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:BackgroundInflammatory 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.MethodsThe 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.ResultsThe 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.ConclusionThe 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 response in the tumor microenvironment (TME) plays a crucial role in cancer progression and recurrence. We aimed to develop an immune-related gene (IRG) signature to improve prognostic predictive power and reveal the immune infiltration characteristics of pancreatic ductal adenocarcinoma (PDAC).MethodsThe Cancer Genome Atlas (TCGA) PDAC was used to construct a prognostic model as a training cohort. The International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO) databases were set as validation datasets. Prognostic genes were screened by using univariate Cox regression. Then, a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating the relative subsets of RNA transcripts (CIBERSORT) and estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithms were used to characterize tumor immune infiltrating patterns. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict immunotherapy responsiveness.ResultsA prognostic signature based on five IRGs (MET, ERAP2, IL20RB, EREG, and SHC2) was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts. Multivariate Cox regression analysis demonstrated that this signature had an independent prognostic value. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve at 1, 3, and 5 years of survival were 0.724, 0.702, and 0.776, respectively. We further demonstrated that our signature has better prognostic performance than recently published ones and is superior to traditional clinical factors such as grade and tumor node metastasis classification (TNM) stage in predicting survival. Moreover, we found higher abundance of CD8+ T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC, and predicted a higher proportion of immunotherapeutic responders in the low-risk group.ConclusionsWe constructed an optimal prognostic model which had independent prognostic value and was comprehensively validated in external PDAC databases. Additionally, this five-genes signature could predict immune infiltration characteristics. Moreover, the signature helped stratify PDAC patients who might be more responsive to immunotherapy.
| S-EPMC9172649 | biostudies-literature
Project description:Liver cancer is an extraordinarily heterogeneous malignant disease. The tumor microenvironment (TME) and tumor-associated macrophages (TAMs) are the major drivers of liver cancer initiation and progression. It is critical to have a better understanding of the complicated interactions between liver cancer and the immune system for the development of cancer immunotherapy. Based on the gene expression profiles of tumor immune infiltration cells (TIICs), upregulated genes in TAMs and downregulated genes in other types of immune cells were identified as macrophage-specific genes (MSG). In this study, we combined MSG, immune subtypes, and clinical information on liver cancer to develop a tumor immune infiltration macrophage signature (TIMSig). A four-gene signature (S100A9, SLC22A15, TRIM54, and PPARGC1A) was identified as the TAM-related prognostic genes for liver cancer, independent of multiple clinicopathological parameters. Survival analyses showed that patients with low TIMSig had a superior survival rate than those with high TIMSig. Additionally, clinical immunotherapy response and TIMSig was observed as highly relevant. In addition, TIMSig could predict the response to chemotherapy. Collectively, the TIMSig could be a potential tool for risk-stratification, clinical decision making, treatment planning, and oncology immunotherapeutic drug development.
| S-EPMC9479109 | 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:LncRNAs and tumor microenvironment (TME) exert an important effect in antitumor immunity. Nonetheless, the role of m6A-related lncRNA clustering patterns in prognosis, TME and immunotherapy of cervical cancer (CC) remains unknown. Here, based on 7 m6A-related prognostic lncRNAs obtained from TCGA-CC dataset, two m6AlncRNA clustering patterns were determined. m6AlncRNA clusterA was characterized by immune cell infiltrates and immune activation. m6AlncRNA clusterB was characterized by enrichment of immune evasion and tumorigenic activation pathways as well as survival and clinical stage disadvantage. Then, principal component analysis algorithms were used to construct m6AlncRNAscore based on prognostic differentially expressed genes between two m6AlncRNA clusters to quantify m6AlncRNA clustering patterns. m6AlncRNAscore was an independent prognostic protective factor. Higher Th2 and Treg cells and enrichment of immunosuppressive pathways were observed in the low-m6AlncRNAscore group, with poorer survival. High-m6AlncRNAscore was characterized by increased infiltration of activated CD8 T cell, enrichment of immune activation pathways, lower IL-10 and TGF-beta1 levels, and higher immunophenscore values, indicating inflamed TME and better anti-tumor immunotherapy efficacy. Quantitative Real-Time Polymerase Chain Reaction was used for detection of m6A-related prognostic lncRNAs. Collectively, we identified two m6AlncRNA clustering patterns which play a nonnegligible role in the prognosis, TME heterogeneity and immunotherapy of CC patients.
| S-EPMC9568557 | biostudies-literature
Project description:Multiple myeloma (MM) is a highly heterogeneous and incurable disease. Inflammation plays a vital role in cancer genesis and progression. However, the relationship between inflammatory response-related genes (IRRGs) and the prognosis of MM patients remains unknown. We constructed a IRRGs prognosis model by least absolute shrinkage and selection operator regression analysis. Moreover, clinical multivariate regression was performed to identify clinical implications. Gene set enrichment analysis was implemented to conduct its biological properties. CIBERSORT deconvolution algorithm was utilized to calculate the immune cell infiltration in different risk groups. The flow cytometry was utilized to perform protein expression of prognostic gene. A Six-IRRGs (VCAM1, RGS1, KIT, CD81, BLNK, and BIRC3) prognostic risk model was successfully constructed and validated. The risk model was an independent predictor for overall survival. Enrichment analysis revealed autophagy and PI3K-Akt signaling pathways were enriched in the high-risk group. Furthermore, we found CD81 widely impacted on the infiltration of immune cells, especially on monocytes and macrophages2. At last, the role of CD81 in MM was confirmed to be an adverse prognostic factor in clinical. Our study explores the potential application value of IRRGs in MM. These findings may provide new insights into the treatment for MM patients.
| S-EPMC10821848 | 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:BackgroundLung cancer is a highly aggressive disease and the leading cause of cancer-related deaths. Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. As a type of programmed cell death, anoikis serves a key role in tumor metastasis. However, as few studies have focused on anoikis and prognostic indicators in LUAD, in this study, we constructed an anoikis-related risk model to explore how anoikis could influence the tumor microenvironment (TME), clinical treatment, and prognosis in LUAD patients; we aimed to provide new insight for future research.MethodsUsing patient data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized the 'limma' package to select differentially expressed genes (DEGs) associated with anoikis and then they were divided into 2 clusters with consensus clustering. Risk models were constructed with least absolute shrinkage and selection operator (LASSO) Cox regression (LCR). Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curves were performed to assess the independent risk factors for different clinical characteristics, including age, sex, disease stage, grade, and their associated risk scores. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to explore the biological pathways in our model. The effectiveness of clinical treatment was detected according to tumor immune dysfunction and exclusion (TIDE), The Cancer Immunome Atlas (TCIA), and IMvigor210.ResultsOur model was found to divide LUAD patients into high- and low-risk groups well, in which high risk groups had poor overall survival (OS), indicating that risk score could be an independent risk factor to predict the prognosis of LUAD patients. Interestingly, we found that anoikis could not only influence the extracellular organization but also play great roles in immune infiltration and immunotherapy, which might provide a new insight for future research.ConclusionsThe risk model constructed in this study can benefit to predict patient survival. Our results provided new potential treatment strategies.
| S-EPMC10089838 | biostudies-literature