Background: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer worldwide. Its highly aggressive and heterogeneous nature and complex tumor microenvironment result in variable prognosis and immunotherapeutic outcomes for patients with HNSCC. Neurotrophic factor-related genes (NFRGs) play an essential role in the development of malignancies but have rarely been studied in HNSCC. The aim of this study was to develop a reliable prognostic model based on NFRGs for assessing the prognosis and immunotherapy of HNSCC patients and to provide guidance for clinical diagnosis and treatment.Methods: Based on the TCGA-HNSC cohort in the Cancer Genome Atlas (TCGA) database, expression profiles of NFRGs were obtained from 502 HNSCC samples and 44 normal samples, and the expression and prognosis of 2601 NFRGs were analyzed. TGCA-HNSC samples were randomly divided into training and test sets (7:3). GEO database of 97 tumor samples was used as the external validation set. One-way Cox regression analysis and Lasso Cox regression analysis were used to screen for differentially expressed genes significantly associated with prognosis. Based on 18 NFRGs, lasso and multivariate Cox proportional risk regression were used to construct a prognostic risk scoring system. ssGSEA was applied to analyze the immune status of patients in high- and low-risk groups.Results: The 18 NFRGs were considered to be closely associated with HNSCC prognosis and were good predictors of HNSCC. The multifactorial analysis found that the NFRGs signature was an independent prognostic factor for HNSCC, and patients in the low-risk group had higher overall survival (OS) than those in the high-risk group. The nomogram prediction map constructed from clinical characteristics and risk scores had good prognostic power. Patients in the low-risk group had higher levels of immune infiltration and expression of immune checkpoints and were more likely to benefit from immunotherapy.Conclusion: The NFRGs risk score model can well predict the prognosis of HNSCC patients. A nomogram based on this model can help clinicians classify HNSCC patients prognostically and identify specific subgroups of patients who may have better outcomes with immunotherapy and chemotherapy, and carry out personalized treatment for HNSCC patients.

BackgroudSkin cutaneous melanoma (SKCM) is an extremely metastatic form of skin cancer. However, there are few valuable molecular biomarkers, and accurate diagnosis is still a challenge. Hypercoagulable state encourages the infiltration and development of tumor cells and is significantly associated with poor prognosis in cancer patients. However, the use of a coagulation-related gene (CRG) signature for prognosis in SKCM, on the other hand, has yet to be determined.MethodWe used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves. Moreover, the Gene Expression Omnibus (GEO), GSE54467 was used as an independent validation. The correlation between risk score and clinicopathological characteristics, tumor microenvironment (TME), and immunotherapy was further analyzed.ResultsTo develop a prognostic model, seven CRGs in SKCM patients related to overall survival (OS) were selected: ANG, C1QA, CFB, DUSP6, KLKB1, MMP7, and RABIF. According to the Kaplan-Meier survival analysis, an increased OS was observed in the low-risk group than in the high-risk group (P<0.05). Immunotherapy was much more beneficial in the low-risk group, as per immune infiltration, functional enrichment, and immunotherapy analysis.ConclusionsThe prognosis of SKCM patients may now be predicted with the use of a CRG prognostic model, thus guiding the development of treatment plans for SKCM patients and promoting OS rates.

Background: Necroptosis has been identified recently as a newly recognized programmed cell death that has an impact on tumor progression and prognosis, although the necroptosis-related gene (NRGs) potential prognostic value in skin cutaneous melanoma (SKCM) has not been identified. The aim of this study was to construct a prognostic model of SKCM through NRGs in order to help SKCM patients obtain precise clinical treatment strategies.Methods: RNA sequencing data collected from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed and prognostic NRGs in SKCM. Depending on 10 NRGs via the univariate Cox regression analysis usage and LASSO algorithm, the prognostic risk model had been built. It was further validated by the Gene Expression Omnibus (GEO) database. The prognostic model performance had been assessed using receiver operating characteristic (ROC) curves. We evaluated the predictive power of the prognostic model for tumor microenvironment (TME) and immunotherapy response.Results: We constructed a prognostic model based on 10 NRGs (FASLG, TLR3, ZBP1, TNFRSF1B, USP22, PLK1, GATA3, EGFR, TARDBP, and TNFRSF21) and classified patients into two high- and low-risk groups based on risk scores. The risk score was considered a predictive factor in the two risk groups regarding the Cox regression analysis. A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group.Conclusions: This risk score signature could be applied to assess the prognosis and classify low- and high-risk SKCM patients and help make the immunotherapeutic strategy decision.