Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

Unraveling the role of the circadian clock genes in cervical squamous cell carcinoma and endocervical adenocarcinoma: A prognostic indicator for prognostic, immunotherapy response, and chemotherapy sensitivity.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) account for a significant proportion of gynecological malignancies and represent a major global health concern. Globally, CESC is ranked as the fourth most common cancer among women. Conventional treatment of this disease has a less favorable prognosis for most patients. However, the discovery of early molecular biomarkers is therefore important for the diagnosis of CESC, as well as for slowing down their progression process. Methods: To identify differentially expressed genes strongly associated with prognosis, univariate Cox proportional hazard analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used. Using multiple Cox proportional hazard regression, a multifactorial model for prognostic risk assessment was then created. Results: The expression of biological clock-related genes, which varied considerably among distinct subtypes and were associated with significantly diverse prognoses, was used to categorize CESC patients. These findings demonstrate how the nomogram developed based on the 7-CRGs signature may assist physicians in creating more precise, accurate, and successful treatment plans that can aid CESC patients at 1, 3, and 5 years. Conclusions: By using machine learning techniques, we thoroughly investigated the impact of CRGs on the prognosis of CESC patients in this study. By creating a unique nomogram, we were able to accurately predict patient prognosis. At the same time, we showed new perspectives on the development of CESC and its treatment by analyzing the associations of the prognostic model with immunity, enrichment pathways, chemotherapy sensitivity, and so on. This research provides a new direction for clinical treatment.

Uncovering the Impact of Aggrephagy in the Development of Alzheimer's Disease: Insights Into Diagnostic and Therapeutic Approaches from Machine Learning Analysis.

BACKGROUND: Alzheimer's disease (AD) stands as a widespread neurodegenerative disorder marked by the gradual onset of memory impairment, predominantly impacting the elderly. With projections indicating a substantial surge in AD diagnoses, exceeding 13.8 million individuals by 2050, there arises an urgent imperative to discern novel biomarkers for AD. METHODS: To accomplish these objectives, we explored immune cell infiltration and the expression patterns of immune cells and immune function-related genes of AD patients. Furthermore, we utilized the consensus clustering method combined with aggrephagy-related genes (ARGs) for typing AD patients and categorized AD specimens into distinct clusters (C1, C2). A total of 272 candidate genes were meticulously identified through a combination of differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Subsequently, we applied three machine learning algorithms-namely random forest (RF), support vector machine (SVM), and generalized linear model (GLM)-to pinpoint a pathogenic signature comprising five genes associated with AD. To validate the predictive accuracy of these identified genes in discerning AD progression, we constructed nomograms. RESULTS: Our analyses uncovered that cluster C2 exhibits a higher immune expression than C1. Based on the ROC(0.956). We identified five characteristic genes (PFKFB4, PDK3, KIAA0319L, CEBPD, and PHC2T) associated with AD immune cells and function. The nomograms constructed on the basis of these five diagnostic genes demonstrated effectiveness. In the validation group, the ROC values were found to be 0.760 and 0.838, respectively. These results validate the robustness and reliability of the diagnostic model, affirming its potential for accurate identification of AD. CONCLUSION: Our findings not only contribute to a deeper understanding of the molecular mechanisms underlying AD but also offer valuable insights for drug development and clinical analysis. The limitation of our study is the limited sample size, and although AD-related genes were identified and some of the mechanisms elucidated, further experiments are needed to elucidate the more in-depth mechanisms of these characterized genes in the disease.

Uncovering the potential role of oxidative stress in the development of periodontitis and establishing a stable diagnostic model via combining single-cell and machine learning analysis.

Background: The primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis. Methods: We used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations. Results: We discovered 3 periodontitis-associated genes (CASP3, IL-1ß, and TXN). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1ß differed with the change of cell status. Conclusion: This study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis.

Tumor necrosis factor-α induces proliferation and reduces apoptosis of colorectal cancer cells through STAT3 activation.

Tumor necrosis factor-alpha (TNF-α) is a potent pro-inflammatory factor that plays an important role in establishing a complicated connection between inflammation and cancer. TNF-α promotes tumor proliferation, migration, invasion, and angiogenesis according to numerous studies. Studies have shown the significant role of STAT3, a downstream transcription factor of another important inflammatory cytokine, IL-6 in the development and progression of different tumors especially colorectal cancer. In the present study, we investigated whether TNF-α has a role in proliferation and apoptosis of colorectal cancer cells through STAT3 activation. HCT116 cell line as human colorectal cancer cells was used in this study. Major assays were MTT assay, reverse transcription-PCR (RT-PCR), flow cytometric analysis, and ELISA. Results showed that TNF-α significantly increased the phosphorylation of STAT3 and expression of all the STAT3 target genes related to cell proliferation, survival, and metastasis compared with control. Moreover, our data showed that the STAT3 phosphorylation and expression of its target genes significantly were reduced in the presence of TNF-α + STA-21 compared with TNF-α-treated group demonstrating that the increase in genes expression partially was due to the TNF-α-induced STAT3 activation. On the other hand, STAT3 phosphorylation and mRNA levels of its target genes were partially decreased in the presence of TNF-α + IL-6R supporting the indirect pathway of STAT3 activation by TNF-α through inducing IL-6 production in cancer cells. Given the growing evidence for STAT3 as a key mediator of inflammation-induced colon cancer, our findings support further investigation of STAT3 inhibitors as potential cancer therapies.

An Aggrephagy-Related LncRNA Signature for the Prognosis of Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a common, highly malignant, and aggressive gastrointestinal tumor. The conventional treatment of PAAD shows poor results, and patients have poor prognosis. The synthesis and degradation of proteins are essential for the occurrence and development of tumors. Aggrephagy is a type of autophagy that selectively degrades aggregated proteins. It decreases the formation of aggregates by degrading proteins, thus reducing the harm to cells. By breaking down proteins, it decreases the formation of aggregates; thus, minimizing damage to cells. For evaluating the response to immunotherapy and prognosis in PAAD patients, in this study, we developed a reliable signature based on aggrephagy-related genes (ARGs). We obtained 298 AGGLncRNAs. Based on the results of one-way Cox and LASSO analyses, the lncRNA signature was constructed. In the risk model, the prognosis of patients in the low-risk group was noticeably better than that of the patients in the high-risk group. Additionally, the ROC curves and nomograms validated the capacity of the risk model to predict the prognosis of PAAD. The patients in the low-risk and high-risk groups showed considerable variations in functional enrichment and immunological analysis. Regarding drug sensitivity, the low-risk and high-risk groups had different half-maximal inhibitory concentrations (IC50).

The role of diet in renal cell carcinoma incidence: an umbrella review of meta-analyses of observational studies.

BACKGROUND: Evidence associating diet with the incidence of renal cell carcinoma (RCC) is inconclusive. We aimed to summarize evidence associating dietary factors with RCC incidence and assess the strength and validity of this evidence. METHODS: We conducted an umbrella review of systematic reviews or meta-analyses (SRoMAs) that assessed the association between diet and RCC incidence. Through April 2021, PubMed, Web of Science, Embase, The Cochrane Library, Scopus, and WCRF were searched. Two independent reviewers selected studies, extracted data, and appraised the quality of SRoMAs. According to credibility assessment criteria, evidence can be divided into five categories: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV), and nonsignificant (class V). RESULTS: Twenty-nine meta-analyses were obtained after screening. After excluding 7 overlapping meta-analyses, 22 meta-analyses including 502 individual studies and 64 summary hazard ratios for RCC incidence were included: dietary patterns or dietary quality indices (n = 6), foods (n = 13), beverages (n = 4), alcohol (n = 7), macronutrients (n =15), and micronutrients (n =19). No meta-analyses had high methodological quality. Five meta-analyses exhibited small study effects; one meta-analysis showed evidence of excess significance bias. No dietary factors showed convincing or highly suggestive evidence of association with RCC in the overall analysis. Two protective factors had suggestive evidence (vegetables (0.74, 95% confidence interval 0.63 to 0.86) and vitamin C (0.77, 0.66 to 0.90)) in overall analysis. One protective factor had convincing evidence (moderate drinking (0.77, 0.70 to 0.84)) in Europe and North America and one protective factor had highly suggestive evidence (cruciferous vegetables (0.78, 0.70 to 0.86)) in North America. CONCLUSIONS: Although many meta-analyses have assessed associations between dietary factors and RCC, no high-quality evidence exists (classes I and II) in the overall analysis. Increased intake of vegetables and vitamin C is negatively associated with RCC risk. Moderate drinking might be beneficial for Europeans and North Americans, and cruciferous vegetables might be beneficial to North Americans, but the results should be interpreted with caution. More researches are needed in the future. TRIAL REGISTRATION: PROSPERO CRD42021246619.