Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

Background. Secreted protein acidic and rich in cysteine‐like 1 (SPARCL1) plays an important role in tumor pathogenesis. We aim to evaluate the clinical significance and potential biological roles of SPARCL1 in colorectal cancer (CRC). Methods. Datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded to evaluate the expression levels of SPARCL1 in CRC. Receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic value of SPARCL1. Then, comprehensive database search was conducted for published clinical studies to explore clinical significance of SPARCL1. In addition, coexpression genes of SPARCL1 were identified through the cBioPortal database and enrichment analysis of SPARCL1 and its coexpression genes were performed by the “clusterProfiler” R package. Finally, the correlations between SPARCL1 and tumor microenvironment scores, tumor‐infiltrating immune cells in CRC were determined by “ESTIMATE” and “GSVA” R packages. Results. SPARCL1 was significantly downregulated in CRC tissues, and SPARCL1 showed high accuracy for diagnosis of primary CRC in both GEO and TCGA datasets. Pooled results from published clinical studies showed SPARCL1 expression was associated with differentiation (OR = 1.89, 95% CI: 1.38‐2.59), tumor stage (OR = 0.47, 95% CI: 0.29‐0.77), distant metastasis (OR = 0.53, 95% CI: 0.33‐0.84), and overall survival (HR = 0.56, 95% CI: 0.43‐0.74). SPARCL1 and its top 300 coexpression genes were involved in several KEGG pathways, such as focal adhesion, cell adhesion molecules, PI3K‐Akt signaling pathway, cGMP‐PKG signaling pathway, and ECM‐receptor interaction. Besides, the SPARCL1 expression was significantly correlated with stromal score, immune score, ESTIMATE score, and diverse immune cells. Conclusion. SPARCL1 significantly correlated with clinicopathological features and tumor microenvironment in CRC.

Background: Upper respiratory tract infection (URTI), one of the most common respiratory diseases, has a high annual incidence. Trollius chinensis capsule has been used to treat URTI in China. However, the underlying-mechanisms remain unclear. Methods: Network pharmacology was used to explore the potential mechanism of action of Trollius chinensis capsule in URTI treatment. The active compounds in Trollius chinensis were obtained from the TCMSP, SymMap, and ETCM databases. The TCMSP, PubChem, and SwissTargetPrediction databases were used to predict potential targets of Trollius chinensis. URTI-associated targets were gathered from GeneCards and DisGeNET databases. The key targets and signaling pathways associated with URTI were selected by network topology, GO, and KEGG pathway enrichment analysis. Molecular docking was used to verify the binding activity between active compounds and key targets. Results: Quercetin, pectolinarigenin, beta-sitosterol, acacetin and cirsimaritin are major active compounds in Trollius chinensis capsule. Eighty one candidate therapeutic targets were confirmed to be involved in protection of Trollius chinensis capsule against URTI. Among them, 7 key targets (TP53, IL6, AKT1, CASP3, CXCL8, MMP9, and EGFR) were verified to have good binding affinities to the main active compounds. Furthermore, enrichment analyses suggested that inflammatory response, virus infection and oxidative stress related biological processes and pathways were possibly the potential mechanism. Conclusion: Overall, the present study clarified that quercetin, pectolinarigenin, beta-sitosterol, acacetin and cirsimaritin are proved to be the main effective compounds of Trollius chinensis capsule treating URTI, possibly by acting on the targets of IL6, AKT1, CASP3, CXCL8, MMP9 and EGFR to play anti-infectious, anti-viral, and anti-oxidative effects. This study provides a new understanding of the active compounds and mechanisms of Trollius chinensis capsule in URTI treatment from the perspective of network pharmacology.

This study aimed to evaluate the clinical significance and underlying biological mechanisms of LIM kinase 1 (LIMK1) in hepatocellular carcinoma (HCC). Using multi-omics data from TCGA and ICGC cohorts, we analyzed LIMK1 expression and its prognostic value. Clinical validation was performed via immunohistochemistry on tissue microarray specimens. A multivariate Cox model integrating LIMK1 and clinicopathological features was constructed and evaluated using machine learning. Tumor immune microenvironment was profiled using multiple immune deconvolution algorithms. Immunotherapy cohorts and drug sensitivity data were leveraged to assess therapeutic implications. LIMK1 was significantly overexpressed in HCC tissues across all cohorts and correlated with poor overall survival (TCGA HR = 2.26, P < 0.001; ICGC HR = 2.23, P = 0.011; in-house HR = 2.09, P = 0.004). The prognostic Cox model integrating LIMK1 achieved high accuracy (1-year AUC = 0.90) and decision curve analysis showed the potential for clinical decision making. High LIMK1 expression was linked to an immunosuppressive microenvironment, characterized by elevated immunosuppressive cells (MDSCs, M2 macrophages, fibroblasts, and regulatory T cells) and immune checkpoint markers (PDCD1, CTLA4). HCC patients with high LIMK1 expression showed poor responses to immunotherapy but increased sensitivity to chemotherapy agents, including sorafenib, paclitaxel, docetaxel and 5-fluorouracil. In conclusion, LIMK1 serves as a promising biomarker in HCC, stratifying patients by prognosis and therapeutic response.

An association has been observed between systemic lupus erythematosus (SLE) and primary biliary cholangitis (PBC) in observational studies, however, the exact causal link remains unclear. We aim to evaluate the causal relationships between SLE and PBC through bidirectional Mendelian randomization (MR). Single-nucleotide polymorphisms (SNPs) were selected as instrumental variables from publicly accessible genome-wide association studies (GWAS) in European populations. The PBC and SLE GWAS data were obtained from the MRC IEU Open GWAS database, consisting of 24,510 and 14,267 samples, respectively. After a series of quality control and outlier removal, inverse variance weighted was used as the primary approach to evaluate the causal association between SLE and PBC. The horizontal pleiotropy and heterogeneity were examined by the MR-Egger intercept test and Cochran Q value, respectively. Seven SNPs were included to examine the causal effect of SLE on PBC. Genetically predicted SLE may increase the risk of PBC development, with an odds ratio (OR) of 1.324 (95% confidence interval [CI] 1.220 ∼ 1.437, P ˂ .001). Twenty SNPs were included to explore the causal effect of PBC on SLE. Genetically predicted PBC may increase the risk of SLE development, with an OR of 1.414 (95% CI 1.323 ∼ 1.511, P ˂ .001). Horizontal pleiotropy and heterogeneity were absent (P > .05) among SNPs. The robustness of our results was further enhanced by using the leave-one-out method. Our research has provided new insights into SLE and PBC, indicating bidirectional causal associations between the 2 diseases. These findings offer valuable contributions to future clinical studies.