Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma.

PANoptosis is considered a novel type of cell death that plays important roles in tumor progression. In this study, we applied machine learning algorithms to explore the relationships between PANoptosis-related lncRNAs (PRLs) and head and neck squamous cell carcinoma (HNSCC) and established a neural network model for prognostic prediction. Information about the HNSCC cohort was downloaded from the TCGA database, and the differentially expressed prognostic PRLs between tumor and normal samples were assessed in patients with different tumor subtypes via nonnegative matrix factorization (NMF) analysis. Subsequently, five kinds of machine-learning algorithms were used to select the core PRLs across the subtypes, and the interactive features were pooled into a neural network model to establish a PRL-related risk score (PLRS) system. Survival differences were compared via Kaplan‒Meier analysis, and the predictive effects were assessed with the areas under the ROCs. Moreover, functional enrichment analysis, immune infiltration, tumor mutation burden (TMB) and clinical therapeutic response were also conducted to further evaluate the novel predictive model. A total of 347 PRLs were identified, 225 of which were differentially expressed between tumor and normal samples. Patients were divided into two clusters via NMF analysis, in which cluster 1 had a better prognosis and more immune cells and functional infiltrates. With the application of five machine learning algorithms, we selected 13 interactive PRLs to construct the predictive model. The AUCs for the ROCs in the entire set were 0.735, 0.740 and 0.723, respectively. Patients in the low-PLRS group exhibited a better prognosis, greater immune cell enrichment, greater immune function activation, lower TMB and greater sensitivity to immunotherapy. In this study, we established a novel neural network prognostic model to predict survival and identify tumor subtypes in HNSCC patients. This novel assessment system is useful for prediction, providing ideas for clinical treatment.

Wang Z ，Cheng L ，Huang J ，Shen Y ... -  《-》

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 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 ... -  《-》

A novel disulfidptosis-related LncRNA prognostic risk model: predicts the prognosis, tumor microenvironment and drug sensitivity in esophageal squamous cell carcinoma.

Ye C ，Xu C ，Tang Y ，Qi Y ，Peng X ，Wei G ，Jiang L ... -  《BMC GASTROENTEROLOGY》

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response.

Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC. To develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model's predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined. From analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs). Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical. The construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.

Li SZ ，Sun HY ，Tian Y ，Zhou LQ ，Zhou T ... -  《Frontiers in Immunology》