Background Histone acetylation-related lncRNAs (HARlncRNAs) play significant roles in various cancers, but their impact on lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop a new HARlncRNA-based prognostic model for LUAD and to explore its potential biological mechanisms. Methods We identified 77 histone acetylation genes based on previous studies. HARlncRNAs related to prognosis were screened by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator regression (LASSO). Afterward, a prognostic model was established based on the screened HARlncRNAs. We analysed the relationship between the model and immune cell infiltration characteristics, immune checkpoint molecule expression, drug sensitivity, and tumour mutational burden (TMB). Finally, the entire sample was divided into three clusters to further distinguish between hot and cold tumours. Results A seven-HARlncRNA-based prognostic model was established for LUAD. The area under the curve (AUC) of the risk score was the highest among all the analysed prognostic factors, indicating the accuracy and robustness of the model. The patients in the high-risk group were predicted to be more sensitive to chemotherapeutic, targeted, and immunotherapeutic drugs. It was worth noting that clusters could effectively identify hot and cold tumours. In our study, clusters 1 and 3 were considered hot tumours that were more sensitive to immunotherapy drugs. Conclusion We developed a risk-scoring model based on seven prognostic HARlncRNAs that promises to be a new tool for evaluating the prognosis and efficacy of immunotherapy in patients with LUAD.

/pdf/histone-acetylation-related-incrna-potential-biomarkers-for-3o2xh6q2.pdf

Histone acetylation-related IncRNA: Potential biomarkers for predicting prognosis and immune response in lung adenocarcinoma, and distinguishing hot and cold tumours

Abstract Objectives Anoikis plays an active role in the metastasis and progression of many tumors and is emerging as a new target for tumor therapy. We aimed to develop an anoikis-related risk model to assess the prognosis of patients with bladder urothelial carcinoma (BLCA) and to explore its potential application value in immunotherapy. Methods Patient expression data and clinical data were obtained from GEO and TCGA database. Lasso regression was used to obtain a risk model and the clinical efficacy of risk model was evaluated with Cox regression, calibration curves, nomogram diagram, and receiver operating characteristics (ROC). Next, GSEA analysis was performed to estimate potential biological pathways for ARGS. The tumor microenvironment (TME) was also assessed, including cancer-associated fibroblast (CAF), CIBERSORT, XCELL, tumor immune exclusion, and tumor-associated macrophage (TAM). Then, ggpubr and ggplot2 packages were utilized to compare immune checkpoint expression discrepancies in different risk groups. Then, we also discussed the survival relevance of ARGS combined with immune checkpoints using survival and survminer packages and evaluated the sensitivity of immunotherapy for ARGS through the cancer immunome atlas (TCIA) and IMvigor210 cohort. Results 15 anoikis genes were identified to construct prognostic ARGS. ARGS can effectively divide BLCA cases into 2 groups with different clinical outcomes and reflect different TME. It was obvious that patients in the high-risk group could not benefit from immunotherapy. Conclusions ARGS can be used to stratify hazards and predict prognosis events in patients with BLCA and give remarkable guidance for personalized and precise immunotherapy.

/pdf/anoikis-related-gene-signature-as-novel-prognostic-biomarker-l11kpvw0.pdf

Anoikis-related gene signature as novel prognostic biomarker to predict immunotherapy with bladder urothelial carcinoma

Prostate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear. 

Telomerase related molecular subtype and risk model reveal immune activity and evaluate prognosis and immunotherapy response in prostate cancer

Huangqin decoction (HD) is a traditional Chinese medicine used for complementary therapy of liver cancer. However, its molecular mechanism of action is still unclear. After determining the active ingredients and targets of HD and identifying the genes related to liver cancer, a protein-protein interaction (PPI) network of the overlapping genes. The Metascape platform was used to perform gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis and the PubChem database, SwissADME database, PyMOL software, and AutoDock software were used to perform molecular docking. The Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA ) database were used to evaluate the expression levels, survival analysis, and pathological stage. The Cancer Genome Atlas (TCGA) database was used to extract the clinical and RNA-seq data of liver cancer patients. The R packages were used to analyse clinical data. Network pharmacology and molecular docking revealed the potential molecular mechanisms of HD in the treatment of liver cancer. The active components of HD were quercetin, kaempferol, wogonin, baicalein, naringenin, formononetin, isorhamnetin, and medicarpin. HD modulated the expression of AKT1, TP53, TNF, IL6, CTNNB1, VEGFA, and ESR1. HD treats liver cancer in a multi-component, multi-target, and multi-pathway manner. Our findings encourage further studies on the mechanisms underlying the therapeutic effects of HD in liver cancer. The proof-of-concept approach enable future studies on the molecular mechanisms of traditional Chinese medicine and contribute to the development of novel therapeutic targets. 

Therapeutic targets and molecular mechanisms of Huangqin decoction in liver cancer: a network pharmacology and molecular docking approach

Background Clear cell renal cell carcinoma (ccRCC) is the most frequently occurring malignant tumor within the kidney cancer subtype. It has low sensitivity to traditional radiotherapy and chemotherapy, the optimal treatment for localized ccRCC has been surgical resection, but even with complete resection the tumor will be eventually developed into metastatic disease in up to 40% of localized ccRCC. For this reason, it is crucial to find early diagnostic and treatment markers for ccRCC. Methods We obtained anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome dataset. The anoikis-related risk model was constructed based on 12 anoikis-related lncRNAs (ARlncRNAs) and verified by principal component analysis (PCA), Receiver operating characteristic (ROC) curves, and T-distributed stochastic neighbor embedding (t-SNE), and the role of the risk score in ccRCC immune cell infiltration, immune checkpoint expression levels, and drug sensitivity was evaluated by various algorithms. Additionally, we divided patients based on ARlncRNAs into cold and hot tumor clusters using the ConsensusClusterPlus (CC) package. Results The AUC of risk score was the highest among various factors, including age, gender, and stage, indicating that the model we built to predict survival was more accurate than the other clinical features. There was greater sensitivity to targeted drugs like Axitinib, Pazopanib, and Sunitinib in the high-risk group, as well as immunotherapy drugs. This shows that the risk-scoring model can accurately identify candidates for ccRCC immunotherapy and targeted therapy. Furthermore, our results suggest that cluster 1 is equivalent to hot tumors with enhanced sensitivity to immunotherapy drugs. Conclusion Collectively, we developed a risk score model based on 12 prognostic lncRNAs, expected to become a new tool for evaluating the prognosis of patients with ccRCC, providing different immunotherapy strategies by screening for hot and cold tumors.

Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs

Background In reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the tumor microenvironment (TME) and the immunotherapeutic response for accurate dose. Methods To obtain the required data, bladder urothelial carcinoma transcriptome data were searched from Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). We used co-expression analysis, differential expression analysis, and univariate Cox regression to screen out prognostic lncRNAs associated with necroptosis in BLCA. Then the least absolute shrinkage and selection operator (LASSO) was conducted to construct the necroptosis-associated lncRNAs model. Based on this model, we also performed the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) to estimate the prognostic power of risk score. Multivariate and univariate Cox regression analysis were performed to build up a nomogram. Calibration curves, and time-dependent ROC were also conducted to evaluate nomogram. Principal component analysis (PCA) revealed a difference between high- and low-risk groups. In addition, we explored immune analysis, gene set enrichment analyses (GSEA), and evaluation of the half-maximal inhibitory concentration (IC50) in constructed model. Finally, the entire samples were divided into three clusters based on model of necroptosis-associated lncRNAs to further compare immunotherapy in cold and hot tumors. Results A model was built up based on necroptosis-associated lncRNAs. The model revealed good consistence between calibration plots and prognostic prediction. The area of 1-, 3-, and 5-year OS under the ROC curve (AUC) were 0.707, 0.679, and 0.675. Risk groups could be helpful for systemic therapy due to the markedly diverse IC50 between risk groups. To our delight, clusters could effectively identify cold and hot tumors, which would be beneficial to accurate mediation. Clusters 2 and 3 were considered the hot tumor, which was more sensitive to immunotherapeutic drugs. Conclusions The outcomes of our study suggested that necroptosis-associated lncRNAs could effectively predict patients with BLCA prognosis, which may be helpful for distinguishing the cold and hot tumors and improving individual treatment of BLCA.

/pdf/predicting-prognosis-and-distinguishing-cold-and-hot-tumors-2v903jbp.pdf

Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs

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Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs