Long Noncoding RNA uc001pwg.1 Is Downregulated in Neointima in Arteriovenous Fistulas and Mediates the Function of Endothelial Cells Derived from Pluripotent Stem Cells

Recent studies indicate important roles for long noncoding RNAs (lncRNAs) as essential regulators of gene expression. However, the specific roles of lncRNAs in stenotic lesions of arteriovenous fistula (AVF) failure are still largely unknown. We first analyzed the expression profiles of lncRNAs in human stenosed and nonstenotic uremic veins using RNA-sequencing methodology. A total of 19 lncRNAs were found to be differentially expressed in stenotic lesions. Among these, uc001pwg.1 was one of the most significantly downregulated lncRNAs and enriched in both control vein segments and human umbilical vein endothelial cells (HUVECs). Further studies revealed that uc001pwg.1 overexpression could increase nitric oxide synthase (eNOS) phosphorylation and nitric oxide (NO) production in endothelial cells (ECs) derived from human-induced pluripotent stem cells (HiPSCs). Mechanistically, uc001pwg.1 improves endothelial function via mediating MCAM expression. This study represents the first effort of identifying a novel candidate lncRNA for modulating the function of iPSC-ECs, which may facilitate the improvement of stem cell-based therapies for AVF failure

Patients with high nuclear grade pT1-ccRCC are more suitable for radical nephrectomy than partial nephrectomy: a multicenter retrospective study using propensity score

Abstract Background Partial nephrectomy (PN) is usually recommended for T1 stage clear cell renal cell carcinoma (ccRCC) regardless of the nuclear grades. However, the question remains unresolved as to whether PN is non-inferior to RN in patients with T1-ccRCC at higher risk of recurrence. In fact, we found that patients with high nuclear grades treated with PN had poorer prognosis compared with those treated with radical nephrectomy (RN). Therefore, this study was designed to evaluate the associations of PN and RN in the four nuclear grade subsets with oncologic outcomes. Methods A retrospective study was conducted in three Chinese urological centers that included 1,714 patients who underwent PN or RN for sporadic, unilateral, pT1, N0, and M0 ccRCC without positive surgical margins and neoadjuvant therapy between 2010 and 2019. Associations of nephrectomy type with local ipsilateral recurrence, distant metastases, and all-cause mortality (ACM) were evaluated using the Kaplan–Meier method and multivariable Cox proportional hazards regression models after overlap weighting (OW). Results A total of 1675 patients entered the OW cohort. After OW, in comparison to PN, RN associated with a reduced risk of local ipsilateral recurrence in the G2 subset (HR = 0.148, 95% CI 0.046–0.474; p < 0.05), G3 subset (HR = 0.097, 95% CI 0.021–0.455; p < 0.05), and G4 subset (HR = 0.091, 95% CI 0.011–0.736; p < 0.05), and resulting in increased five-year local recurrence-free survival rates of 7.0%, 17.9%, and 36.2%, respectively. An association between RN and a reduced risk of distant metastases in the G4 subset (HR = 0.071, 95% CI 0.016–0.325; p < 0.05), with the five-year distant metastases-free survival rate increasing by 33.1% was also observed. No significant difference in ACM between PN and RN was identified. Conclusions Our findings substantiate that opting for RN, as opposed to PN, is more advantageous for local recurrence-free survival and distant metastases-free survival in patients with high nuclear grade (especially G4) pT1-ccRCC. We recommend placing a heightened emphasis on enhancing preoperative nuclear grade assessment, as it can significantly influence the choice of surgical plan. Trial registration This study was registered at Chinese Clinical Trial Registry (ID: ChiCTR2200063333)

Image2_Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy.tif

Background: Collagen represents a prominent constituent of the tumor’s extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions.Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer.Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells.Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.</p

Image9_Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy.png

Background: Collagen represents a prominent constituent of the tumor’s extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions.Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer.Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells.Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.</p

Image5_Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy.tif

Background: Collagen represents a prominent constituent of the tumor’s extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions.Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer.Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells.Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.</p

Image3_Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy.tif

Background: Collagen represents a prominent constituent of the tumor’s extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions.Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer.Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells.Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.</p

Characteristics of microRNAs and Target Genes in Maize Root under Drought Stress

Maize (Zea mays) is an important multi-functional crop. The growth and yield of maize are severely affected by drought stress. Previous studies have shown that microRNAs (miRNAs) in maize play important roles in response to abiotic stress; however, their roles in response to drought stress in maize roots is unclear. In our study, we found 375 miRNAs in the roots of 16 inbred lines. Of the 16 lines, zma-MIR168, zma-MIR156, and zma-MIR166 were highly expressed, whereas zma-MIR399, zma-MIR2218, and zma-MIR2275 exhibited low expression levels. The expression patterns of miRNA in parental lines and their derived RILs are different. Over 50% of miRNAs exhibited a lower expression in recombinant inbred lines than in parents. The expression of 50 miRNAs was significantly altered under water stress (WS) in at least three inbred lines, and the expression of miRNAs in drought-tolerant lines changed markedly. To better understand the reasons for miRNA response to drought, the degree of histone modifications for miRNA genes was estimated. The methylation level of H3K4 and H3K9 in miRNA precursor regions changed more noticeably after WS, but no such phenomenon was seen for DNA methylation and m6A modification. After the prediction of miRNA targets using psRNATarget and psRobot, we used correlation analysis and qRT-PCR to further investigate the relationship between miRNAs and target genes. We found that 87 miRNA–target pairs were significantly negatively correlated. In addition, a weighted gene co-expression network analysis using miRNAs, as well as their predicted targets, was conducted to reveal that miR159, miR394, and miR319 may be related to maize root growth. The results demonstrated that miRNAs might play essential roles in the response to drought stress

Image8_Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy.png

Background: Collagen represents a prominent constituent of the tumor’s extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions.Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer.Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells.Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.</p

Image10_Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy.tif

Background: Collagen represents a prominent constituent of the tumor’s extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions.Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer.Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells.Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.</p