Ferroptosis-associated lncRNA prognostic signature predicts prognosis and immune response in clear cell renal cell carcinoma.

Clear cell Renal Cell Carcinoma (ccRCC), the most deadly and life-threatening tumor in the urinary system, has a dismal prognosis and a high risk of metastasizing. Regulation of ferroptosis is a prospective therapeutic target to eradicate malignant cells. Our objective was to seek ferroptosis-associated long non-coding RNAs (FALs) and developed a prediction signature for ccRCC. We extracted transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) databases. Ferroptosis-associated genes (FAGs) were obtained from FerrDb database. A ferroptosis-associated lncRNA prognostic signature (FLPS) of ccRCC was generated utilizing univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, sequentially, based on 8 lncRNAs (LINC00460, AC124854.1, AC084876.1, IGFL2-AS1, LINC00551, AC083967.1, AC073487.1, and LINC02446). The signature's independent predictive value for ccRCC was demonstrated using univariate and multivariate regression analysis (P < 0.05). Subsequently, by combining independent predictive factors, a prognostic nomogram was established. Immunity analysis proclaimed a striking difference in terms of cells, function, checkpoints, and ESTIMATE scores between low- and high-risk groups. Overall, the innovative signature of ferroptosis-associated signatures may have a considerable effect on the immune response and prognosis for ccRCC.

Integrated bioinformatics analysis to identify the key gene associated with metastatic clear cell renal cell carcinoma.

Metastasis of clear cell renal cell carcinoma (ccRCC) is a leading cause of death. The purpose of this research was to investigate the key gene in ccRCC tumor metastasis. Three microarray datasets (GSE22541, GSE85258, and GSE105261), which included primary and metastatic ccRCC tissues, were obtained from the Gene Expression Omnibus (GEO) database. Expression profiling and clinical data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) dataset. A total of 20 overlapping differentially expressed genes (DEGs) were identified using the R limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the DEGs were mainly enriched in tumor metastasis-related pathways. Gene expression analysis and survival analysis in the GEPIA2 database further identified the key gene HSD11B2. qRT-PCR result manifested that HSD11B2 level was significantly down-regulated in ccRCC tissues compared with adjacent normal tissues. ROC analysis showed that HSD11B2 exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Univariate and multivariate Cox regression analysis showed that HSD11B2 expression was an independent prognostic factor. To establish a nomogram combining HSD11B2 expression and clinical factors, and a new method for predicting the survival probability of ccRCC patients. Gene Set Enrichment Analysis (GSEA) enrichment results showed that low expression of HSD11B2 was mainly enriched in tumor signaling pathways and immune-related pathways. Immune analysis revealed a significant correlation between HSD11B2 and tumor immune infiltrates in ccRCC. This study suggests that HSD11B2 can serve as a potential biomarker and therapeutic target for ccRCC metastasis.

Guanylate-binding Protein 2 Expression Is Associated With Poor Survival and Malignancy in Clear-cell Renal Cell Carcinoma.

BACKGROUND/AIM: Clear-cell renal cell carcinoma (ccRCC) is a common recalcitrant cancer. However, little is known about biomarkers of ccRCC. In the present study, we investigated the role of guanylate-binding protein 2 (GBP2), an interferon-induced GTPase, in ccRCC and its potential as a biomarker of this disease. MATERIALS AND METHODS: GBP2 expression was analyzed using the Gene Expression Omnibus, The Cancer Genome Atlas, and Human Protein Atlas databases. Univariate and multivariate Cox-regression analyses were used to investigate the prognostic value of GBP2 in ccRCC. In addition, quantitative real-time polymerase chain reactions, western blotting, and immunohistochemistry were performed to confirm the expression of GBP2 in tissues from patients with ccRCC who had undergone radical nephrectomy at the Department of Urology, the First Affiliated Hospital of Chongqing Medical University (Chongqing, P. R. China). Cell-function assays were performed to investigate the effect of GBP2 on Caki-1 human kidney-cancer cells. RESULTS: Bioinformatics and in vitro experiments showed that the expression of GBP2 was up-regulated in ccRCC tissues and cells and was positively correlated with the malignant clinicopathological parameters of the disease. Univariate and multivariate Cox-regression analyses showed that GBP2 expression was an independent risk factor for the prognosis of ccRCC. Cell-function assays showed that GBP2 expression promoted the proliferation, migration, and invasion of Caki-1 cells through the WNT/ß-catenin signaling pathway. CONCLUSION: The present results indicate that GBP2 expression may serve as a prognostic biomarker for ccRCC.

Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment.

Non-small cell lung cancer (NSCLC) is characterized by relatively rapid response to systemic treatments yet inevitable resistance and predisposed to distant metastasis. We thus aimed at performing sequencing analysis to determine genomic events and underlying mechanisms concerning drug resistance in NSCLC. We performed targeted sequencing of 40 medication-relevant genes on plasma samples from 98 NSCLC patients and analyzed impact of genetic alterations on clinical presentation as well as response to systemic treatments. Profiling of multi-omics data from 1024 NSCLC tissues in public datasets was carried out for comparison and validation of identified molecular events implicated in resistance. A genetic association of CYP2D6 deletion with drug resistance was identified through circulating tumor DNA (ctDNA) profiling and response assessment. FCGR3A amplification was potentially involved in resistance to EGFR inhibitors. We further verified our findings in tissue samples and focused on potential resistance mechanisms, which uncovered that depleted CYP2D6 affected a set of genes involved in EMT, oncogenic signaling as well as inflammatory pathways. Tumor microenvironment analysis revealed that NSCLC with CYP2D6 loss manifested increased levels of immunomodulatory gene expressions, PD-L1 expression, relatively high mutational burden and lymphocyte infiltration. DNA methylation alterations were also found to be correlated with mRNA expressions and copy numbers of CYP2D6. Finally, MEK inhibitors were identified by CMap as the prospective therapeutic drugs for CYP2D6 deletion. These analyses identified novel resistance mechanisms to systemic NSCLC treatments and had significant implications for the development of new treatment strategies.

Identifying CpG methylation signature as a promising biomarker for recurrence and immunotherapy in non-small-cell lung carcinoma.

Epigenetic alterations are crucial to oncogenesis and regulation of gene expression in non-small-cell lung carcinoma (NSCLC). DNA methylation (DNAm) biomarkers may provide molecular-level prediction of relapse risk in cancer. Identification of optimal treatment is warranted for improving clinical management of NSCLC patients. Using machine learning algorithm we identified 4 recurrence predictive CpG methylation markers (cg00253681/ART4, cg00111503/KCNK9, cg02715629/FAM83A, cg03282991/C6orf10) and constructed a risk score model that potently predicted recurrence-free survival and prognosis for patients with NSCLC (P = 0.0002). Integrating genomic, transcriptomic, proteomic and clinical data, the DNAm-based risk score was observed to significantly associate with clinical stage, cell proliferation markers, somatic alterations, tumor mutation burden (TMB) as well as DNA damage response (DDR) genes, and potentially predict the efficacy of immunotherapy. In general, our identified DNAm signature shows a significant correlation to TMB and DDR pathways, and serves as an effective biomarker for predicting NSCLC recurrence and response to immunotherapy. These findings demonstrate the utility of 4-DNAm-marker panel in the prognosis, treatment decision-making and evaluation of therapeutic responses for NSCLC.