Construction of a prognostic signature based on T-helper 17 cells differentiation-related genes for predicting survival and tumor microenvironment in head and neck squamous cell carcinoma.

T-helper 17 (Th17) cells significantly influence the onset and advancement of malignancies. This study endeavor focused on delineating molecular classifications and developing a prognostic signature grounded in Th17 cell differentiation-related genes (TCDRGs) using machine learning algorithms in head and neck squamous cell carcinoma (HNSCC). A consensus clustering approach was applied to The Cancer Genome Atlas-HNSCC cohort based on TCDRGs, followed by an examination of differential gene expression using the limma package. Machine learning techniques were utilized for feature selection and model construction, with validation performed using the GSE41613 cohort. The interplay between the predictive marker, immune landscape, immunotherapy response, drug sensitivity, and clinical outcomes was assessed, and a nomogram was constructed. Functional evaluations of TCDRGs were conducted through colony formation, transwell invasion, and wound healing assays. Two distinct HNSCC subtypes with significant differences in prognosis were identified based on 87 TCDRGs, indicating different levels of Th17 cell differentiation. Thirteen differentially expressed TCDRGs were selected and used to create a risk signature, T17I, using the random survival forest algorithm. This signature was associated with grade, chemotherapy, radiotherapy, T stage, and somatic mutations. It was revealed that there were differences in the immune response-related pathways between the high- and low-risk groups. Inflammatory pathways were significantly activated in the low-risk group. The T17I signature was associated with immune infiltration. Specifically, there was a higher infiltration of immune activation cells in the low-risk group, whereas the high-risk group had a higher infiltration of M2 macrophages. In addition, the T17I signature was significantly associated with drug sensitivity. A nomogram combining age, radiotherapy, and the T17I signature accurately predicted the prognosis of patients with HNSCC. Finally, in vitro experiments confirmed that knockdown of LAT gene expression promotes proliferation, metastasis, and invasion of HNSCC cells. In conclusion, this study successfully identified molecular subtypes and constructed a prognostic signature and nomogram based on TCDRGs in HNSCC, which may aid in personalized treatment strategies.

Chen S ，Wei P ，Wang G ，Wu F ，Zou J ... -  《-》

Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma.

In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head and neck squamous cell carcinoma (HNSCC). However, accurately predicting the treatment effectiveness of ICIs for patients at the same TNM stage remains a challenge. In this study, we first combined multi-omics data (mRNA, lncRNA, miRNA, DNA methylation, and somatic mutations) and 10 clustering algorithms, successfully identifying two distinct cancer subtypes (CSs) (CS1 and CS2). Subsequently, immune-regulated genes (IRGs) and machine learning algorithms were utilized to construct a consensus machine learning-driven prediction immunotherapy signature (CMPIS). Further, the prognostic model was validated and compared across multiple datasets, including clinical characteristics, external datasets, and previously published models. Ultimately, the response of different CMPIS patients to immunotherapy, targeted therapy, radiotherapy and chemotherapy was also explored. First, Two distinct molecular subtypes were successfully identified by integrating immunomics data with machine learning techniques, and it was discovered that the CS1 subtype tended to be classified as "cold tumors" or "immunosuppressive tumors", whereas the CS2 subtype was more likely to represent "hot tumors" or "immune-activated tumors". Second, 303 different algorithms were employed to construct prognostic models and the average C-index value for each model was calculated across various cohorts. Ultimately, the StepCox [forward] + Ridge algorithm, which had the highest average C-index value of 0.666, was selected and this algorithm was used to construct the CMPIS predictive model comprising 16 key genes. Third, this predictive model was compared with patients' clinical features, such as age, gender, TNM stage, and grade stage. The findings indicated that this prognostic model exhibited the best performance in terms of C-index and AUC values. Additionally, it was compared with previously published models and it was found that the C-index of CMPIS ranked in the top 5 among 94 models across the TCGA, GSE27020, GSE41613, GSE42743, GSE65858, and META datasets. Lastly, the study revealed that patients with lower CMPIS were more sensitive to immunotherapy and chemotherapy, while those with higher CMPIS were more responsive to radiation therapy and EGFR-targeted treatments. In summary, our study identified two CSs (CS1 and CS2) of HNSCC using multi-omics data and predicted patient prognosis and treatment response by constructing the CMPIS model with IRGs and 303 machine learning algorithms, which underscores the importance of immunotherapy biomarkers in providing more targeted, precise, and personalized immunotherapy plans for HNSCC patients, significantly contributing to the optimization of clinical treatment outcomes.

Yin J ，Xu L ，Wang S ，Zhang L ，Zhang Y ，Zhai Z ，Zeng P ，Grzegorzek M ，Jiang T ... -  《Scientific Reports》

Construction of lysosome-related prognostic signature to predict the survival outcomes and selecting suitable drugs for patients with HNSCC.

Lysosomes are digestive organelles responsible for endocytosis and autophagy. Recently, the malignancy and invasiveness head and neck squamous cell carcinoma (HNSCC) has been increasingly studied with the role of lysosomes. A list of lysosome-related genes were obtained from MSigDB. A Spearman correlation and univariate Cox regression analyses combined with differential expression analysis were conducted to detect differentially expressed lysosome-related genes related to prognosis. The prediction of prognostic signature was evaluated by plotting survival curve, ROC, and by developing a nomogram. Immune subtypes, infiltration of immune cells, GSVA, TIDE, IC50 of common chemotherapy and targeted therapy, GO, and KEGG function enrichment analyses were carried out to explore the immune microenvironment of the signature. We constructed a lysosome-related prognostic signature that could function as an independent prognostic indicator for patients with HNSCC. High-risk patients were better suited to receive Doxorubicin, Mitomycin C, Pyrimethamine, anti-PD-L1 and anti-CTLA-4 immunotherapy, whereas low-risk patients had sensitivity to Lapatinib. GO functional enrichment analysis showed that prognostic features were strongly associated with epidermis-related functions (e.g., epidermal cell differentiation, epidermal development, and keratinization). In addition, a KEGG function enrichment analysis revealed a potential relationship between the risk assessment model and cardiomyopathy. We constructed a prognostic signature based on lysosome-related genes and successfully predicted the survival outcome of HNSCC patients, which not only provides potential guidance for personalized treatment but also provides a new idea for precision treatment of HNSCC.

Cao B ，Gu S ，Shen Z ，Zhang Y ，Shen Y ... -  《-》

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Wang SY ，Wang YX ，Guan LS ，Shen A ，Huang RJ ，Yuan SQ ，Xiao YL ，Wang LS ，Lei D ，Zhao Y ，Lin C ，Wang CP ，Yuan ZP ... -  《BMC Medical Genomics》

Erratum: Eyestalk Ablation to Increase Ovarian Maturation in Mud Crabs.

《Jove-Journal of Visualized Experiments》