Identification of an Immune Score-Based Gene Panel with Prognostic Power for Oral Squamous Cell Carcinoma

Huang, Su-Hua; Li, Guosheng; Zhou, Xiaoting; Chen, Xiaoyi; Ying, Yang; Zhang, Xiao-Guohui; Liang, Yao; Li, Mingxuan; Chen, Gang; Huang, Zhi‐Guang; Dang, Yi‐Wu; Li, Jing; Li, Ping; Tang, Xiao‐Zhun; Rong, Minhua

doi:10.12659/msm.922854

Cited by 20 publications

(17 citation statements)

References 19 publications

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“…Studies have found unique advantages of immune genes as cancer prognostic indicators, including ovarian cancer, liver cancer, and pancreatic cancer [ 7 – 9 ]. In addition, a study reported that an immune gene panel was able to predict the prognosis of OSCC, but its prediction sensitivity was low and lacked molecular level verification [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Immune-Associated Gene Signature for Overall Survival Prediction in Patients with Oral Squamous Cell Carcinoma

Yang

Feng

Yuan

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Objective. To explore a novel Immune-associated gene signature for overall survival (OS) in patients with oral squamous cell carcinoma (OSCC). Methods. Expression profiles of genes and corresponding clinical materials of OSCC patients were obtained through the TCGA database. With a LASSO Cox regression model, a multigene signature was established to predict the OS of OSCC patients. Some molecular experiments including RNA interference, MTT, and Transwell assay were applied to verify the role of the risk gene FGF9 in OSCC. Results. 43 immune-related prognostic DEGs were identified in OSCC. A 17-gene signature was established to assign the patients to either a high-risk group (HG) or a low-risk group (LG). The HG presented a shorter OS than the LG ( P < 0.05 ). According to multivariate Cox regression analyses, the risk score was considered an independent factor for OS prediction (training set: HR = 3.485, 95% CI = 2.037–5.961, P < 0.001 ; test set: HR = 4.531, 95% CI = 2.120–9.682, P < 0.001 ). ROC curve-based analysis revealed the signature’s ability for prediction. According to functional analysis, the immune cell expression and immune function of the HG were significantly inhibited. After knocking down the high-risk gene FGF9, the migration, proliferation, and invasion capabilities of OSCC cells HSC6 were significantly suppressed ( P < 0.05 ). Conclusion. A novel immune-associated gene signature was identified for predicting the prognosis of OSCC. These risk genes show great potential as targets for OSCC treatment.

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Section: Introductionmentioning

confidence: 99%

A Novel Immune-Associated Gene Signature for Overall Survival Prediction in Patients with Oral Squamous Cell Carcinoma

Yang

Feng

Yuan

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…For example, Yao et al integrated four IRGs and node status to develop a prognostic model for head and neck squamous cell carcinoma (HNSCC) [18]. Huang et al strati ed patients with oral squamous cell carcinoma into high-and low-risk groups based on 9 IRGs [19]. However, there is on study based on IRGPs for oral cancer.…”

Section: Discussionmentioning

confidence: 99%

A Signature of Immune-related Gene Pairs (IRGPs) for Risk Stratification and Prognosis of Oral Cancer Patients

Tian²,

Hou

et al. 2022

Preprint

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Background:With low response to present immunotherapy, it is imperative to identify new immune-related biomarkers for more effective immunotherapies for oral cancer.Methods:RNA profile for 390 oral cancer patients and 32 normal samples were downloaded from the Cancer Genome Atlas (TCGA) database and differentially expressed genes (DEGs) were analyzed. Immune genesets from Immportrepository were overlapped with DEGs. After implementing univariate Cox analysisand the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, key immune-related gene pairs (IRGPs) among the overlappedDEGs for predicting the survival riskwere obtained. Then, the cutoff of risk score was calculated by receiver operating characteristic (ROC) curve to stratify oral cancer patients into high and low-risk groups. Multivariate Cox analysis was used to analyze independent prognostic indicator fororal cancer.Besides, infiltration of immune cells, functional annotation and mutation analysis of IRGPs were conducted.Biological function correlated with IRGPs were enriched by Gene Set Enrichment Analysis (GSEA) method. Results: We identified698 differentially expressed genes (DEGs)in response to oral cancer. 17IRGPs among the DEGs were identified and integrated into a risk score model. Patients in high-risk group have significantly worse prognosis than those in low-risk group. Meanwhile, IRGPs model was identified as an independent prognostic factor for oral cancer. Different infiltration pattern of immune cells were found between high- and low-risk groups that more types of T and B cells were enriched in low-risk group. More immune-related signaling pathways were highly enriched in low-risk group. And Tenascin C (TNC) was the most frequently mutated genes. We have developed a novel 17-IRGPs signature for risk stratification and prognostic predication of oral cancer.Conclusion: Our study provides a foundation for improved immunotherapy and prognosis and is beneficial to the individualized management of oral cancer patients.

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“…Pan et al have also proved that LINC00324 promoted the proliferation and metastasis of LUAD tissues and cells by the miR-615-5p/AKT1 pathway 32 . However, in the remaining orlncRNAs of 11 orlncRNAs (e.g., AC010980.2 [33][34][35][36] , AL365203.2 [37][38][39] , AL606489.1 40,34 , AC004687.1 [41][42][43] , HLA-DQB1-AS1 40,47 , AL590226.1 48 ), although there are few experiments to explore their relationship with the occurrence and development of malignant tumors, plenty of prognostic signatures have included them to predict the OS of patients with various malignant tumors.…”

Section: Discussionmentioning

confidence: 99%

Construction of an Algorithm Based on Oncosis-Related LncRNAs Comprising the Molecular Subtypes and a Risk Assessment Model in Lung Adenocarcinoma

Chen

Zhou

et al. 2022

Preprint

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As an important non-apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few researches have reported the relationship between oncosis and lung cancer. In this study, we established a oncosis-based algorithm comprised of the cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with LUAD. We selected 11 oncosis-related lncRNAs associated with the prognosis to establish the oncosis-based algorithm and divided the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low-risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low-risk patients tended to have higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD-1, CTLA-4, HAVCR2, and were more sensitive to ICIs, including PD-1/CTLA-4 inhibitors. The risk score had a significantly positive correlation with TMB. The survival curve of the novel oncosis-based algorithm suggested that low-risk patients in cluster2 have the most obvious survival advantage. The novel oncosis-based algorithm based on the 11 oncosis-related lncRNAs was established to investigate the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.

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Identification of an Immune Score-Based Gene Panel with Prognostic Power for Oral Squamous Cell Carcinoma

Cited by 20 publications

References 19 publications

A Novel Immune-Associated Gene Signature for Overall Survival Prediction in Patients with Oral Squamous Cell Carcinoma

A Novel Immune-Associated Gene Signature for Overall Survival Prediction in Patients with Oral Squamous Cell Carcinoma

A Signature of Immune-related Gene Pairs (IRGPs) for Risk Stratification and Prognosis of Oral Cancer Patients

Construction of an Algorithm Based on Oncosis-Related LncRNAs Comprising the Molecular Subtypes and a Risk Assessment Model in Lung Adenocarcinoma

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