Decoding the tumor microenvironment and molecular mechanism: unraveling cervical cancer subpopulations and prognostic signatures through scRNA-Seq and bulk RNA-seq analyses.

Background: Cervical carcinoma (CC) represents a prevalent gynecological neoplasm, with a discernible rise in prevalence among younger cohorts observed in recent years. Nonetheless, the intrinsic cellular heterogeneity of CC remains inadequately investigated. Methods: We utilized single-cell RNA sequencing (scRNA-seq) transcriptomic analysis to scrutinize the tumor epithelial cells derived from four specimens of cervical carcinoma (CC) patients. This method enabled the identification of pivotal subpopulations of tumor epithelial cells and elucidation of their contributions to CC progression. Subsequently, we assessed the influence of associated molecules in bulk RNA sequencing (Bulk RNA-seq) cohorts and performed cellular experiments for validation purposes. Results: Through our analysis, we have discerned C3 PLP2+ Tumor Epithelial Progenitor Cells as a noteworthy subpopulation in cervical carcinoma (CC), exerting a pivotal influence on the differentiation and progression of CC. We have established an independent prognostic indicator-the PLP2+ Tumor EPCs score. By stratifying patients into high and low score groups based on the median score, we have observed that the high-score group exhibits diminished survival rates compared to the low-score group. The correlations observed between these groups and immune infiltration, enriched pathways, single-nucleotide polymorphisms (SNPs), drug sensitivity, among other factors, further underscore their impact on CC prognosis. Cellular experiments have validated the significant impact of ATF6 on the proliferation and migration of CC cell lines. Conclusion: This study enriches our comprehension of the determinants shaping the progression of CC, elevates cognizance of the tumor microenvironment in CC, and offers valuable insights for prospective CC therapies. These discoveries contribute to the refinement of CC diagnostics and the formulation of optimal therapeutic approaches.

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies.

Background: Idiopathic pulmonary fibrosis (IPF) is a rare interstitial lung disease with a poor prognosis that currently lacks effective treatment methods. Preventing the acute exacerbation of IPF, identifying the molecular subtypes of patients, providing personalized treatment, and developing individualized drugs are guidelines for predictive, preventive, and personalized medicine (PPPM / 3PM) to promote the development of IPF. Oxidative stress (OS) is an important pathological process of IPF. However, the relationship between the expression levels of oxidative stress-related genes (OSRGs) and clinical indices in patients with IPF is unclear; therefore, it is still a challenge to identify potential beneficiaries of antioxidant therapy. Because PPPM aims to recognize and manage diseases by integrating multiple methods, patient stratification and analysis based on OSRGs and identifying biomarkers can help achieve the above goals. Methods: Transcriptome data from 250 IPF patients were divided into training and validation sets. Core OSRGs were identified in the training set and subsequently clustered to identify oxidative stress-related subtypes. The oxidative stress scores, clinical characteristics, and expression levels of senescence-associated secretory phenotypes (SASPs) of different subtypes were compared to identify patients who were sensitive to antioxidant therapy to conduct differential gene functional enrichment analysis and predict potential therapeutic drugs. Diagnostic markers between subtypes were obtained by integrating multiple machine learning methods, their expression levels were tested in rat models with different degrees of pulmonary fibrosis and validation sets, and nomogram models were constructed. CIBERSORT, single-cell RNA sequencing, and immunofluorescence staining were used to explore the effects of OSRGs on the immune microenvironment. Results: Core OSRGs classified IPF into two subtypes. Patients classified into subtypes with low oxidative stress levels had better clinical scores, less severe fibrosis, and lower expression of SASP-related molecules. A reliable nomogram model based on five diagnostic markers was constructed, and these markers' expression stability was verified in animal experiments. The number of neutrophils in the immune microenvironment was significantly different between the two subtypes and was closely related to the degree of fibrosis. Conclusion: Within the framework of PPPM, this work comprehensively explored the role of OSRGs and their mediated cellular senescence and immune processes in the progress of IPF and assessed their capabilities aspredictors of high oxidative stress and disease progression,targets of the vicious loop between regulated pulmonary fibrosis and OS for targeted secondary and tertiary prevention, andreferences for personalized antioxidant and antifibrotic therapies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00334-4.

Non-linear association between composite dietary antioxidant index and depression.

Background: Growing evidence has shown that the antioxidant diet is a protective factor against depression. However, the relationship between the Composite Dietary Antioxidant Index (CDAI), an important measure of antioxidant diet, and depression has received little attention. Therefore, we investigated the relationship between CDAI and depression through a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. Methods: The association between CDAI and depression was investigated using a weighted multiple logistic regression model with subgroup analysis. Non-linear correlations were explored using fitted smoothing curves. And we used a recursive method to figure out the turning point and build a weighted two-piece linear regression model. Results: In the multivariate logistic regression model with full adjustment for confounding variables, the ORs (95% CI) for the association between CDAI and depression were 0.83 (0.78, 0.88). Moreover, a non-linear association was found, with 0.16 being the inflection point. Before the inflection point, each unit increase in CDAI was associated with a 30% decrease in the risk of depression. After the inflection point, the risk of depression was found to be reduced by 11% for each unit increase. None of the interactions in all subgroup analyses were statistically significant. Conclusions: Our study highlighted a negative non-linear association between CDAI and depression in a nationally representative sample of US adults. Further clinical and basic research is needed to explore their association better.

The severity of depression is associated with pelvic inflammatory diseases: A cross-sectional study of the United States National Health and Nutrition Examinations from 2013 to 2018.

Purpose: As depression in patients with pelvic inflammatory diseases (PID) has received increasing attention in recent years, this study aims to investigate the relationship between depression severity and risk factors for pelvic inflammatory disease, and to provide new perspectives in the treatment of PID. Patients and methods: Multivariate regression was used to evaluate the association between pelvic inflammatory disease and the severity of depression. Females who participated in the United States National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018 were included. In addition, risk factors for PID and depression were also included in the analysis as adjustment factors. Results: The risk of developing PID was associated with depressive status (odds ratio, OR 1.10, 95% confidence interval, CI 1.08-1.12), especially in people with severe depression (odds ratio, OR 6.34, 95% confidence interval, CI 3.72-10.79). Subgroup analysis showed differences in the risk of PID among people with different characteristics. Conclusion: This study showed that there may be a potential positive association between depressive status and the prevalence of PID in the United States adult female population. Depression should be actively looked for in all patients with PID and treated appropriately.

D-Limonene Is a Potential Monoterpene to Inhibit PI3K/Akt/IKK-α/NF-κB p65 Signaling Pathway in Coronavirus Disease 2019 Pulmonary Fibrosis.

At the time of the prevalence of coronavirus disease 2019 (COVID-19), pulmonary fibrosis (PF) related to COVID-19 has become the main sequela. However, the mechanism of PF related to COVID (COVID-PF) is unknown. This study aimed to explore the key targets in the development of COVID-PF and the mechanism of d-limonene in the COVID-PF treatment. The differentially expressed genes of COVID-PF were downloaded from the GeneCards database, and their pathways were analyzed. d-Limonene was molecularly docked with related proteins to screen its pharmacological targets, and a rat lung fibrosis model was established to verify d-limonene's effect on COVID-PF-related targets. The results showed that the imbalance between collagen breakdown and metabolism, inflammatory response, and angiogenesis are the core processes of COVID-PF; and PI3K/AKT signaling pathways are the key targets of the treatment of COVID-PF. The ability of d-limonene to protect against PF induced by bleomycin in rats was reported. The mechanism is related to the binding of PI3K and NF-κB p65, and the inhibition of PI3K/Akt/IKK-α/NF-κB p65 signaling pathway expression and phosphorylation. These results confirmed the relationship between the PI3K-Akt signaling pathway and COVID-PF, showing that d-limonene has a potential therapeutic value for COVID-PF.

Catalpol Protects Against Pulmonary Fibrosis Through Inhibiting TGF-ß1/Smad3 and Wnt/ß-Catenin Signaling Pathways.

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by fibroblast proliferation and extracellular matrix remodeling; however, the molecular mechanisms underlying its occurrence and development are not yet fully understood. Despite it having a variety of beneficial pharmacological activities, the effects of catalpol (CAT), which is extracted from Rehmannia glutinosa, in IPF are not known. In this study, the differentially expressed genes, proteins, and pathways of IPF in the Gene Expression Omnibus database were analyzed, and CAT was molecularly docked with the corresponding key proteins to screen its pharmacological targets, which were then verified using an animal model. The results show that collagen metabolism imbalance, inflammatory response, and epithelial-mesenchymal transition (EMT) are the core processes in IPF, and the TGF-ß1/Smad3 and Wnt/ß-catenin pathways are the key signaling pathways for the development of pulmonary fibrosis. Our results also suggest that CAT binds to TGF-ßR1, Smad3, Wnt3a, and GSK-3ß through hydrogen bonds, van der Waals bonds, and other interactions to downregulate the expression and phosphorylation of Smad3, Wnt3a, GSK-3ß, and ß-catenin, inhibit the expression of cytokines, and reduce the degree of oxidative stress in lung tissue. Furthermore, CAT can inhibit the EMT process and collagen remodeling by downregulating fibrotic biomarkers and promoting the expression of epithelial cadherin. This study elucidates several key processes and signaling pathways involved in the development of IPF, and suggests the potential value of CAT in the treatment of IPF.