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BackgroundIncreasing evidence indicates that L-dopa decarboxylase (DDC), which mediates aberrant amino acid metabolism, is significantly associated with tumor progression. However, the impacts of DDC are not elucidated clearly in clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate DDC prognostic value and potential mechanisms for ccRCC patients.MethodsTranscriptomic and proteomic expressions of and clinical data including 532 patients with ccRCC (The Cancer Genome Atlas RNA-seq data), 226 ccRCC samples (Gene Expression Omnibus), 101 ccRCC patients from the E-MTAB-1980 cohort, and 232 patients with ccRCC with proteogenomic data (Fudan University Shanghai Cancer Center) were downloaded and analyzed to investigate the prognostic implications of DDC expression. Cox regression analyses were implemented to explore the effect of DDC expression on the prognosis of pan-cancer. The "limma" package identified the differentially expressed genes (DEGs) between high DDC subgroups and low DDC groups. Functional enrichments were performed based DEGs between DDC subgroups. The differences of immune cell infiltrations and immune checkpoint genes between DDC subgroups were analyzed to identify potential influence on immune microenvironment.ResultsWe found significantly decreased DDC expression in ccRCC tissues compared with normal tissues from multiple independent cohorts based on multi-omics data. We also found that DDC expression was correlated with tumor grades and stages.The following findings revealed that lower DDC expression levels significantly correlated with shorter overall survival (P

BackgroundRenal cancer is one of the most lethal cancers because of its atypical symptoms and metastatic potential. The metabolism of amino acids and their derivatives is essential for cancer cell survival and proliferation. Thus, the construction of the amino acid metabolism-related risk signature might enhance the accuracy of the prognostic model and shed light on the treatments of renal cancers.MethodsRNA expression and clinical data were downloaded from Santa Cruz (UCSC) Xena, GEO, and ArrayExpress databases. The “DESeq2” package identified the differentially expressed genes. Univariate COX analysis selected prognostic genes related to the metabolism of amino acids. Patients were divided into two clusters using the “ConsensusClusterPlus” package, and the CIBERSORT, ESTIMATE methods were explored to assess the immune infiltrations. The LASSO regression analysis constructed a risk model which was evaluated the prediction accuracy in two independent cohorts. The genomic alterations and drug sensitivity of 18-LASSO-genes were assessed. The differentially expressed genes between two clusters were used to perform functional enrichment analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, external validation of TMEM72 expression was conducted in the FUSCC cohort containing 33 ccRCC patients.ResultsThe amino acid metabolism-related genes had significant correlations with prognosis. The patients in Cluster A demonstrated better survival, lower Treg cell proportion, higher ESTIMATE scores, and higher cuproptosis-related gene expressions. Amino acid metabolism-related genes with prognostic values were used to construct a risk model and patients in the low risk group were associated with improved outcomes. The Area Under Curve of the risk model was 0.801, 0.777, and 0.767 at the first, second, and third year respectively. The external validation cohort confirmed the stable prognostic value of the risk model. WGCNA identified four gene modules correlated with immune cell infiltrations and cuproptosis. We found that TMEM72 was downregulated in tumors by using TCGA, GEO datasets (p

BackgroundThis study aims to establish an N6-methyladenosine (m6A) RNA methylation regulators-mediated methylation model and explore its role in predicting prognostic accuracy of immune contexture and characterizations of clear cell renal cell carcinoma (ccRCC).MethodsThe m6A modification subclasses (m6AMS) were identified by unsupervised cluster analysis and three clusters were determined by consensus clustering algorithm in a discovering cohort. Testing and real-world validation cohorts were used to identify predictive responses for immune checkpoint therapies (ICTs) of m6AMS.ResultsPrognostic implications landscape of m6A regulators in cancers and its differential expression levels in ccRCC patients were identified. Based on discovering cohort, ccRCC were automatically divided into three m6AMS, and cluster 3 showed significant worse survival than cluster 1/2. Importantly, it was found that the immune checkpoint molecules expression was significantly elevated in cluster 3. Besides, m6A scoreLow group (cluster 1&2) have significantly elevated TIDE score compared with m6A scoreHigh group (cluster 3). There was conspicuous tertiary lymphoid tissue, aggressive phenotype, elevated glycolysis, expression of PD-L1, abundance of CD8+ T cells, CD4+ FOXP3+ Treg cells and TCRn immune cells infiltration in the high m6A score group. Interestingly, there are significantly increased patients with clinical benefit in m6A scoreHigh group in 368 patients receiving ICTs from testing IMvigor210 (n = 292) and validation FUSCC (n = 55) cohorts.ConclusionOur discovery highlights the relationship between tumor epigenetic heterogeneity and immune contexture. Immune-rejection cluster 3 has pro-tumorigenic immune infiltration, and shows significant clinical benefits for ccRCC patients receiving ICTs, enabling patient selection for future clinical treatment.

BackgroundThe development of a new strategy to overcome chemoresistance to hepatocellular carcinoma (HCC) treatment is a long-standing issue. We have previously found that upregulated SETD3 levels are closely correlated with HCC. This study aims to explore the mechanism underlying how upregulation of SETD3 promotes liver carcinogenesis.MethodsRNA-Sequencing analysis was used to explore the correlation of SETD3 with regulatory targets. In vitro assays including cell proliferation and migration were performed to study the oncogenic roles of SETD3 and PLK1. Western blotting, immunohistochemical staining, and blood biochemical assays were performed to examine protein expression or pathological index in tumor tissues and mice liver tissues. Luciferase reporter system and chromatin immunoprecipitation assays were used to explore the mechanism.ResultsWe revealed that SETD3 regulates gene expression in subgroups, including cell division, cell proliferation, and cell cycle, in hepatocellular tumor cells. We found that SETD3 upregulation is associated with elevated PLK1 level in both hepatic tumor cells and clinical liver tissues. We further showed that overexpression of SETD3 promoted tumor cell proliferation and migration, whereas inhibition of PLK1 activity attenuated these phenotypes caused by SETD3. By taking advantage of the Sleep Beauty transposase system, we confirmed that upregulated mouse Setd3 promoted hepatic carcinogenesis in situ, but knockdown of mouse Plk1 mitigated Setd3-promoted tumorigenesis in mice. Mechanistically, we showed that SETD3 could be recruited to the promoter of PLK1 gene to facilitate PLK1 transcription.ConclusionsOur data demonstrate that elevated SETD3 may promote HCC by enhancing PLK1 expression, which suggests that SETD3 may act as a potential drug target combined with PLK1 inhibition to treat HCC.

Currently, non-small cell lung carcinoma (NSCLC) is a major worldwide health problem. Meanwhile accumulating evidence indicates that histone deacetylase (HDAC) activation could induce PD-L1 expression in various types of cancer, especially in myeloma and B-cell lymphomas. Therefore, we hypothesized that high-level expression of HDAC10 is associated with PD-L1 induction and poor prognosis in patients with NSCLC. In total 180 NSCLC patients receiving complete pulmonary resection and systematic lymph node dissection were enrolled from April 2004 to August 2009. The patients with integrated clinicopathological records were followed up. The expression level of HDAC10 and PD-L1 in tissue samples was determined by immunohistochemistry. We observed that HDAC10 expression in lung cancer tissue is significantly higher than that in corresponding para-cancer tissue. Moreover, HDAC10 expression positively correlated with the expression level of PD-L1 (r = 0.213, P < 0.05) in NSCLC patients. Subgroup, multivariate analysis showed that the expression level of HDAC10 can be an independent prognostic factor and high-level expression of HDAC10 indicated poor overall survival for pulmonary carcinoma (r = 0.540, P < 0.001). Our findings suggest that the expression level of HDAC10 is positively associated with PD-L1 expression and may predict the outcome of patients with NSCLC.