Identifying prognostic biomarkers in oral squamous cell carcinoma: an integrated single-cell and bulk RNA sequencing study on mitophagy-related genes.

Oral squamous cell carcinoma (OSCC) has an extremely poor prognosis. Recent studies have suggested that mitophagy-related genes (MRGs) are closely correlated with the development and occurrence of cancer, but the role they play in oral cancer has not yet been explained.We conducted a comprehensive analysis of integrated single-cell and bulk RNA sequencing (RNA-seq) data retrieved from Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database. Multiple methods were combined to provide a comprehensive understanding of the genetic expression patterns and biology of OSCC, such as analysis of pseudotime series, CellChat cell communication, immune infiltration, Gene Ontology (GO), LASSO Cox regression, gene set variation analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), Tumor Mutation Burden (TMB) and drug sensitivity assessments. The findings of this study demonstrated significantly greater activity of MRGs in NK cells than in other cells in OSCC. A reliable prognostic model was developed using 12 candidate genes strongly associated with mitochondrial autophagy. T stage, N stage and risk score were revealed as independent prognostic factors. Distinctively enriched pathways and immune cells were observed in different risk groups. Notably, low-risk patients were more responsive to chemotherapy. In addition, a nomogram model with excellent predictive ability was established by combining the risk scores and clinical features. The activity of MRGs suggest the potential for the development of new targeted therapies. The construction of a robust prognostic model also provides reference value for individualized prediction and clinical decision-making in patients with OSCC.

Li M ，Wei Y ，Huang W ，Wang C ，He S ，Bi S ，Hu S ，You L ，Huang X ... -  《Scientific Reports》

Screening and identification of autophagy-related biomarkers for oral squamous cell carcinoma (OSCC) via integrated bioinformatics analysis.

Increasing evidences have showed that autophagy played a significant role in oral squamous cell carcinoma (OSCC). Purpose of our study was to explore the prognostic value of autophagy-related genes (ATGs) and screen autophagy-related biomarkers for OSCC. RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database following extracting ATG expression profiles. Then, differentially expressed analysis was performed in R software and a risk score model according to ATGs was established. Moreover, comprehensive bioinformatics analyses were used to screen autophagy-related biomarkers which were later verified in OSCC tissues and cell lines. A total of 232 ATGs were extracted, and 37 genes were differentially expressed in OSCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that these genes were mainly located in autophagosome membrane and associated with autophagy. Furthermore, the risk score on basis of ATGs was identified as potential independent prognostic biomarker. Moreover, ATG12 and BID were identified as potential autophagy-related biomarkers of OSCC. This study successfully constructed a risk model, and the risk score could predict the prognosis of OSCC patients accurately. Moreover, ATG12 and BID were identified as two potential independent prognostic autophagy-related biomarkers and might provide new OSCC therapeutic targets.

Huang GZ ，Lu ZY ，Rao Y ，Gao H ，Lv XZ ... -  《-》

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics.

Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome. This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database. The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making. In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

Zhang J ，Ma C ，Qin H ，Wang Z ，Zhu C ，Liu X ，Hao X ，Liu J ，Li L ，Cai Z ... -  《BMC Medical Genomics》

Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics.

Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC. Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate. Of 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients. We successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC.

Zhang YY ，Mao MH ，Han ZX 《-》

Significance of NKX2-1 as a biomarker for clinical prognosis, immune infiltration, and drug therapy in lung squamous cell carcinoma.

This study was performed to determine the biological processes in which NKX2-1 is involved and thus its role in the development of lung squamous cell carcinoma (LUSC) toward improving the prognosis and treatment of LUSC. Raw RNA sequencing (RNA-seq) data of LUSC from The Cancer Genome Atlas (TCGA) were used in bioinformatics analysis to characterize NKX2-1 expression levels in tumor and normal tissues. Survival analysis of Kaplan-Meier curve, the time-dependent receiver operating characteristic (ROC) curve, and a nomogram were used to analyze the prognosis value of NKX2-1 for LUSC in terms of overall survival (OS) and progression-free survival (PFS). Then, differentially expressed genes (DEGs) were identified, and Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were used to clarify the biological mechanisms potentially involved in the development of LUSC. Moreover, the correlation between the NKX2-1 expression level and tumor mutation burden (TMB), tumor microenvironment (TME), and immune cell infiltration revealed that NKX2-1 participates in the development of LUSC. Finally, we studied the effects of NKX2-1 on drug therapy. To validate the protein and gene expression levels of NKX2-1 in LUSC, we employed immunohistochemistry(IHC) datasets, The Gene Expression Omnibus (GEO) database, and qRT-PCR analysis. NKX2-1 expression levels were significantly lower in LUSC than in normal lung tissue. It significantly differed in gender, stage and N classification. The survival analysis revealed that high expression of NKX2-1 had shorter OS and PFS in LUSC. The multivariate Cox regression hazard model showed the NKX2-1 expression as an independent prognostic factor. Then, the nomogram predicted LUSC prognosis. There are 51 upregulated DEGs and 49 downregulated DEGs in the NKX2-1 high-level groups. GO, KEGG and GSEA analysis revealed that DEGs were enriched in cell cycle and DNA replication.The TME results show that NKX2-1 expression was positively associated with mast cells resting, neutrophils, monocytes, T cells CD4 memory resting, and M2 macrophages but negatively associated with M1 macrophages. The TMB correlated negatively with NKX2-1 expression. The pharmacotherapy had great sensitivity in the NKX2-1 low-level group, the immunotherapy is no significant difference in the NKX2-1 low-level and high-level groups. The analysis of GEO data demonstrated concurrence with TCGA results. IHC revealed NKX2-1 protein expression in tumor tissues of both LUAD and LUSC. Meanwhile qRT-PCR analysis indicated a significantly lower NKX2-1 expression level in LUSC compared to LUAD. These qRT-PCR findings were consistent with co-expression analysis of NKX2-1. We conclude that NKX2-1 is a potential biomarker for prognosis and treatment LUSC. A new insights of NKX2-1 in LUSC is still needed further research.

Lin H ，Wang J ，Shi Q ，Wu M ... -  《PeerJ》