A senescence-related lncRNA signature predicts prognosis and reflects immune landscape in HNSCC.

OBJECTIVE: Long noncoding RNAs (lncRNAs) regulate cancer cell senescence in many cancers. However, their specific involvement in head and neck squamous cell carcinoma (HNSCC) remains unclear. We are looking for an ingenious prognostic signature that utilizes senescence-related lncRNAs (SRlncRNAs) to predict prognosis and provide insights into the immune landscape in HNSCC. MATERIALS AND METHODS: HNSCC clinical and Cellular senescence genes information were collected from The Cancer Genome Atlas and Human Aging Genomic Resources. Then we performed Cox and Lasso regression to locate SRlncRNAs related to the prognosis of HNSCC and built a predictive signature. Further, prognosis assessment, potential mechanisms, and immune status were assessed by Kaplan-Meier analysis, Gene Set Enrichment Analysis (GSEA), and CIBERSORT, respectively. RESULTS: A prognosis prediction model based on sixteen SRlncRNAs was identified and internally validated. Then, patients with high-risk scores suffered an unfavorable overall survival (All p < 0.05). The risk score, age, and stage were independent prognostic parameters (all p < 0.001). Our model has good predictive ability (The AUC (area under the curves) 1-year = 0.707, AUC3-year = 0.748 and AUC5-year = 0.779). Subsequently, GESA revealed SRlncRNAs regulated immune responses. Patients in the high-risk group had higher tumor mutation burden and Tumor Immune Dysfunction and Exclusion but lower levels of 37 immune checkpoint genes, immune scores, and immune cells like CD8 + T cells, follicular helper T cells, and regulatory T cells. CONCLUSIONS: A prognostic model based on SRlncRNAs is the potential target for improving immunotherapy outcomes for HNSCC.

Development and validation of a model based on immunogenic cell death related genes to predict the prognosis and immune response to bladder urothelial carcinoma.

Background: Immunogenic cell death (ICD) has been categorized as a variant of regulated cell death that is capable of inducing an adaptive immune response. A growing body of evidence has indicated that ICD can modify the tumor immune microenvironment by releasing danger signals or damage-associated molecular patterns (DAMPs), potentially enhancing the efficacy of immunotherapy. Consequently, the identification of biomarkers associated with ICD that can classify patients based on their potential response to ICD immunotherapy would be highly advantageous. Therefore the goal of the study is to better understand and identify what patients with bladder urothelial carcinoma (BLCA) will respond to immunotherapy by analyzing ICD signatures and investigate ICD-related prognostic factors in the context of BLCA. Methods: The data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases regarding BLCA and normal samples was categorized based on ICD-related genes (IRGs). Specifically, we conducted an immunohistochemical (IHC) experiment to validate the expression levels of Calreticulin (CALR) in both tumor and adjacent tissues, and evaluated its prognostic significance using the Kaplan-Meier (KM) curve. Subsequently, the samples from TCGA were divided into two subtypes using consensus clustering. To obtain a more comprehensive comprehension of the biological functions, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The calculation of immune landscape between two subtypes was performed through ESTIMATE and CIBERSORT. Risk models were constructed using Cox and Lasso regression and their prognosis predictive ability was evaluated using nomogram, receiver operating characteristic (ROC), and calibration curves. Finally, Tumor Immune Dysfunction and Exclusion (TIDE) algorithms was utilized to predict the response to immunotherapy. Results: A total of 34 IRGs were identified, with most of them exhibiting upregulation in BLCA samples. The expression of CALR was notably higher in BLCA compared to the adjacent tissue, and this increase was associated with an unfavorable prognosis. The differentially expressed genes (DEGs) associated with ICD were linked to various immune-related pathways. The ICD-high subtypes exhibited an immune-activated tumor microenvironment (TME) compared to the ICD-low subtypes. Utilizing three IRGs including CALR, IFNB1, and IFNG, a risk model was developed to categorize BLCA patients into high- and low-risk groups. The overall survival (OS) was considerably greater in the low-risk group compared to the high-risk group, as evidenced by both the TCGA and GEO cohorts. The risk score was identified as an independent prognostic parameter (all p < 0.001). Our model demonstrated good predictive ability (The area under the ROC curve (AUC), AUC1-year= 0.632, AUC3-year= 0.637, and AUC5-year =0.653). Ultimately, the lower risk score was associated with a more responsive immunotherapy group. Conclusion: The potential of the ICD-based risk signature to function as a marker for evaluating the prognosis and immune landscape in BLCA suggests its usefulness in identifying the suitable population for effective immunotherapy against BLCA.

An Immunogenic Cell Death-Related Gene Signature Reflects Immune Landscape and Predicts Prognosis in Melanoma Independently of BRAF V600E Status.

Immunogenic cell death (ICD) is a type of regulated cell death that can activate adaptive immune response, and its ability to reshape the tumor microenvironment via multiple mechanisms may contribute to immunotherapy. The treatment options for patients with skin cutaneous melanoma (SKCM) vary based on BRAF V600E statuses. However, all standard treatments include immunotherapy. Therefore, it is critical to identify ICD-associated signatures that can help classify patients according to benefits from ICD immunotherapy. In this study, data on melanoma samples with BRAF V600E mutation (BRAF V600E-mutant melanoma) and melanoma samples with wild-type BRAF V600E alleles (BRAF V600E WT melanoma) were collected from The Cancer Genome Atlas (TCGA) database. The ICD-related (ICD-high and ICD-low) subgroups of patients with BRAF V600E WT melanoma were established via consensus clustering. The analyses of survival, differentially expressed genes (DEGs), functional annotation, and immune landscape were performed in these two subgroups. Results showed that ICD-high subgroup was correlated with a positive overall survival (OS) and active tumor immune landscape. A model comprising seven prognosis ICD-related gene biomarkers was developed. Survival analysis and receiver operating characteristic (ROC) curve evaluation in both cohorts with BRAF V600E WT and BRAF V600E-mutant melanoma showed an accurate prognostic estimation of ICD-related risk signature. There was a correlation between immune cell infiltration and immunotherapy response and risk score. Thus, the ICD risk signature was closely associated with the tumor's immune microenvironment. Our results may provide insights to further individualize and improve precision therapeutic decision-making in BRAF V600E-mutant and WT melanoma.

FAM3D as a Prognostic Indicator of Head and Neck Squamous Cell Carcinoma Is Associated with Immune Infiltration.

Background: Globally, head and neck squamous cell carcinoma (HNSCC) is a common malignant tumor with high morbidity and mortality. Hence, it is important to find effective biomarkers for the diagnosis and prediction of the prognosis of patients with HNSCC. FAM3D had been proven to be vital in other cancers. However, its predictive and therapeutic value in HNSCC is unclear. Therefore, it is valuable to explore the association between the expression level of FAM3D and its impacts on the prognosis and tumor microenvironment in HNSCC. Methods: The Cancer Genome Atlas (TCGA) dataset, Genotype-Tissue Expression (GTEx) dataset, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, and The Human Protein Atlas (THPA) website were used to assess HNSCC expressions in tumor and nontumor tissues. Then, we further conducted immunohistochemistry experiment as internal cohort to validate the same results. The Cox regression analysis, Kaplan-Meier analysis, and nomograms were performed to find the predictive prognostic value of FAM3D in HNSCC patients and its relationship with the clinicopathological features in HNSCC. The Gene Expression Omnibus (GEO) dataset was utilized to externally verify the prognosis value of FAM3D in HNSCC. Gene Set Enrichment Analysis (GESA) was applied to search the molecular and biological functions of FAM3D. The association between FAM3D and immune cell infiltration was investigated with the Tumor Immune Estimating Resource, version 2 (TIMER2). The relationships between FAM3D expression and tumor microenvironment (TME) scores, immune checkpoints, and antitumor compound half-maximal inhibitory concentration predictions were also explored. Results: In different datasets, FAM3D mRNA and protein levels were all significantly lower in HNSCC tissues than in normal tissues, and they were strongly inversely associated with tumor grade, stage, lymph node metastasis, and T stage. Patients with high-FAM3D-expression displayed better prognosis than those with low-FAM3D-expression. FAM3D was also determined to be a suitable biomarker for predicting the prognosis of patients with HNSCC. This was externally validated in the GEO dataset. As for gene and protein level, the functional and pathway research results of FAM3D indicated that it was enriched in alteration of immune-related pathways in HNSCC. The low-expression group had higher stromal and ESTIMATE scores by convention than the high-expression group. FAM3D expression were found to be positively correlated with immune infiltrating cells, such as cancer-associated fibroblasts, myeloid-derived suppressor cells, macrophage cells, T cell CD8+ cells, regulatory T cells, and T cell follicular helper cells. FAM3D's relationships with immune checkpoints and sensitivity to antitumor drugs were also investigated. Conclusion: Our study explored the impact of FAM3D as a favorable prognostic marker for HNSCC on the tumor immune microenvironment from multiple perspectives. The results may provide new insights into HNSCC-targeted immunotherapy.

Construction and validation of a prognostic model based on stage-associated signature genes of head and neck squamous cell carcinoma: a bioinformatics study.

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignancy of epithelial origin and with poor prognosis. Exploring the biomarkers and prognostic models that can contribute to early tumor detection is meaningful. A comprehensive analysis was conducted according to the stage-related signature genes of HNSCC, and a prognostic model was developed to validate their ability to predict the prognosis. Methods: The transcriptome profiles and clinical information of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) respectively. mRNA expressions of differentially expressed genes (DEGs) were analyzed in stage I-II patients and stage III-IV patients from TCGA by R packages. A protein-protein interaction (PPI) network and core-gene network map were constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to examine pathway enrichment. Kaplan-Meier, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were applied to establish a stage-associated signature model. A Spearman analysis was conducted to examine the correlations between the characteristic genes and immune cell infiltration. Kaplan-Meier analysis and a receiver operating characteristic (ROC) curve were used to test the effectiveness of the model. Univariate multivariate Cox regression analyses were used to assess whether the risk score was an independent prognostic indicator for HNSCC. Results: In TCGA cohort, 5 genes (i.e., BRINP1, IL17A, ALB, FOXA2, and ZCCHC12) in the constructed prognostic risk model were associated with prognosis. Patients in the low-risk group had a better prognosis outcome than those in the high-risk group. The predictive power was good because all the area under the curve (AUC) of the risk score was higher than 0.6. Risk score [hazard ratio (HR) =1.985; P<0.001] was an independent risk factor for the prognosis of HNSCC. The results in the GEO cohort were consistent with those in the TCGA cohort. Conclusions: We constructed and verified a prognostic risk model of stage-related signature genes for HNSCC based on the GEO and TCGA data. Due to the good predictive accuracy of this model, the prognosis of and the tumor immune cell infiltration with patients can be estimated.

Preparative separation of six antimycin A components from antimycin fermentation broth by high-speed counter-current chromatography.

A method of using high-speed counter-current chromatography (HSCCC) was established for preparative isolation and purification of antimycin A components from antimycin fermentation broth. Six antimycin A components were successfully purified for the first time by HSCCC with a two-phase solvent system composed of n-hexane-ethyl acetate-methanol-water (5:2:4:1, by volume). Total of 20mg antimycin A(4)(a or b), 25mg antimycin A(3)(a or b), 21mg antimycin A(8)(a or b), 34mg antimycin A(2)(a or b), 26mg antimycin A(1)(a or b) and 34mg antimycin A(1)(a or b) with the purities of 93.2, 98.6, 96.2, 94.1, 94.9 and 96.7%, respectively, determined by high-performance liquid chromatography (HPLC), were yielded from 200mg crude sample only in one HSCCC run.

Application of silver ion in the separation of macrolide antibiotic components by high-speed counter-current chromatography.

Three macrolide antibiotic components - ascomycin, tacrolimus and dihydrotacrolimus - were separated and purified by silver ion high-speed counter-current chromatography (HSCCC). The solvent system consisted of n-hexane-tert-butyl methyl ether-methanol-water (1:3:6:5, v/v) and silver nitrate (0.10mol/l). The silver ion acted as a pi-complexing agent with tacrolimus because of its extra side double bond compared with ascomycin and dihydrotacrolimus. This complexation modified the partition coefficient values and the separation factors of the three components. As a result, ascomycin, tacrolimus and dihydrotacrolimus were purified from 150mg extracted crude sample with purities of 97.6%, 98.7% and 96.5%, respectively, and yields over 80% (including their tautomers). These results cannot be achieved with the same solvent system but without the addition of silver ion.