Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma.
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ABSTRACT:
SUBMITTER: Zhu C
PROVIDER: S-EPMC8724436 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Changes in the oral microbiome, particularly Fusobacterium nucleatum, are associated with oral squamous cell carcinoma (OSCC). F. nucleatum has been reported to modulate local immunity in cancers. We aimed to assess the association between intratumoral F. nucleatum and clinico-pathological features, relapse, and overall survival (OS) in two independent cohorts of patients with OSCC, and to explore the interplay with immune-related genes. We retrospectively analyzed tissue samples from a first cohort of 122 patients with head and neck squamous cell carcinoma, including 61 OSCC (cohort #1), and a second cohort of 90 additional OSCC (cohort #2). We then performed a sensitivity analysis on the merged cohort of OSCC patients (N = 151). F. nucleatum 16S rRNA gene sequences were quantified using real-time quantitative PCR. The presence of gram-negative bacteria and macrophages was confirmed by LPS and CD163 immunostainings, respectively. F. nucleatum positivity was associated with older age, less alcohol and combined alcohol plus tobacco consumption, and less frequent lymph node invasion. There was a trend for a lower recurrence rate in F. nucleatum-positive cases, with less metastatic relapses compared to F. nucleatum-negative tumors, and significantly longer OS, relapse-free and metastasis-free survival. F. nucleatum status was independently associated with OS in multivariate analysis. Immune-related gene and immunohistochemistry analyses showed that gram-negative bacteria load inversely correlated with M2 macrophages. F. nucleatum-associated OSCC has a specific immune microenvironment, is more frequent in older, non-drinking patients, and associated with a favorable prognosis.
| S-EPMC8041800 | biostudies-literature
Project description:To identify an apoptosis-related gene (ARG) prediction model for oral squamous cell carcinoma (OSCC), we analyzed and validated the data from TCGA and GEO, respectively. Kaplan-Meier survival analysis and ROC curves showed a good prognostic ability of the model both in the internal training set and in the external testing set. Furthermore, we built a nomogram using these ARGs to forecast the survival probability of OSCC patients. Moreover, we evaluated the rate of immune cells infiltrating in the tumor samples and found obvious, different patterns between the high and low risk groups. GO and KEGG analyses demonstrated multiple molecular biological processes and signaling pathways connecting with this prognostic model in OSCC. The expression of these risk genes in clinical specimens was higher in the non-survival patients than in the well-survival patients by immunohistochemical staining analysis. In conclusion, we established a signature made up of six risk apoptosis-related genes to predict the survival rate of OSCC. These genes could also be targets for the treatment of OSCC.
| S-EPMC9235536 | biostudies-literature
Project description:Esophageal carcinoma (EC) is a common malignant cancer worldwide. Esophageal squamous cell carcinoma (ESCC), the main type of EC, is difficult to treat because of the widespread morbidity, high fatality rates, and low quality of life caused by postoperative complications and no specific molecular target. In this study, we screened genes to establish a prognostic model for ESCC. The transcriptome expression profiles of 81 ESCC tissues and 340 normal esophageal mucosal epithelium tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts. The transcriptome expression datasets of 19 esophageal squamous carcinoma cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE). The R software Limma package was used to identify 6,231 differentially expressed genes and 647 differentially expressed immune-related genes between normal and ESCC tissues. Gene functional analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was used to screen out 18 immune-related prognostic genes. We then established the prognostic and risk signature using these genes, and the patients were divided into low-risk and high-risk groups. Compared with high-risk group patients, the low-risk group patients had longer overall survival. M1 macrophages and resting dendritic cells were differentially distributed between the low-risk and high-risk groups and were related to patient survival. We also examined the functional immune cell and immune molecule levels in low-risk and high-risk group patients, with significant differences in the tumor microenvironment between the two groups. To further verify the accuracy of the prognostic risk model, we performed area under the ROC curve (AUC) analysis. The AUC value was 0.931 for the prognostic risk, which was better than the microsatellite instability (MSI) and Tumor Immune Dysfunction and Exclusion (TIDE) scores. In conclusion, we found 18 immune-related prognostic genes related to the occurrence of ESCC and established a prognostic model for predicting disease severity.
| S-EPMC9043362 | biostudies-literature
Project description:Background: Ferroptosis is an iron-dependent programmed cell death (PCD) form that plays a crucial role in tumorigenesis and might affect the antitumor effect of radiotherapy and immunotherapy. This study aimed to investigate distinct ferroptosis-related genes, their prognostic value and their relationship with immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The differentially expressed ferroptosis-related genes in HNSCC were filtered based on multiple public databases. To avoid overfitting and improve clinical practicability, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were performed to construct a prognostic risk model. Moreover, a nomogram was constructed to forecast individual prognosis. The differences in tumor mutational burden (TMB), immune infiltration and immune checkpoint genes in HNSCC patients with different prognoses were investigated. The correlation between drug sensitivity and the model was firstly analyzed by the Pearson method. Results: Ten genes related to ferroptosis were screened to construct the prognostic risk model. Kaplan-Meier (K-M) analysis showed that the prognosis of HNSCC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001), and the area under the curve (AUC) of the 1-, 3- and 5-year receiver operating characteristic (ROC) curve increased year by year (0.665, 0.743, and 0.755). The internal and external validation further verified the accuracy of the model. Then, a nomogram was build based on the reliable model. The C-index of the nomogram was superior to a previous study (0.752 vs. 0.640), and the AUC (0.729 vs. 0.597 at 1 year, 0.828 vs. 0.706 at 3 years and 0.853 vs. 0.645 at 5 years), calibration plot and decision curve analysis (DCA) also shown the satisfactory predictive capacity. Furthermore, the TMB was revealed to be positively correlated with the risk score in HNSCC patients (R = 0.14; P < 0.01). The differences in immune infiltration and immune checkpoint genes were significant (P < 0.05). Pearson analysis showed that the relationship between the model and the sensitivity to antitumor drugs was significant (P < 0.05). Conclusion: Our findings identified potential novel therapeutic targets, providing further potential improvement in the individualized treatment of patients with HNSCC.
| S-EPMC8591309 | biostudies-literature
Project description:The aim of this study was to investigate whether there is a correlation between the expressions of four matrix metalloproteinases (MMPs): MMP-2, MMP-7, MMP-9 and MMP-13, and the TNM (tumour-node-metastasis) stages of oral squamous cell carcinoma (OSCC); and to explore the implication of these MMPs in OSCC dissemination. Samples from 61 patients diagnosed with oropharyngeal tumour were studied by immunohistochemistry against MMP-2, MMP-7, MMP-9 and MMP-13. The assessment of immunoreactivity was semi-quantitative. The results showed that MMP-2 and MMP-9 had similar expression patterns in the tumour cells with no changes in the immunoreactivity during tumour progression. MMP-9 always had the highest expression, whereas that of MMP-2 was moderate. MMP-7 showed a significant decrease in expression levels during tumour evolution. MMP-13 had constant expression levels within stage T2 and T3, but showed a remarkable decline in immunoreactivity in stage T4. No significant differences in the MMPs immunoreactivity between tumour cells and stroma were observed. Although strong evidence for the application of MMPs as reliable predictive markers for node metastasis was not acquired, we believe that combining patients' MMPs expression intensity and clinical features may improve the diagnosis and prognosis. Strong evidence for the application of MMPs as reliable predictive markers for node metastasis was not acquired. Application of MMPs as prognostic indicators for the malignancy potential of OSCC might be considered in every case of tumour examination. We believe that combining patients' MMPs expression intensity and clinical features may improve the process of making diagnosis and prognosis.
| S-EPMC4433935 | biostudies-other
Project description:BackgroundPredicting the outcome of oral squamous cell carcinoma (OSCC) is challenging due to its diverse nature and intricate causes. This research explores how lysosome-associated genes (LRGs) might forecast overall survival (OS) and correlate with immune infiltration in OSCC patients.MethodsWe analyzed OSCC patients' LRGs' mRNA expression data and clinical details from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through univariate Cox regression, we pinpointed LRGs with prognostic potential. A signature comprising 12 LRGs linked to prognosis was developed via the Least Absolute Shrinkage and Selection Operator (LASSO) in a training dataset. Patients were classified as higher or lower risk based on their risk scores, and the prognostic independence of the risk score was assessed using multivariate analysis. The model's robustness and precision were confirmed through bioinformatics in the GEO test set. Differential gene expression analysis between risk groups highlighted functional disparities, while various immune evaluation methods elucidated immune differences.ResultsThe prognostic framework utilized 12 LRGs (SLC46A3, MANBA, NEU1, SDCBP, BRI3, TMEM175, CD164, GPC1, SFTPB, TPP1, Biglycan (BGN) and TMEM192), showing that higher risk was associated with poorer OS. This set of genes independently predicted OS in OSCC, linking LRGs to cellular adhesion and extracellular matrix involvement. Initial assessments using ssGSEA and CIBERSORT suggested that the adverse outcomes in the higher-risk cohort may be tied to immune system deregulation.ConclusionTwelve-LRGs signature has been identified for OSCC prognosis prediction, offering novel directions for lysosome-targeted therapies against OSCC.
| S-EPMC10900434 | biostudies-literature
Project description:Purpose Oral squamous cell carcinoma (OSCC) is the most common oral cancer worldwide. Pyroptosis is a type of programmed cell death mediated by caspase, accompanied by an inflammatory response, and plays an important role in cancer progression. The purpose of this study was to explore and identify potential biomarkers and further elucidate the potential role of cell pyroptosis in OSCC. Methods We regarded the samples from The Cancer Genome Atlas database as a training dataset, screened differentially expressed genes (DEGs), and further screened out OSCC phenotypic characteristic genes by using weighted gene co-expression network analysis. The analysis of 42 known pyroptosis-related genes showed that Psuch genes were widely expressed, mutated, and methylated in OSCC samples. Results Through correlation analysis, we identified our OSCC pyroptosis-related DEGs. To further evaluate the prognostic value of pyroptosis-related regulators, we constructed a seven gene-based prognostic signature using Cox univariate analysis and least absolute shrinkage and selection operator Cox regression analysis. Meanwhile, we found that patients in the low-risk group had higher immune infiltration. Moreover, our results also indicated significant differences in sensitivity to cisplatin and gefitinib between the high-risk and low-risk groups. Conclusion Our study successfully constructed the pyroptosis-related prognostic signature, which might play a potential prediction role in OSCC prognosis. Our findings also suggested that pyroptosis-related regulators might be novel biomarkers for tumor diagnosis and treatment in OSCC.
| S-EPMC9437919 | biostudies-literature
Project description:Background: Cuproptosis has been recognized as a novel regulatory cell death, which has been confirmed to promote the occurrence and development of tumors. However, whether cuproptosis-related lncRNA has an impact on the prognosis of squamous cell carcinoma of the head and neck (HNSCC) is still unclear. Methods: In total, 501 HNSCC tumor samples and 44 normal were downloaded from the TCGA database. Cuproptosis-related lncRNAs were obtained by co-expressed analysis. We got prognostic lncRNA that was associated with cuproptosis by using univariate Cox regression analysis and LASSO Cox regression. Then we constructed and validated the prognostic signature of HNSCC and analyzed the immune landscape of the signature. Results: The Prognostic Signature is based on 10 cuproptosis-related lncRNAs including AC090587.1, AC004943.2, TTN-AS1, AL162458.1, AC106820.5, AC012313.5, AL132800.1, WDFY3-AS2, CDKN2A-DT, and AL136419.3. The results of overall survival, risk score distribution, and survival status in the low-risk group were better than those in the high-risk group. In addition, all immune checkpoint genes involved were significantly different between the two risk groups (p < 0.05). The risk score was positively correlated with Eosinophils. M0 and M2 phenotype macrophages, mast cells activated, NK cells activated, and negatively related with B cells naive, mast cells resting, plasma cells, CD8T cells, T cells follicular helper, T cells regulatory (Tregs). Consensus clustering was identified in molecular subtypes of HNSC. More high-risk samples concentrated in Cluster1, which had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and Single Nucleotide Polymorphisms (SNP) alternation than Cluster2. Conclusion: Our study elucidated the correlation between cuproptosis-related lncRNA with prognosis and immune landscape of HNSCC, which may provide references for further research on the exploration of the mechanism and functions of the prognosis for HNSCC.
| S-EPMC9356288 | biostudies-literature
Project description:The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, very limited robust and reliable immunological biomarkers have been developed that are capable of estimating prognosis in HNSCC patients. In this study, we aimed to identify the effects of novel immune-related gene signatures (IRGs) that can predict HNSCC prognosis. Based on gene expression profiles and clinical data of HNSCC patient cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, a total of 439 highly variable expressed immune-related genes (including 239 upregulated and 200 downregulated genes) were identified by using differential gene expression analysis. Pathway enrichment analysis indicated that these immune-related differentially expressed genes were enriched in inflammatory functions. After process screening in the training TCGA cohort, six immune-related genes (PLAU, STC2, TNFRSF4, PDGFA, DKK1, and CHGB) were significantly associated with overall survival (OS) based on the LASSO Cox regression model. Integrating these genes with clinicopathological features, a multivariable model was built and suggested better performance in determining patients' OS in the testing cohort, and the independent validation cohort. In conclusion, a well-established model encompassing both immune-related gene signatures and clinicopathological factors would serve as a promising tool for the prognostic prediction of HNSCC.
| S-EPMC8249947 | biostudies-literature
Project description:Objective: Immune landscape is a key feature that affects cancer progression, survival, and treatment response. Herein, this study sought to comprehensively characterize the immune-related genes (IRGs) in oral squamous cell carcinoma (OSCC) and conduct an immune-related risk score (IRS) model for prognosis and therapeutic response prediction. Methods: Transcriptome profiles and follow-up data of OSCC cohorts were curated from TCGA, GSE41613, and IMvigor210 datasets. An IRS model was established through univariate Cox, Random Survival Forest, and multivariate Cox analyses. Prognostic significance was evaluated with Kaplan-Meier curves, ROC, uni- and multivariate Cox, and subgroup analyses. A nomogram was conducted and assessed with C-index, ROC, calibration curves, and decision curve analyses. Immune cell infiltration and immune response were estimated with ESTIMATE and ssGSEA methods. Results: An IRS model was constructed for predicting the overall survival and disease-free survival of OSCC, containing MASP1, HBEGF, CCL22, CTSG, LBP, and PLAU. High-risk patients displayed undesirable prognosis, and the predictive efficacy of this model was more accurate than conventional clinicopathological indicators. Multivariate Cox analyses demonstrated that this model was an independent risk factor. The nomogram combining IRS, stage, and age possessed high clinical application values. The IRS was positively associated with a nonflamed tumor microenvironment. Moreover, this signature enabled to predict immunotherapeutic response and sensitivity to chemotherapeutic agents (methotrexate and paclitaxel). Conclusion: Collectively, our study developed a robust IRS model with machine learning method to stratify OSCC patients into subgroups with distinct prognosis and benefits from immunotherapy, which might assist identify biomarkers and targets for immunotherapeutic schemes.
| S-EPMC9289552 | biostudies-literature