Long non-coding RNA ATB expedites non-small cell lung cancer progression by the miR-200b/fibronectin 1 axis.

BACKGROUND: Long non-coding RNA (lncRNA) ATB belongs to an active modulator in multiple cancers, but its expression along with potential underlying non-small cell lung cancer (NSCLC) is obscure. Our study aimed to investigate the role and potential mechanism of LncRNA ATB in NSCLC. METHODS: LncRNA ATB expression in NSCLC tissues and cell lines was detected by qRT-PCR. Effects of LncRNA ATB on NSCLC cell proliferation, migration and invasion were assessed by MTS, colony formation and transwell assays. The connection among LncRNA ATB, miR-200b and fibronectin 1 (FN1) was determined by bioformatics prediction and luciferase reporter assay. RESULTS: In this research, upregulation of LncRNA ATB was discovered in NSCLC tissue samples and cell lines. LncRNA ATB was positively related to advanced tumor phase as well as lymph node metastasis. Cell function assays reflected LncRNA ATB expedited NSCLC cells proliferation, migration and invasion. LncRNA ATB promoted fibronectin 1 (FN1) expression via inhibiting miR-200b. Furthermore, LncRNA ATB depletion suppressed NSCLC cells proliferation, migration and invasion, while miR-200b inhibitor or pcDNA-FN1 rescued these effects. CONCLUSION: In summary, our outcomes elucidated that LncRNA ATB/miR-200b axis expedited NSCLC cells proliferation, migration and invasion by up-regulating FN1.

Peripheral blood IRF1 expression as a marker for glucocorticoid sensitivity.

OBJECTIVE: Despite of the common usage of glucocorticoids (GCs), a significant portion of asthma patients exhibit GC insensitivity. This could be mediated by diverse mechanisms, including genomics. Recent work has suggested that measuring changes in gene expression may provide more predictive information about GC insensitivity than baseline gene expression alone, and that expression changes in peripheral blood may be reflective of those in the airway. METHODS: We performed in silico discovery using gene expression omnibus (GEO) data that evaluated GC effect on gene expression in multiple tissue types. Subsequently, candidate genes whose expression levels are affected by GC were examined in cell lines and in primary cells derived from human airway and blood. RESULTS: Through gene expression omnibus analysis, we identified interferon regulator factor 1 (IRF1), whose expression is affected by GC treatment in airway smooth muscle cells, normal human bronchial epithelial (NHBE) cells, and lymphoblastoid cell lines (LCLs). Significant IRF1 downregulation post GC exposure was confirmed in two cultured airway epithelial cell lines and primary NHBE cells (P<0.05). We observed large interindividual variation in GC-induced IRF1 expression changes among primary NHBE cells tested. Significant downregulation of IRF1 was also observed in six randomly selected LCLs (P<0.05), with variable degrees of downregulation among different samples. In peripheral blood mononuclear cells obtained from healthy volunteers, variable downregulation of IRF1 by GC was also shown. NFKB1, a gene whose expression is known to be downregulated by GC and the degree of downregulation of which is reflective of GC response, was used as a control in our study. IRF1 shows more consistent downregulation across tissue types when compared with NFKB1. CONCLUSION: Our results suggest that GC-induced IRF1 gene expression changes in peripheral blood could be used as a marker to reflect GC response in the airway.

Causal associations of plasma omega-3 polyunsaturated fatty acids with sarcopenia-related traits: a two-sample Mendelian randomization study.

OBJECTIVE: To evaluate the causal effect of plasma omega-3 polyunsaturated fatty acids (PUFAs) on sarcopenia-related traits (lean mass, grip strength and walking pace) utilizing two-sample Mendelian randomization (MR) approach. METHODS: Based on genome-wide association study (GWAS) summary statistics, we performed two-sample MR applying the inverse variance weighted (IVW) as the primary method, supplemented with four additional sensitivity analyses. Furthermore, multivariable MR (MVMR) was applied to assess these associations independent of alcohol drinking, type 2 diabetes (T2D), triglycerides (TG), estimated glomerular filtration rate (eGFR) and C-reactive protein (CRP). RESULTS: In univariable MR, the IVW analysis suggested no significant causal effect of genetically determined plasma omega-3 PUFAs on fat-free mass (right leg: ß = 0.01, 95% CI = -0.02 to 0.05, P = 0.375; left leg: ß = 0.01, 95% CI = -0.02 to 0.04, P = 0.446; right arm: ß = 0.01, 95% CI = -0.02 to 0.05, P = 0.376; left arm: ß = 0.01, 95% CI = -0.02 to 0.04, P = 0.384; trunk:ß = 0.02, 95% CI = -0.02 to 0.06, P = 0.283; whole: ß = 0.01, 95% CI = -0.03 to 0.04, P = 0.631), grip strength (right hand: ß = -0.01, 95% CI = -0.03 to 0.01, P = 0.387; left hand: ß = -0.01, 95% CI = -0.02 to 0.01, P = 0.553) and walking pace (ß = 0.00, 95% CI = -0.01 to 0.02, P = 0.575), and sensitive analysis generated similar non-significant results. Furthermore, the MVMR revealed no independent causal association. CONCLUSIONS: Genetically determined plasma omega-3 PUFAs have no causal effect on sarcopenia-related traits.

Pan-cancer Multi-omics Analysis Reveals HMGN1 as a Potential Prognostic and Immune Infiltration-associated Biomarker.

BACKGROUND: The High Mobility Group Nucleosomal Binding Domain 1 Gene (HMGN1) is crucial for epigenetic regulation. However, the specific function of HMGN1 in cancer development is unclear. METHODS: Raw data on HMGN1 expression were procured from Genotype-Tissue Expression (GTEx), the University of Alabama- Birmingham CANcer data analysis Portal (UALCAN), and The Cancer Genome Atlas (TCGA). Thereafter, the pan-cancer analysis was implemented to understand the HMGN1 expression patterns, prognostic value, and immunological features. Furthermore, the Gene Set Enrichment Analysis (GSEA) was executed via R language. In addition, the relationship between HMGN1 and the sensitivity of antitumor drugs was also determined. Finally, real-time PCR (RT-PCR) experiments were carried out. RESULTS: Pan-cancer analysis revealed that HMGN1 was upregulated in several solid tumors and was associated with pathological staging and poor prognosis. In addition, HMGN1 was found to be involved in regulating the tumor microenvironment. The GSEA enrichment analysis indicated that HMGN1 assisted in the regulation of oncogenic processes, especially metabolic and immune pathways. Furthermore, HMGN1 expression was linked to microsatellite instability (MSI) and tumor mutational burden (TMB) across diverse tumor types. RT-PCR assays indicated that HMGN1 was overexpressed in the gastric and breast cancer cell lines and tissues. CONCLUSION: This study highlighted the potential of HMGN1 as a biomarker for pan- - cancer analysis.

Investigating the impact of inflammatory response-related genes on renal fibrosis diagnosis: a machine learning-based study with experimental validation.

Renal fibrosis plays a crucial role in the progression of renal diseases, yet the lack of effective diagnostic markers poses challenges in scientific and clinical practices. In this study, we employed machine learning techniques to identify potential biomarkers for renal fibrosis. Utilizing two datasets from the GEO database, we applied LASSO, SVM-RFE and RF algorithms to screen for differentially expressed genes related to inflammatory responses between the renal fibrosis group and the control group. As a result, we identified four genes (CCL5, IFITM1, RIPK2, and TNFAIP6) as promising diagnostic indicators for renal fibrosis. These genes were further validated through in vivo experiments and immunohistochemistry, demonstrating their utility as reliable markers for assessing renal fibrosis. Additionally, we conducted a comprehensive analysis to explore the relationship between these candidate biomarkers, immunity, and drug sensitivity. Integrating these findings, we developed a nomogram with a high discriminative ability, achieving a concordance index of 0.933, enabling the prediction of disease risk in patients with renal fibrosis. Overall, our study presents a predictive model for renal fibrosis and highlights the significance of four potential biomarkers, facilitating clinical diagnosis and personalized treatment. This finding presents valuable insights for advancing precision medicine approaches in the management of renal fibrosis.Communicated by Ramaswamy H. Sarma.

[The effect of environmental factors and DNA methylation on type 2 diabetes mellitus].

Type 2 diabetes mellitus (T2DM) is a glucose metabolic disorder driven by both genetic and environmental factors. Recent DNA methylation studies have established that T2DM may be contributed by environmental factors through the regulation of DNA methylation. Human and animal model studies have made much progress on the interaction between DNA methylation of T2DM genes and environmental factors in multiple tissues. Current studies on DNA methylation of T2DM genes mainly focus on glucose and energy metabolism, inflammation, and so on. This review comprehensively introduces the DNA methylation studies for the genes involved in T2DM and its related environmental factors.

Relationship between blood viscosity and existence and severity of carotid artery plaque.

BACKGROUND: Accumulating evidence shows that the increase in blood viscosity (BV) is an independent risk factor for atherosclerosis and its related diseases, but as far as we know, there are few studies on the relationship between blood viscosity and carotid plaque severity. Therefore, we aimed to investigate the relationship between blood viscosity and the presence of carotid plaques, and further explore its relationship with the severity of carotid plaques. METHODS: We retrospectively analyzed the data of consecutive subjects in the physical examination center of the Affiliated Hospital of Ningbo University Medical College from January 2022 to May 2022.The parameters of blood viscosity include the whole blood viscosity (WBV) at high, middle, and low shear rate, plasma viscosity (PV), hematocrit (HCT), rigidity "k", rigidity index (RI), aggregation index (AI) and electrophoresis rate (ER), and standardized BV calculated by Quemada's equation were included in the study. Carotid plaque score (CPS) was used to measure the severity of carotid artery disease, and participants were divided into mild, moderate, and severe groups according to the quartile of the score. Independent samples t-test and one-way ANOVA were used to compare normally distributed continuous variables between two or more independent groups, respectively. Binary logistic regression was used to evaluate the risk factors of carotid plaque. RESULTS: 314 men were enrolled in the study, of which 165 participants were diagnosed with Carotid artery plaque (CAP) (66.9%). Compared with the CAP- group, the WBV and PV of the CAP+group decreased, but the difference only existed in the PV (p = 0.001). However, standardized BV values (HCT set at 0.45) were higher in the CAP+group than in the CAP- group (3.8643±0.35431vs 3.9542±0.64871, p = 0.188). Regarding the rigidity and aggregation of RBC, the parameters including rigidity "k", RI, AI and ER increased in the CAP+group compared with the CAP- group. The difference was statistically significant in k and ER (p = 0.04, p = 0.009). To assess the severity of carotid plaque, we divided the participants into mild, moderate, and severe groups by using the tertile of CPS value. The mild group was defined as CPS≤0.5 (n = 108), the moderate group as 0.5 < CPS≤1.7 (n = 105), and the severe group as CPS > 1.7 (n = 101). It was found that WBV and PV decreased with the increase of plaque severity, but the difference among the three groups was significant in PV (F = 8.073, p < 0.0001). In addition, with the severity of plaque from mild to severe, standardized BV gradually increased, which were 3.8611±0.34845, 3.8757±0.36637, 3.9007±0.38353 respectively. The difference between the groups was close to statistically significant (F = 2.438, p = 0.089). The values of parameters describing erythrocyte aggregation and rigidity increased among the mild, moderate, and severe groups. The difference was statistically significant in RBC rigidity "k" and ER of RBC (F = 3.863, p = 0.022; F = 5.897, p = 0.003, respectively). CONCLUSION: Increased blood viscosity is a risk factor for carotid plaque, but its increase may be hidden by decreased hematocrit. Therefore, it is necessary to comprehensively analyze various parameters of blood viscosity, such as the standardized BV calculated by Quemada's equation, which may provide more useful reference value.

Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy.

Introduction: Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME. Methods: Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR. Results: Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed. Discussion: In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy.

Development and validation of an inflammatory response-related signature in triple negative breast cancer for predicting prognosis and immunotherapy.

Background: Inflammation is one of the most important characteristics of tumor tissue. Signatures based on inflammatory response-related genes (IRGs) can predict prognosis and treatment response in a variety of tumors. However, the clear function of IRGs in the triple negative breast cancer (TNBC) still needs to be explored. Methods: IRGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO). Verification analyses were conducted to show the robustness of the signature. The expression of risk genes was identified by RT-qPCR. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool. Results: The IRGs signature, comprised of four genes, was developed and was shown to be highly correlated with the prognoses of TNBC patients. In contrast with the performance of the other individual predictors, we discovered that the IRGs signature was remarkably superior. Also, the ImmuneScores were elevated in the low-risk group. The immune cell infiltration showed significant difference between the two groups, as did the expression of immune checkpoints. Conclusion: The IRGs signature could act as a biomarker and provide a momentous reference for individual therapy of TNBC.

Identification of a novel reactive oxygen species (ROS)-related genes model combined with RT-qPCR experiments for prognosis and immunotherapy in gastric cancer.

Reactive oxygen species play a crucial role in the prognosis and tumor microenvironment (TME) of malignant tumors. An ROS-related signature was constructed in gastric cancer (GC) samples from TCGA database. ROS-related genes were obtained from the Molecular Signatures Database. Consensus clustering was used to establish distinct ROS-related subtypes related to different survival and immune cell infiltration patterns. Sequentially, prognostic genes were identified in the ROS-related subtypes, which were used to identify a stable ROS-related signature that predicted the prognosis of GC. Correlation analysis revealed the significance of immune cell iniltration, immunotherapy, and drug sensitivity in gastric cancers with different risks. The putative molecular mechanisms of the different gastric cancer risks were revealed by functional enrichment analysis. A robust nomogram was established to predict the outcome of each gastric cancer. Finally, we verified the expression of the genes involved in the model using RT-qPCR. In conclusion, the ROS-related signature in this study is a novel and stable biomarker associated with TME and immunotherapy responses.

Corrigendum: Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy.

[This corrects the article DOI: 10.3389/fimmu.2023.1128390.].

Construction and validation of a signature for T cell-positive regulators related to tumor microenvironment and heterogeneity of gastric cancer.

Background: Positive regulators of T cell function play a vital role in the proliferation and differentiation of T cells. However, their functions in gastric cancer have not been explored so far. Methods: The TCGA-STAD dataset was utilized to perform consensus clustering in order to identify subtypes related to T cell-positive regulators. The prognostic differentially expressed genes of these subtypes were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. To validate the robustness of the identified signature, verification analyses were conducted across the TCGA-train, TCGA-test, and GEO datasets. Additionally, a nomogram was constructed to enhance the clinical efficacy of this predictive tool. Transwell migration, colony formation, and T cell co-culture assays were used to confirm the function of the signature gene in gastric cancer and its influence on T cell activation. Results: Two distinct clusters of gastric cancer, related to T cell-positive regulation, were discovered through the analysis of gene expression. These clusters exhibited notable disparities in terms of survival rates (P = 0.028), immune cell infiltration (P< 0.05), and response to immunotherapy (P< 0.05). Furthermore, a 14-gene signature was developed to classify gastric cancer into low- and high-risk groups, revealing significant differences in survival rates, tumor microenvironment, tumor mutation burden, and drug sensitivity (P< 0.05). Lastly, a comprehensive nomogram model was constructed, incorporating risk factors and various clinical characteristics, to provide an optimal predictive tool. Additionally, an assessment was conducted on the purported molecular functionalities of low- and high-risk gastric cancers. Suppression of DNAAF3 has been observed to diminish the migratory and proliferative capabilities of gastric cancer, as well as attenuate the activation of T cells induced by gastric cancer within the tumor microenvironment. Conclusion: We identified an ideal prognostic signature based on the positive regulators of T cell function in this study.

Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics.

Background: Recently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagnostic and therapeutic tools available for the disease are insufficient, necessitating the screening of potential biomarkers to predict renal fibrosis. Methods: Using the Gene Expression Omnibus (GEO) database, we obtained two gene array datasets (GSE76882 and GSE22459) from patients with renal fibrosis and healthy individuals. We identified differentially expressed genes (DEGs) between renal fibrosis and normal tissues and analyzed possible diagnostic biomarkers using machine learning. The diagnostic effect of the candidate markers was evaluated using receiver operating characteristic (ROC) curves and verified their expression using Reverse transcription quantitative polymerase chain reaction (RT-qPCR). The CIBERSORT algorithm was used to determine the proportions of 22 types of immune cells in patients with renal fibrosis, and the correlation between biomarker expression and the proportion of immune cells was studied. Finally, we developed an artificial neural network model of renal fibrosis. Results: Four candidate genes namely DOCK2, SLC1A3, SOX9 and TARP were identified as biomarkers of renal fibrosis, with the area under the ROC curve (AUC) values higher than 0.75. Next, we verified the expression of these genes by RT-qPCR. Subsequently, we revealed the potential disorder of immune cells in the renal fibrosis group through CIBERSORT analysis and found that immune cells were highly correlated with the expression of candidate markers. Conclusion: DOCK2, SLC1A3, SOX9, and TARP were identified as potential diagnostic genes for renal fibrosis, and the most relevant immune cells were identified. Our findings provide potential biomarkers for the diagnosis of renal fibrosis.

Interleukin-41 diminishes cigarette smoke-induced lung inflammation in mice.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and other inflammatory lung illnesses are markedly exacerbated by cigarette smoke (CS). The novel cytokine interleukin (IL)-41 has immunoregulatory effects, but data on its function in lung inflammation caused by CS are limited and inconclusive. Our study aimed to investigate the ability of IL-41 to protect against CS-induced lung inflammation in vivo. METHODS: In this model, mice were exposed to six cigarettes three times daily for 1 h, with 4-hour intervals between exposures, for 5 consecutive days. Mice received an intraperitoneal dose of IL-41 or a negative control 1 day prior to their initial exposure to CS. On day 6, mice were sacrificed to assess the impact of IL-41 on CS-induced lung inflammation. RESULTS: We found that IL-41 pre-treatment alleviated pulmonary inflammatory infiltration and lung tissue lesions. IL-41 pre-treatment also limited CS-induced weight loss, and resulted in lower numbers of macrophages in the bronchoalveolar lavage fluid and lower percentages of neutrophils and monocytes in the blood. Furthermore, it promoted the polarization of M2 macrophages rather than M1 macrophages, as determined by immunohistochemistry. Consistent with its effects on M2 polarization, pre-treatment with IL-41 was associated with higher levels of IL-10 in the bronchoalveolar lavage fluid and lung tissues of CS-exposed animals and lower production of tumor necrosis factor-α, IL-6 and IL-1ß in the serum and lung tissues. CONCLUSIONS: These findings suggest that IL-41 could be used therapeutically to treat CS-induced lung inflammatory disorders as it inhibits CS-induced pulmonary inflammation when administered in vivo in mice.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma.

Background: Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses. Methods: The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool. Results: The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes. Conclusion: The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

Predictive Value of Noninvasive Peripheral Atherosclerosis Measurement for Coronary Artery Disease in Patients with Long T2DM Duration.

Objective: This study aimed to compare the predictive value of carotid or femoral artery ultrasound for coronary artery disease (CAD) in type 2 Diabetes mellitus (T2DM) patients free from known CAD, and to assess the relationship with the severity of coronary artery stenosis. Methods: Cross-sectional study in adults with a T2DM duration of at least 5 years and without established CAD. Carotid plaque score (CPS) and Gensini score were used to measure the severity of carotid and coronary artery stenosis, respectively, and patients were divided into no or mild group, moderate group, and severe group according to the tertile of the score. Univariate and multivariate logistic regression analysis was used to explore the possible risk factors for CAD. Receiver operating characteristic (ROC) curves were created to determine the most accurate assessment for detecting significant CAD (≥50% stenosis). Results: 245 patients (137 male) aged 68.21±9.5 years (range: 36-95 years), with T2DM duration 12.04± 6.17 years (range: 5-34 years), and without CVD were included. CAD was diagnosed in 165 patients (67.3%). Multiple regression analysis showed that CPS, femoral plaque, and smoking were independently and positively correlated with CAD. CPS yielded the highest area under the curve for detecting significant coronary disease (AUC=0.7323). In contrast, the area under the curve of femoral artery plaque and carotid intima-media thickness was lower than 0.7, which was at a lower prediction level. Conclusion: In patients with long T2DM duration, CPS has a higher ability to predict the occurrence and severity of CAD. However, femoral artery plaque has special value in predicting moderate to severe coronary artery disease in patients with long-term T2DM.

Corrigendum: Prognostic implication and immunotherapy response prediction of a ubiquitination-related gene signature in breast cancer.

[This corrects the article DOI: 10.3389/fgene.2022.1038207.].

Diet-derived circulating antioxidants and risk of knee osteoarthritis, hip osteoarthritis and rheumatoid arthritis: a two-sample Mendelian randomization study.

Objective: To examine the causal associations of diet-derived circulating antioxidants with knee osteoarthritis (OA), hip OA, and rheumatoid arthritis (RA) within the two-sample Mendelian randomization (MR) framework. Method: Independent single-nucleotide polymorphisms (SNPs) significantly associated with circulating levels of diet-derived antioxidants (retinol, ß-carotene, lycopene, vitamin C and vitamin E) were extracted as genetic instruments. Summary statistics of genetic instruments associated with knee OA, hip OA, and RA were obtained from corresponding genome-wide association studies (GWASs). The inverse-variance weighted (IVW) was applied as the primary analysis method, with four sensitivity analysis approaches employed to evaluate the robustness of the primary results. Results: Genetically determined per unit increment of absolute circulating levels of retinol was significantly associated with a reduced risk of hip OA [odds ratio (OR) = 0.45, 95% confidence interval (CI) 0.26-0.78, p = 4.43 × 10-3], while genetically determined per unit increase in absolute circulating levels of ß-carotene was suggestively associated with increased risk of RA (OR = 1.32, 95% CI 1.07-1.62, p = 9.10 × 10-3). No other causal association was found. Significant evidence for heterogeneity and pleiotropic outlier was only identified when absolute circulating vitamin C was evaluated as the exposure, whereas all sensitive analysis provided consistently non-significant results. Conclusion: Our results demonstrated that genetically determined lifelong higher exposure to absolute circulating levels of retinol is associated with a decreased risk of hip OA. Further MR study with more genetic instruments for absolute circulating levels of antioxidants are needed to confirm our results.

Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells.

Background: Renal fibrosis is a physiological and pathological characteristic of chronic kidney disease (CKD) to end-stage renal disease. Since renal biopsy is the gold standard for evaluating renal fibrosis, there is an urgent need for additional non-invasive diagnostic biomarkers. Methods: We used R package "limma" to screen out differently expressed genes (DEGs) based on Epithelial-mesenchymal transformation (EMT), and carried out the protein interaction network and GO, KEGG enrichment analysis of DEGs. Secondly, the least absolute shrinkage and selection operator (LASSO), random forest tree (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify candidate diagnostic genes. ROC curves were plotted to evaluate the clinical diagnostic value of these genes. In addition, mRNA expression levels of candidate diagnostic genes were analyzed in control samples and renal fibrosis samples. CIBERSORT algorithm was used to evaluate immune cells level. Additionally, gene set enrichment analysis (GSEA) and drug sensitivity were conducted. Results: After obtaining a total of 24 DEGs, we discovered that they were mostly involved in several immunological and inflammatory pathways, including NF-KappaB signaling, AGE-RAGE signaling, and TNF signaling. Five genes (COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA) were subsequently identified as biomarkers for renal fibrosis through machine learning, and their expression levels were confirmed by validation cohort data sets and in vitro RT-qPCR experiment. The AUC values of these five genes demonstrated significant clinical diagnostic value in both the training and validation sets. After that, CIBERSORT analysis showed that these biomarkers were strongly associated with immune cell content in renal fibrosis patients. GSEA also identifies the potential roles of these diagnostic genes. Additionally, diagnostic candidate genes were found to be closely related to drug sensitivity. Finally, a nomogram for diagnosing renal fibrosis was developed. Conclusion: COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA are promising diagnostic biomarkers of tissue and serum for renal fibrosis.

Clinical characteristics and cancer-specific survival analysis of double primary cancer patients with lung cancer as the first primary cancer.

The objective of this study is to explore the prognostic factors of double primary cancer patients with lung cancer as the first primary cancer (FPC). The Surveillance, Epidemiology, and End Results (SEER) database is a database established by the National Institutes of Research for cancer registration purposes, which collects relatively complete demographic characteristics and clinical data for assessing the epidemiological characteristics of cancer worldwide. Clinical data on patients with a clear histopathological diagnosis of double primary with lung cancer as the FPC were identified and collected from the SEER database from 2010 to 2015. Survival curves were plotted by Kaplan-Meier survival analysis. Independent prognostic factors of patients were analyzed by COX proportional risk model. Clinical data were collected from a total of 9306 patients, including 6516 patients in the modeling group and 2790 patients in the validation group. When we retrieved that the FPC was lung cancer, we found that the most common site of the second primary cancer was located in the respiratory system (54.0%). In addition, the most common site of first primary lung cancer in patients with double primary cancer was the right upper lobe (33.3%). A total of 14 independent prognostic factors were included, and the constructed survival nomogram had high accuracy and clinical applicability. The nomogram established in this study can help to raise awareness of clinical workers and the importance of such diseases, and guide the treatment and follow-up strategies.