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Exploration and Validation of a Novel Inflammatory Response-Associated Gene Signature to Predict Osteosarcoma Prognosis and Immune Infiltration.

ABSTRACT: 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.

SUBMITTER: Fu Y

PROVIDER: S-EPMC8668229 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and accounts for the fourth common cause of cancer-related deaths. Recently, pyroptosis has been revealed to be involved in the progression of multiple cancers. However, the role of pyroptosis in the HCC prognosis remains elusive. Methods: The clinical information and RNA-seq data of the HCC patients were collected from the TCGA-LIHC datasets, and the differential pyroptosis-related genes (PRG) were firstly explored. The univariate Cox regression and consensus clustering were applied to recognize the HCC subtypes. The prognostic PRGs were then submitted to the LASSO regression analysis to build a prognostic model in the TCGA training cohort. We further evaluated the predictive model in the TCGA test cohort and ICGC validation cohort (LIRI-JP). The accuracy of prediction was validated using the Kaplan-Meier (K-M) and receiver operating characteristic (ROC) analyses. The single-sample gene set enrichment analysis (ssGSEA) was used to determine the differential immune cell infiltrations and related pathways. Finally, the expression of the prognostic genes was validated by qRT-PCR in vivo and in vitro. Results: We identified a total of 26 differential PRGs, among which three PRGs comprising GSDME, GPX4, and SCAF11 were subsequently chosen for constructing a prognostic model. This model significantly distinguished the HCC patients with different survival years in the TCGA training, test, and ICGC validation cohorts. The risk score of this model was confirmed as an independent prognostic factor. A nomogram was generated indicating the survival years for each HCC patient. The ssGSEA demonstrated several tumor-infiltrating immune cells to be remarkably associated with the risk scores. The qRT-PCR results also showed the apparent dysregulation of PRGs in HCC. Finally, the drug sensitivity was analyzed, indicating that Lenvatinib might impact the progression of HCC via targeting GSDME, which was also validated in human Huh7 cells. Conclusion: The PRG signature comprised of GSDME, GPX4, and SCAF11 can serve as an independent prognostic factor for HCC patients, which would provide further evidence for more clinical and functional studies.

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.

Project description:BackgroundIncreasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients' prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis.MethodsThe differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan-Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature.ResultsTwo genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group.ConclusionThe current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.

Project description:BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated.MethodsIn this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively.ResultsAn inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort.ConclusionA novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.

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Exploration and Validation of a Novel Inflammatory Response-Associated Gene Signature to Predict Osteosarcoma Prognosis and Immune Infiltration.

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