A Novel Defined Ferroptosis-Related Gene Signature for Predicting The Prognosis of Kidney Renal Clear Cell Carcinoma
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Abstract Objective: We developed a predictive model associated with ferroptosis to provide a more comprehensive view of kidney renal clear cell carcinoma (KIRC) immunotherapy. Methods: Gene expression data and corresponding clinical outcomes were obtained from the GEO and The Cancer Genome Atlas (TCGA) databases, and a ferroptosis-related gene set was obtained from the FerrDb database. Results: We identified 17 ferroptosis-related genes that were differentially expressed, including enrichment in genes involved in the immune system process. We established a ferroptosis-related gene-based prognostic model based on the results of univariate Cox regression and multivariate Cox regression analyses, with an area under the curve (AUC) of 0.644 (3 years). We found that the higher exprssion of MT1G, LAMP2 and MIOX showed a higher proportion of CD8+ T cells, CD4+ memory activated T cells, etc. Finally, a predictive ferroptosis-related prognostic nomogram, which included the predictive values of age, gender, grade, TNM stage, and risk score, was established to predict overall survival. Conclusions: In sum, we developed a ferroptosis-related gene-based prognostic model that provides novel insights into the prediction of KIRC prognosis and identifies the relevance of the immune microenvironment for patient outcomes.Keywords:
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Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy characterized by a poor prognosis. The treatment efficacy of immune checkpoint inhibitors (ICIs) also varies widely in advanced ccRCC. We aim to construct a robust gene signature to improve the prognostic discrimination and prediction of ICIs for ccRCC patients. In this study, adopting differentially expressed genes from seven ccRCC datasets in GEO (Gene Expression Omnibus), a novel signature (FOXM1&TOP2A) was constructed in TCGA (The Cancer Genome Atlas) database by LASSO and Cox regression. Survival and time-dependent ROC analysis revealed the strong predictive ability of our signature in discovery set, two online validation sets and one tissue microarray (TMA) from our institution. High-risk group based on the signature comprises more high-grade (G3&G4) and advanced pathologic stage (stageIII/IV) tumors and presents hyperactivation of cell cycle process according to the functional analysis. Meanwhile, high-risk tumors demonstrate an immunosuppressive phenotype with more infiltrations of regulatory T cells (Tregs), macrophages and high expressions of genes negatively regulating anti-tumor immunity. Low-risk tumors have an improved response to anti-PD-1 therapy and the predictive ability of our signature is better than other recognized biomarkers in ccRCC. A nomogram containing this signature showed a high predictive accuracy with AUCs of 0.90 and 0.84 at 3 and 5 years. Overall, this robust signature could predict prognosis, evaluate immune microenvironment and response to anti-PD-1 therapy in ccRCC, which is very promising in clinical promotion.
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Background: Renal cell carcinoma (RCC) is one of the most common cancers, with an annual incidence of nearly 400,000 cases worldwide. Increasing evidence has also demonstrated the vital role of neutrophil extracellular traps (NETs) in cancer progression and metastatic dissemination. Methods: Consensus cluster analysis was performed to determine the number of ccRCC subtypes. The Kruskal-Wallis test or Student t-test was performed to evaluate the difference of infiltrating immune cell and gene expression in different groups. The Kaplan-Meier (KM) method was used to draw the survival curve. LASSO cox regression analysis was conducted to construct a NET-related prognostic signature. We also constructed a lncRNA-miRNA-mRNA regulatory axis by several miRNA and lncRNA target databases. Results: A total of 23 differentially expressed NET-related genes were obtained in ccRCC. Three clusters of ccRCC cases with significant difference in prognosis, immune infiltration, and chemotherapy and targeted therapy were identified. LASSO Cox regression analysis identified a NET-related prognostic signature including six genes (G0S2, DYSF, MMP9, SLC22A4, SELP, and KCNJ15), and this signature had a good performance in predicting the overall survival of ccRCC patients. The expression of prognostic signature genes was significantly correlated with the pTMN stage, immune infiltration, tumor mutational burdens, microsatellite instability, and drug sensitivity of ccRCC patients. MMP9 was identified as the hub gene. We also identified the lncRNA UBA6-AS1/miR-149-5p/MMP9 regulatory axis for the progression of ccRCC. Conclusion: Collectively, the current study identified three molecular clusters and a prognostic signature for ccRCC based on neutrophil extracellular traps. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.
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The tumor microenvironment plays a key role in regulating tumor progression. This research aimed to develop the biomarker related to tumor microenvironment in clear cell renal cell carcinoma (ccRCC).The ESTIMATE algorithm was used to evaluate the immune score of ccRCC cases from The Cancer Genome Atlas (TCGA). Differentially expressed genes between high and low immune scores were identified and a 13-gene signature was constructed by the LASSO Cox regression model to predict overall survival (OS) for ccRCC cases in TCGA or International Cancer Genome Consortium (ICGC) project. The immune cell fractions were calculated by the TIMER algorithm. Cell viability and gene expression were determined by CCK-8 and qRT-PCR, respectively.The OS of patients with high immune scores was worse than that of patients with low immune scores. The OS between ccRCC patients from TCGA or ICGC cohort in high- and low-risk groups stratified by the gene signature was significantly different. Subgroup analysis also showed a robust prognostic ability of the gene signature. Multivariate Cox regression analysis demonstrated that this gene signature was an independent prognostic factor. The nomogram that integrated the gene signature and three clinicopathological risk factors had a favorably predictive ability in predicting 3, 5 and 10 year survival. Moreover, the high-risk group had a significantly higher abundance of B cell, T cell, CD4, neutrophil and DC infiltration. Among 13 genes, X-C motif chemokine receptor1 (XCR1) was upregulated in ccRCC cells and exerted an inhibitory effect on cell proliferation.This study constructs a 13-gene signature as a novel prognostic marker to predict the survival of ccRCC patients and XCR1 may serve as a therapeutic target.
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Accumulating evidence has highlighted the effects of natural killer (NK) cells on shaping anti-tumor immunity. This study aimed to construct an NK cell marker gene signature (NKMS) to predict prognosis and therapeutic response of clear cell renal cell carcinoma (ccRCC) patients.Publicly available single-cell and bulk RNA profiles with matched clinical information of ccRCC patients were collected from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), ArrayExpress, and International Cancer Genome Consortium (ICGC) databases. A novel NKMS was constructed, and its prognostic value, associated immunogenomic features and predictive capability to immune checkpoint inhibitors (ICIs) and anti-angiogenic therapies were evaluated in ccRCC patients.We identified 52 NK cell marker genes by single-cell RNA-sequencing (scRNA-seq) analysis in GSE152938 and GSE159115. After least absolute shrinkage and selection operator (LASSO) and Cox regression, the most prognostic 7 genes (CLEC2B, PLAC8, CD7, SH3BGRL3, CALM1, KLRF1, and JAK1) composed NKMS using bulk transcriptome from TCGA. Survival and time-dependent receiver operating characteristic (ROC) analysis exhibited exceptional predictive capability of the signature in the training set and two independent validation cohorts (E-MTAB-1980 and RECA-EU cohorts). The seven-gene signature was able to identify patients within high Fuhrman grade (G3-G4) and American Joint Committee on Cancer (AJCC) stage (III-IV). Multivariate analysis confirmed the independent prognostic value of the signature, and a nomogram was built for clinical utility. The high-risk group was characterized by a higher tumor mutation burden (TMB) and greater infiltration of immunocytes, particularly CD8+ T cells, regulatory T (Treg) cells and follicular helper T (Tfh) cells, in parallel with higher expression of genes negatively regulating anti-tumor immunity. Moreover, high-risk tumors exhibited higher richness and diversity of T-cell receptor (TCR) repertoire. In two therapy cohorts of ccRCC patients (PMID32472114 and E-MTAB-3267), we demonstrated that high-risk group showed greater sensitivity to ICIs, whereas the low-risk group was more likely to benefit from anti-angiogenic therapy.We identified a novel signature that can be utilized as an independent predictive biomarker and a tool for selecting the individualized treatment for ccRCC patients.
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Abstract This 1:5 case‐control study aimed to identify the risk factors of hospital‐acquired pressure injuries (HAPIs) and to develop a mathematical model of nomogram for the risk prediction of HAPIs. Data for 370 patients with HAPIs and 1971 patients without HAPIs were extracted from the adverse events and the electronic medical systems. They were randomly divided into two sets: training (n = 1951) and validation (n = 390). Significant risk factors were identified by univariate and multivariate analyses in the training set, followed by a nomogram constructed. Age, independent movement, sensory perception and response, moisture, perfusion, use of medical devices, compulsive position, hypoalbuminaemia, an existing pressure injury or scarring from a previous pressure injury, and surgery sufferings were considered significant risk factors and were included to construct a nomogram. In both of the training and validation sets, the areas of 0.90 under the receiver operating characteristic curves showed excellent discrimination of the nomogram; calibration plots demonstrated a good consistency between the observed probability and the nomogram's prediction; decision curve analyses exhibited preferable net benefit along with the threshold probability in the nomogram. The excellent performance of the nomogram makes it a convenient and reliable tool for the risk prediction of HAPIs.
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Objective: To compare the diagnostic accuracy of various transcutaneous bilirubin (TcB) nomograms for predischarge screening. Methods: The paired total serum bilirubin (TSB) and TcB measurements collected in neonates ≥35 weeks and ≥2000 g birth weight were analyzed. BiliCare™ bilirubinometer was used for TcB measurement. We chose the following nomograms for the study: Bhutani nomogram, Maisel's nomogram, Agarwal nomogram, Thakkar nomogram, American Academy of Pediatrics (AAP) nomogram within 3 mg/dl of phototherapy cutoff, AAP nomogram >70% of phototherapy cutoff and if TcB value is above 13 mg/dl. The diagnostic accuracy of these nomograms for TcB was compared with TSB plotted in the Bhutani nomogram. Results: TcB showed a positive correlation with TSB (Pearson correlation coefficient = 0.783). Bhutani nomogram, Maisel's nomogram and AAP (using within 3 mg/dL cutoff) nomogram showed good sensitivity and low false-negative rate while avoiding blood draws in most neonates. Conclusion: Bhutani nomogram, Maisel's nomogram, and AAP (using within 3 mg/dL of phototherapy cutoff) nomograms have comparable diagnostic accuracy for predischarge bilirubin screening in neonates.
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