PCDH8 is a novel prognostic biomarker in thyroid cancer and promotes cell proliferation and viability.

Protocadherin 8 (PCDH8), a calcium-dependent transmembrane protein in the protocadherin family, regulates cell adhesion and signal transduction. While some studies have provided indirect evidence that PCDH8 has cancer-promoting properties, this association is controversial. In particular, its involvement in thyroid cancer (THCA) remains unclear. We aimed to elucidate the role of PCDH8 in THCA using bioinformatic analysis. Subsequently, the results were experimentally validated. The analysis conducted using the R programming language and online web tools explored PCDH8 expression levels, prognostic, and clinical implications, and its relationship with the tumor immune microenvironment in THCA. Furthermore, we examined the association between PCDH8 and co-expressed genes, highlighting their involvement in several biological processes relevant to THCA. The potential of PCDH8 as a therapeutic target for this pathology was also explored. Immunohistochemical (IHC) staining was performed on samples from 98 patients with THCA, and experimental validation was carried out. PCDH8 was significantly elevated in cancer tissues and associated with poor prognosis, several clinical factors, and immune cell and checkpoint abundance. Cox regression and survival analyses, together with Receiver Operating Curves (ROC) indicated that PCDH8 was an independent prognostic factor for THCA. Furthermore, PCDH8 impacts cell viability and proliferation, promoting tumorigenesis. Also, it influences tumor cell sensitivity to various drugs. Thus, PCDH8 might be a potential therapeutic target for THCA. IHC, cell culture, MTT, and colony formation experiments further confirmed our findings. This analysis provided insights into the potential carcinogenic role of PCDH8 in THCA, as it impacts cell viability and proliferation. Thus, PCDH8 might play an important role in its prognosis, immune infiltration, and diagnosis.

Age-specific differences in association of glycosylated hemoglobin levels with the prevalence of cardiovascular diseases among nondiabetics: the National Health and Nutrition Examination Survey 2005-2018.

BACKGROUND: Previous research has supported the presence of an association between high glycated hemoglobin (HbA1c) levels and cardiovascular disease (CVD). The objective of the present study was to determine whether increased HbA1c levels are associated with high CVD prevalence among nondiabetics. Furthermore, we aimed to explore the possible interaction of HbA1c levels and age in regard to CVD. METHODS: This cross-sectional study analyzed data of 28,534 adult participants in the National Health and Nutrition Examination Survey 2005-2018. The association between HbA1c and CVD was assessed using univariate and multivariate logistic regression models. Propensity score matching was used to reduce selection bias. Subgroup analysis and restricted cubic spline (RCS) were used to further characterize the association between HbA1c levels and CVD. We modeled additive interactions to further assess the relationship between HbA1c levels and age. RESULTS: In the multivariate logistic regression model, a positive association was found between CVD and increased HbA1c levels (highest quartile [Q4] vs. lowest quartile [Q1]: odds ratio [OR] = 1.277, 95% confidence interval [CI] = 1.111-1.469, P = 0.001). In the stratified analyses, the adjusted association between HbA1c and CVD was significant for those younger than 55 years (Q4 vs. Q1: OR = 1.437, 95% CI = 1.099-1.880, P = 0.008). RCS did not reveal a nonlinear relationship between HbA1c levels and CVD among nondiabetics (P for nonlinearity = 0.609). Additionally, a high HbA1c level was favorably connected with old age on CVD, with a synergistic impact. CONCLUSIONS: Increased HbA1c levels were associated with high CVD prevalence among nondiabetics. However, we still need to carefully explain the effect of age on the relationship between HbA1c and CVD in nondiabetic population. Given the correlations of HbA1c with CVDs and CV events, HbA1c might be a useful indicator for predicting CVDs and CV events in the nondiabetic population.

Association between visceral adipose tissue and asthma based on the NHANES and Mendelian randomization study.

BACKGROUND: Obesity is a crucial risk factor for asthma. Observational studies have examined the association between abdominal obesity and asthma symptoms. This study aimed to investigate the causal relationship between visceral adipose tissue (VAT) and asthma and its potential as an independent indicator. METHODS: This study utilized data from the National Health and Nutrition Examination Survey spanning 2011-8. Multivariable logistic regression and stratified variable selection were employed to identify associations between asthma and VAT. Moreover, a two-sample Mendelian randomization analysis, using 221 genetic variants as instrumental variables, was conducted to assess this relationship further. RESULTS: Our findings indicated that individuals with higher VAT levels were more likely to develop asthma. Visceral obesity remained a significant risk factor for asthma after adjusting for demographic characteristics. Genetic predictions suggest a positive association between VAT and an elevated risk of asthma (odds ratio [OR] = 1.393, 95% confidence interval [CI]: 1.266-1.534, and P = 1.43E-11). No significant polymorphisms were detected using the Mendelian randomization-Egger intercept test. CONCLUSIONS: This study presents potential evidence supporting the causal role of VAT in asthma development. Furthermore, the findings from the Mendelian randomization analysis further reinforce the relationship between VAT and asthma risk.

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma.

Background: Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechanism of co-morbidity. This study aims to explore the key genes, pathways, and immune cells that may link psoriasis and cervical squamous cell carcinoma (CESC). Methods: The cervical squamous cell carcinoma dataset (GSE63514) was downloaded from the Gene Expression Omnibus (GEO). Two psoriasis-related datasets (GSE13355 and GSE14905) were merged into one comprehensive dataset after removing batch effects. Differentially expressed genes were identified using Limma and co-expression network analysis (WGCNA), and machine learning random forest algorithm (RF) was used to screen the hub genes. We analyzed relevant gene enrichment pathways using GO and KEGG, and immune cell infiltration in psoriasis and CESC samples using CIBERSORT. The miRNA-mRNA and TFs-mRNA regulatory networks were then constructed using Cytoscape, and the biomarkers for psoriasis and CESC were determined. Potential drug targets were obtained from the cMAP database, and biomarker expression levels in hela and psoriatic cell models were quantified by RT-qPCR. Results: In this study, we identified 27 key genes associated with psoriasis and cervical squamous cell carcinoma. NCAPH, UHRF1, CDCA2, CENPN and MELK were identified as hub genes using the Random Forest machine learning algorithm. Chromosome mitotic region segregation, nucleotide binding and DNA methylation are the major enrichment pathways for common DEGs in the mitotic cell cycle. Then we analyzed immune cell infiltration in psoriasis and cervical squamous cell carcinoma samples using CIBERSORT. Meanwhile, we used the cMAP database to identify ten small molecule compounds that interact with the central gene as drug candidates for treatment. By analyzing miRNA-mRNA and TFs-mRNA regulatory networks, we identified three miRNAs and nine transcription factors closely associated with five key genes and validated their expression in external validation datasets and clinical samples. Finally, we examined the diagnostic effects with ROC curves, and performed experimental validation in hela and psoriatic cell models. Conclusions: We identified five biomarkers, NCAPH, UHRF1, CDCA2, CENPN, and MELK, which may play important roles in the common pathogenesis of psoriasis and cervical squamous cell carcinoma, furthermore predict potential therapeutic agents. These findings open up new perspectives for the diagnosis and treatment of psoriasis and squamous cell carcinoma of the cervix.

DNA damage response-related signatures characterize the immune landscape and predict the prognosis of HCC via integrating single-cell and bulk RNA-sequencing.

BACKGROUND: The occurrence and progression of hepatocellular carcinoma (HCC) are significantly affected by DNA damage response (DDR). Exploring DDR-related biomarkers can help predict the prognosis and immune characteristics of HCC. METHODS: First, the single-cell RNA sequencing (scRNA-seq) dataset GSE242889 was processed and performed manual annotation. Then we found the marker genes of DDR-active subgroups based on "AUCell" algorithm. The "Limma" R package was used to identify differentially expressed genes (DEGs) between tumor and normal samples of HCC. The risk prognostic model was constructed by filtering genes using univariate Cox and LASSO regression analyses. Finally, the signatures were analyzed for immune infiltration, gene mutation, and drug sensitivity. Last but not least, KPNA2, which had the largest coefficient in our model was validated by experiments including western blot, MTT, colony formation and γ-H2AX assays. RESULTS: We constructed a prognostic model based on 5 DDR marker genes including KIF2C, CDC20, KPNA2, UBE2S and ADH1B for HCC. We also proved that the model had an excellent performance in both training and validation cohorts. Patients in the high-risk group had a poorer prognosis, different immune features, gene mutation frequency, immunotherapy response and drug sensitivity compared with the low-risk group. Besides, our experimental results proved that KPNA2 was up-regulated in liver cancer cells than in hepatocytes. More importantly, the knockdown of KPNA2 significantly inhibited cell variability, proliferation and promoted DNA damage. CONCLUSIONS: We innovatively integrated scRNA-seq and bulk RNA sequencing to construct the DDR-related prognostic model. Our model could effectively predict the prognosis, immune landscape and therapy response of HCC.

Machine Learning-based Development and Validation of a Cell Senescence Predictive and Prognostic Signature in Intrahepatic Cholangiocarcinoma.

Background: Previous studies have shown that cellular senescence is strongly associated with tumorigenesis and the tumor microenvironment. Accordingly, we developed a novel prognostic signature for intrahepatic cholangiocarcinoma (ICCA) based on senescence-associated long non-coding RNAs (SR-lncRNAs) and identified a lncRNA-miRNA-mRNA axis involving in ICCA. Methods: Based on the 197 senescence-associated genes (SRGs) from Genacards and their expression in Fu-ICCA cohort, we identified 20 lncRNAs as senescence-associated lncRNAs (SR-lncRNAs) through co-expression and cox-regression analysis. According to 20 SR-lncRNAs, patients with ICCA were classified into 2 molecular subtypes using unsupervised clustering machine learning approach and to explore the prognostic and functional heterogeneity between these two subtypes. Subsequently, we integrated 113 machine learning algorithms to develop senescence-related lncRNA signature, ultimately identifying 11 lncRNAs and constructing prognostic models and risk stratification. The correlation between the signature and the immune landscape, immunotherapy response as well as drug sensitivity are explored too. Results: We developed a novel senescence related signature. The predictive model and risk score calculated by the signature exhibited favorable prognostic predictive performance, which is a suitable independent risk factor for the prognosis of patients with ICCA based on Kaplan-Meier plotter, nomogram and receiving operating characteristic (ROC) curves. The results were validated using external datasets. Estimate, ssGSEA (single sample gene set enrichment analysis), IPS (immunophenotype score) and TIDE (tumor immune dysfunction and exclusion) algorithms revealed higher immune infiltration, higher immune scores, lower immune escape potential and better response to immunotherapy in the high-risk group. In addition, signature identifies eight chemotherapeutic agents, including cisplatin for patients with different risk levels, providing guidance for clinical treatment. Finally, we identified a set of lncRNA-miRNA-mRNA axes involved in ICCA through regulation of senescence. Conclusion: SR-lncRNAs signature can favorably predict the prognosis, risk stratification, immune landscape and immunotherapy response of patients with ICCA and consequently guide individualized treatment.

FOXC2-AS1/FOXC2 axis mediates matrix stiffness-induced trans-differentiation of hepatic stellate cells into fibrosis-promoting myofibroblasts.

Matrix stiffness is a central modulator of hepatic stellate cells (HSCs) activation and hepatic fibrogenesis. However, the long non-coding RNAs (lncRNAs)-regulated transcriptional factors linking matrix stiffness to alterations in HSCs phenotype are not completely understood. In this study, we investigated the effects of matrix stiffness on HSCs activation and its potential mechanism. Through analysis the RNA-seq data with human primary HSCs cultured on 0.4 kPa and 25.6 kPa hydrogel, we identified that forkhead box protein C2 (FOXC2) and its antisense lncRNA FXOC2-AS1 as the new mechanosensing transcriptional regulators that coordinate HSCs responses to the matrix stiffness, moreover, FOXC2 and FOXC2-AS1 expression were also elevated in human fibrosis and cirrhosis tissues. The matrix stiffness was sufficient to activate HSCs into myofibroblasts, resulting in nuclear accumulation of FOXC2. Disrupting FOXC2 and FOXC2-AS1 level abrogated stiffness-induced activation of HSCs. Further mechanistic studies displayed that stiffness-upregulated lncRNA FOXC2-AS1 had no influence on transcription of FOXC2. FOXC2-AS1 exerted its biological function through maintaining the RNA stability of FOXC2, and protecting FOXC2 mRNA from degradation by RNA exosome complex. Additionally, rescue assays confirmed that reintroduction of FOXC2 in FOXC2-AS1-depleted HSCs reversed the repression of FOXC2-AS1 knockdown on stiffness-induced HSCs activation. In AAV6-treated mice fibrotic models, targeting FOXC2 in vivo lead to a reduced degree of liver fibrosis. In sum, our study uncovers a reciprocal crosstalk between matrix stiffness and FOXC2-AS1/FOXC2 axis leading to modulation of HSCs mechanoactivation and liver fibrosis, and present AAV6 shRNA as an effective strategy that targets FOXC2 leading to the resolution of liver fibrosis.