, decision curve analysis, and real-time PCR were further deployed to confirm the model's accuracy. We examined this model's link to other regulated cell death forms. Applying TMB, immune-related signatures, and TIDE demonstrated the immunotherapeutic capabilities of signatures. We evaluated the relationship of our signature to anticancer drug sensitivity. GSEA, immune infiltration analysis, and function experiments further investigated the functional mechanisms of the signature and the role of immune cells in the prognostic power of the signature. Results: An eight-lncRNA signature (TSPOAP1-AS1, AC107464.3, AC006449.7, LINC00324, COLCA1, HAGLR, MIR4435-2HG, and NKILA) was built and demonstrated owning prognostic power by applied to the validation cohort. Each signature gene was confirmed differentially expressed in the real world by real-time PCR. The eight-lncRNA signature correlated with 2321/3681 (63.05%) apoptosis-related genes, 11/20 (55.00%) necroptosis-related genes, 34/50 (68.00%) pyroptosis-related genes, and 222/380 (58.42%) ferroptosis-related genes. Immunotherapy analysis suggested that our signature may have utility in predicting immunotherapy efficacy in patients with LUAD. Mast cells were identified as key players that support the predicting capacity of the eight-lncRNA signature through the immune infiltrating analysis. Conclusion: In this study, an eight-lncRNA signature linked to cuproptosis was identified, which may improve LUAD management strategies. This signature may possess the ability to predict the effect of LUAD immunotherapy. In addition, infiltrating mast cells may affect the signature's prognostic power.
| S-EPMC10475834 | biostudies-literature
Project description:Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified.
| S-EPMC7785859 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is one of the world's most lethal malignant tumors with a poor prognosis. Growing evidence has been demonstrating that immune-related long non-coding RNAs (lncRNAs) are relevant to the tumor microenvironment (TME) and can help assess the effects of immunotherapy and evaluate one's prognosis. This study aims to identify an immune-related lncRNA signature for the prospective assessment of the immunotherapy and prognosis in HCC.MethodWe downloaded HCC RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database. We first used ESTIMATE to evaluate the TME. Then, we conducted a cox regression analysis to construct a prognostic signature and the riskScore. We then applied the univariate Cox regression, multivariate Cox regression, principal components analysis (PCA), receiver operating characteristic (ROC) curve, and stratification analyses to confirm our previous assessments. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNA signature and immune checkpoint genes. Finally, we used the quantitative real-time polymerase chain reaction (qRT-PCR) assays to demonstrate the expression of the six lncRNAs.ResultsWe identified six immune-related lncRNAs - MSC-AS1, AC145207.5, SNHG3, AL365203.2, AL031985.3, NRAV - which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The univariate Cox regression, multivariate Cox regression, ROC, and stratification analyses confirmed that the immune-related six-lncRNA signature was a novel independent prognostic factor in HCC patients. The high-risk group and low-risk group illustrated contrasting distributions in PCA. The GSEA suggested that the immune-related six-lncRNA signature was involved in the immune-related biological processes and pathways. Besides, the immune-related six-lncRNA signature was associated with the infiltration of immune cells. Furthermore, it was linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the qRT-PCR demonstrated that the six lncRNAs were significantly differentially expressed in HCC cell lines and normal hepatic cell lines.ConclusionIn summary, we identified an immune-related six-lncRNA signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with hepatocellular carcinoma.
| S-EPMC8283691 | biostudies-literature
Project description:Background: Globally, hepatocellular carcinoma (HCC) is the sixth most frequent malignancy with a high incidence and a poor prognosis. Immune cell infiltration (ICI) underlies both the carcinogenesis and immunogenicity of tumors. However, a comprehensive classification system based on the immune features for HCC remains unknown. Methods: The HCC dataset from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts was used in this study. The ICI patterns of 571 patients were characterized using two algorithms: the patterns were determined based on the ICI using the ConsensusClusterPlus package, and principal component analysis (PCA) established the ICI scores. Differences in the immune landscape, biological function, and somatic mutations across ICI scores were evaluated and compared, followed by a predictive efficacy evaluation of ICI scores for immunotherapy by the two algorithms and validation using an external immunotherapy cohort. Results: Based on the ICI profile of the HCC patients, three ICI patterns were identified, including three subtypes having different immunological features. Individual ICI scores were determined; the high ICI score subtype was characterized by enhanced activation of immune-related signaling pathways and a significantly high tumor mutation burden (TMB); concomitantly, diminished immunocompetence and enrichment of pathways associated with cell cycle and RNA degradation were found in the low ICI score subtype. Taken together, our results contribute to a better understanding of an active tumor and plausible reasons for its poor prognosis. Conclusion: The present study reveals that ICI scores may serve as valid prognostic biomarkers for immunotherapy in HCC.
| S-EPMC8657761 | biostudies-literature
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: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