Identification of prognostic marker genes in head and neck squamous cell carcinoma: A study based on The Cancer Genome Atlas database and experimental validation.

BACKGROUND: Early detection and prognostic prediction are crucial in improving the survival of patients with head and neck squamous cell carcinoma (HNSCC). Therefore, we provided potential molecular markers in this study for early diagnosis and prognosis of this cancer based on The Cancer Genome Atlas (TCGA) database analysis and experimental validations. METHODS: Differentially expressed genes (DEGs) between HNSCC tumor and normal samples were identified by TCGA database-based analyses. Univariate and multivariate Cox regression analyses were applied, respectively, to identify survival-related DEGs and independent prognostic factors in HNSCC. Further, RT-qPCR was employed to verify expression of DEGs in cancer and adjacent tissues from HNSCC patients recruited in our hospital, in which we also clarified the correlation between candidate genes and clinicopathological characteristics and prognosis of HNSCC patients. RESULTS: TCGA data analyses yielded 59 DEGs. Cox analyses identified 13 candidate genes closely related to prognosis of HNSCC patients and established a five-gene signature comprising AC103702.2, LINC00941, RPL29, FOXL2, and CCL11. This five-gene signature could classify patients into high- and low-risk groups. The survival rate of the high-risk group was significantly lower than that of the low-risk group. Clinical tissue experiments further confirmed that AC103702.2, LINC00941, CCL11, and RPL29P19 genes were inversely associated with the prognosis of HNSCC patients, while CCL11 gene was positively associated. We also found that high-risk HNSCC patients presented a higher incidence of lymph node metastasis. CONCLUSION: Five prognostic marker genes (AC103702.2, LINC00941, CCL11, RPL29P19, and FOXL2) as a gene cluster may serve as prognostic marker genes in HNSCC.

Comprehensive analysis of ferroptosis-related genes in immune infiltration and prognosis in multiple myeloma.

Background: One particular type of cellular death that is known as ferroptosis is caused by the excessive lipid peroxidation. It is a regulated form of cell death that can affect the response of the tumor cells. Currently, it is not known if the presence of this condition can affect the prognosis of patients with multiple myeloma (MM). Methods: In this study, we studied the expression differences and prognostic value of ferroptosis-related genes (FRGs) in MM, and established a ferroptosis risk scoring model. In order to improve the prediction accuracy and clinical applicability, a nomogram was also established. Through gene enrichment analysis, pathways closely related to high-risk groups were identified. We then explored the differences in risk stratification in drug sensitivity and immune patterns, and evaluated their value in prognostic prediction and treatment response. Lastly, we gathered MM cell lines and samples from patients to confirm the expression of marker FRGs using quantitative real-time PCR (qRT-PCR). Results: The ability to predict the survival of MM patients is a challenging issue. Through the use of a risk model derived from ferroptosis, we were able to develop a more accurate prediction of the disease's prognosis. They were then validated by a statistical analysis, which showed that the model is an independent factor in the prognosis of MM. Patients of high ferroptosis risk scores had a much worse chance of survival than those in the low-risk groups. The calibration and power of the nomogram were also strong. We noted that the link between the ferroptosis risk score and the clinical treatment was suggested by the FRG's significant correlation with the immune checkpoint genes and the medication sensitivity. We validated the predictive model using qRT-PCR. Conclusion: We demonstrated the association between FRGs and MM, and developed a new risk model for prognosis in MM patients. Our study sheds light on the potential clinical relevance of ferroptosis in MM and highlights its potential as a therapeutic target for patients with this disease.

Construction and validation of a necroptosis-related lncRNAs prognosis signature of hepatocellular carcinoma.

Background: Immunotherapy has achieved remarkable success in treating advanced liver cancer. Current evidence shows that most of the available immune checkpoint inhibitor (ICB) treatments are suboptimal, and specific markers are needed for patients regarded as good candidates for immunotherapy. Necroptosis, a type of programmed cell death, plays an important role in hepatocellular carcinoma (HCC) progression and outcome. However, studies on the necroptosis-related lncRNA in HCC are scarce. In this view, the present study investigates the link among necroptosis-related lncRNA, prognosis, immune microenvironment, and immunotherapy response. Methods: Gene transcriptome and clinical data were retrieved from The Cancer Genome Atlas database. Pearson correlation analysis of necroptosis-related genes was performed to identify necroptosis-related lncRNAs. The Wilcoxon method was used to detect differentially expressed genes, and prognostic relevant lncRNAs were obtained by univariate Cox regression analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were utilized to perform functional enrichment analysis. Lasso-Cox stepwise regression analysis was employed to calculate risk score, which was involved in analyzing immune cells infiltration, immune checkpoints expression, and predicting immunotherapeutic efficacy. Quantitative RT-PCR (qRT-PCR) was performed to detect the expression pattern of lncRNA in cell lines. Results: The 10 lncRNAs generated in this study were used to create a prognostic risk model for HCC and group patients into groups based on risk. High-risk patients with HCC have a significantly lower OS rate than low-risk patients. Multivariate Cox regression analysis showed that risk score is an independent risk factor for HCC with high accuracy. Patients in the high-risk group exhibited a weaker immune surveillance and higher expression level of immune checkpoint molecules. In terms of drug resistance, patients in the low-risk group were more sensitive to sorafenib. The OS-related nomogram was constructed to verify the accuracy of our model. Finally, quantitative RT-PCR experiments were used to verify the expression patterns of candidate genes. Conclusion: The lncRNA signature established herein, encompassing 10 necroptosis-related lncRNAs, is valuable for survival prediction and holds promise as prognostic markers for HCC.