Carcinogenesis is closely related to the expression, maintenance, and stability of DNA. These processes are regulated by one-carbon metabolism (1CM), which involves several vitamins of the complex B (folate, B2, B6, and B12), whereas alcohol disrupts the cycle due to the inhibition of folate activity. The relationship between nutrients related to 1CM (all aforementioned vitamins and alcohol) in breast cancer has been reviewed. The interplay of genes related to 1CM was also analyzed. Single nucleotide polymorphisms located in those genes were selected by considering the minor allele frequency in the Caucasian population and the linkage disequilibrium. These genes were used to perform several in silico functional analyses (considering corrected p-values < 0.05 as statistically significant) using various tools (FUMA, ShinyGO, and REVIGO) and databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GeneOntology (GO). The results of this study showed that intake of 1CM-related B-complex vitamins is key to preventing breast cancer development and survival. Also, the genes involved in 1CM are overexpressed in mammary breast tissue and participate in a wide variety of biological phenomena related to cancer. Moreover, these genes are involved in alterations that give rise to several types of neoplasms, including breast cancer. Thus, this study supports the role of one-carbon metabolism B-complex vitamins and genes in breast cancer; the interaction between both should be addressed in future studies.

BACKGROUND: Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population.
METHODS: Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital\'s cohort as well as TCGA-KIRC cohort.
RESULTS: VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses.
CONCLUSIONS: In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC.

This study aimed to identify novel biomarkers associated with cuproptosis in human nonobstructive azoospermia (NOA). We obtained 4 NOA microarray datasets (GSE145467, GSE9210, GSE108886, and GSE45885) from the NCBI Gene Expression Omnibus database and merged them into training set. Another NOA dataset (GSE45887) was used as validation set. Differentially expressed cuproptosis-related genes were identified from training set. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination were used to identify hub cuproptosis-related genes. We calculated the expression of the hub cuproptosis-related genes in both validation set and patients with NOA. Gene set variation analysis was used to explore their potential biological functions. The risk prediction model was built by logistic regression analysis and was evaluated in the validation set. Finally, we constructed a competing endogenous RNA network. The training set included 29 patents in the control group and 92 in the NOA group, and 10 cuproptosis-related differentially expressed genes were identified. Subsequently, we screened 6 hub cuproptosis-related genes (DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1) by least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination. GCSH, NFE2L2, NLRP3, and SLC31A1 expressed higher in NOA group than in control group (P < .05) in the validation set (4 patients in control and 16 in NOA groups), while the expression levels of GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 were higher in NOA group than in control group (P < .05) in our patients (3 patients in control and 4 in NOA groups). The model based on the 6-gene signature showed superior performance with an AUC value of 0.970 in training set, while 1.0 in validation set. Gene set variation analysis revealed a higher enrichment score of \"homologous recombination\" in the high expression groups of the 6 hub genes. Finally, we constructed a competing endogenous RNA network and found hsa-miR-335-3p and hsa-miR-1-3p were the most frequently related to the 6 hub genes. DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 may serve as predictors of cuproptosis and play important roles in the NOA pathogenesis.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.
METHODS: The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.
RESULTS: An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.
CONCLUSIONS: The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.

Objective: Exploring gene-age interactions associated with breast cancer prognosis based on epigenomic data. Methods: Differential expression analysis of DNA methylation was conducted using multiple independent epigenomic datasets of breast cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The false discovery rate (FDR) method was used for multiple corrections, retaining differentially methylated sites with q-FDR≤0.05. A three-stage analytic strategy was implemented, using a multivariable Cox proportional hazards regression model to examine gene-age interactions. In the discovery phase, signals with q-FDR ≤ 0.05 were screened out using TCGA-BRCA database. In validation phaseⅠ, the interaction was validated using GSE72245 data, with criteria of P≤0.05 and consistent effect direction. In validation phaseⅡ, the signals were further validated using GSE37754 and GSE75067 data. A prognostic prediction model was constructed by incorporating clinical indicators and interaction signals. Results: The three-stage analytic strategy identified a methylation site (cg16126280EBF1), which interacted with age to jointly affect the overall survival time of patients (interaction HR= 1.001 1,95%CI:1.000 7-1.001 5,P<0.001). Stratified analysis by age showed that the effect of hypermethylation of cg16126280EBF1 was completely opposite in younger patients (HR=0.550 5, 95%CI: 0.383 8-0.789 6, P=0.001) and older patients (HR=2.166 5, 95%CI: 1.285 2-3.652 2, P=0.004). Conclusions: The DNA methylation site cg16126280EBF1 exhibits an interaction with age, jointly influencing the prognosis of breast cancer in a complex association pattern. This finding contributes new population-based evidence for the development of age-specific targeted drugs.
目的： 基于表观基因组数据，探索乳腺癌预后基因-年龄交互作用。 方法： 利用癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）多个独立乳腺癌表观基因组数据集，进行DNA甲基化的差异表达分析。采用错误发现率（FDR）法进行多重校正，保留q-FDR≤0.05的差异表达甲基化位点。应用三阶段分析策略，采用多因素Cox比例风险回归模型检验基因-年龄交互作用。发现阶段使用TCGA-BRCA数据库筛选q-FDR≤0.05的信号。验证阶段Ⅰ使用GSE72245数据验证交互作用，标准为P≤0.05且效应方向一致。验证阶段Ⅱ使用GSE37754和GSE75067数据再次验证信号。通过结合临床指标与交互作用信号构建预后预测模型。 结果： 三阶段分析策略鉴定出一个甲基化位点（cg16126280EBF1），其与年龄存在交互作用，共同影响患者的生存时间（交互作用HR=1.001 1，95%CI：1.000 7~1.001 5，P<0.001）。年龄分层分析显示，cg16126280EBF1的高甲基化效应在乳腺癌年轻患者（HR=0.550 5，95%CI：0.383 8~0.789 6，P=0.001）和老年患者中完全相反（HR=2.166 5，95%CI：1.285 2~3.652 2，P=0.004）。 结论： DNA甲基化位点cg16126280EBF1与年龄存在交互作用，以复杂的关联模式共同影响乳腺癌预后，为年龄特异性靶向药物研发提供了新的人群证据。.

OBJECTIVE: To investigate the association of the genetic predisposition of specific gut microbiotas with the clinical outcome of ischemic stroke.
METHODS: We leveraged publicly available genome-wide association study (GWAS) data to perform Mendelian randomization (MR) analysis. The gut microbiota-related GWAS data from 18,340 individuals from the international consortium MiBioGen was used. The summary data for functional outcomes after ischemic stroke was obtained from the Genetics of Ischemic Stroke Functional Outcome (GISCOME) network meta-analysis. The primary outcomes were judged by the modified Rankin Scale (mRS). The principal analyses were conducted using the inverse-variance weighted (IVW) MR method. The Cochran\'s Q test, weighted median, MR-Egger regression, leave-one-SNP-out analysis, MR-Pleiotropy Residual Sum, and Outlier methods were adopted as sensitivity analyses. Furthermore, we performed bi-directional MR analysis and the MR Steiger directionality test to examine the direction of the causal relations.
RESULTS: The results demonstrated that the genetic predisposition of genus Lactococcus, genus Ruminococcaceae NK4A214 group, family Peptostreptococcaceae, and genus Odoribacter was positively associated with favorable functional outcome after ischemic stroke. Genus Collinsella, genus Ruminococcaceae UCG005, genus Akkermansia, genus Eubacterium oxidoreducens group, and family Verrucomicrobiaceae were identified to be associated with worse functional outcomes after ischemic stroke. Our results showed no evidence of heterogeneity, directional pleiotropic effects, or collider bias, and the sensitivity of our analysis was acceptable.
CONCLUSIONS: The genetic predisposition of different gut microbiotas was associated with the clinical outcome of ischemic stroke. Microbiota adjustment was a promising method to improve the clinical outcome of ischemic stroke.

The World Health Organization has considered the infertility as an international public health problem. Infertility affect nearly 1 in 7 couples and male component contributes to 50% of infertility cases. There is a clear link between male infertility and some cancers such as testicular germ cell, prostate and colon cancers. Two possibilities support this finding: 1) Cancer treatments can affect the fertility factors 2) Genetic profile of infertility genes have been altered in cancer patients. Although the previously published researches have mostly focused on the first factor, no article has yet confirmed the role of genetic factors. In this in silico study, we collected the large number of genes (n = 17703) involved in infertility. These genes were collected from NGS panel tests of male infertility and comprehensive literature review or online data base. The Prostate Adenocarcinoma genomic and transcriptomics raw data were downloaded from the cBioPortal Cancer dataset. This included with 494 patients of Prostate Cancer with 494 mutation data, 489 with CNA and 493 with RNA seqV2 data. TCGA RNA-Seq raw data was extracted in R using the cgdsr extension package with a threshold of ±2 relative to normal samples. The observed data showed that male infertility genes have been distributed through the human genome. Among the 17703 analyzed genes of this study, the genomic profile of three genes including OR9Q1, H4C6 and PSG7 were changed approximately in 100% of (n = 493) patients. In most of patients (>98%), genetic alteration was related to change in gene expression. In conclusion, this study showed that the genomic and transcriptomics patterns of some male-infertility genes are notably altered in patients of prostate cancer and suggested a possible role of genetic factors in occurrence of infertility in cancer patients. Our information can be used as a source for the design of genetic database of male-infertility.

OBJECTIVE: The postnatal development of craniofacial bone plays a crucial role in shaping the overall structure and functionality of the skull and face. Understanding the underlying mechanisms of this intricate process is essential for both clinical and research purposes. In this study, the authors conducted a bioinformatics analysis using the Gene Expression Omnibus database to investigate the molecular pathways and regulatory networks involved in the postnatal development of craniofacial bone.
METHODS: In this study, the online Gene Expression Omnibus microarray expression profiling data set GSE27976 was used to identify differentially expressed genes (DEGs) in different age groups. Protein-Protein Interaction network analyses, functional enrichment, and hub genes analysis were performed. The differences in immune infiltration and microenvironment among different types of cells were also analyzed.
RESULTS: In total, 523 DEGs, including 287 upregulated and 236 downregulated genes, were identified. GO and KEGG analysis showed that the DEGs were significantly enriched in multiple signaling pathways, such as skeletal system morphogenesis, osteoblast differentiation, and stem cell differentiation. Immune infiltration and microenvironment characteristics analysis showed that there were significant differences in fibroblasts, mesenchymal stem cell, osteoblast, stroma score, and microenvironment score between the two groups. Five hub genes, including IGF1, IL1B, ICAM1, MMP2 , and brain-derived neurotrophic factor, were filled out.
CONCLUSIONS: The findings of this study showed a significant shift in gene expression towards osteogenesis during the first 12 months after birth. These findings emphasize the critical role of the postnatal period in craniofacial bone development and provide valuable insights into the molecular mechanisms underlying this process.

BACKGROUND: Previous studies have reported low serum 25-hydroxyvitamin D [25(OH)D] levels in dermatomyositis (DM) patients, but the exact causal relationship between them remains elusive. Our aim is to confirm the causal relationship between 25(OH)D and DM risk through a Mendelian randomization study.
METHODS: Retrieve genome-wide association study (GWAS) data on 25(OH)D (n = 441 291) and DM (n cases = 201, n controls = 172 834) from the GWAS database (https://gwas.mrcieu.ac.uk/). Select single-nucleotide polymorphisms (SNPs) strongly correlated with 25(OH)D as instrumental variables (IVs). The primary analytical approach involves the use of the inverse-variance weighted method (IVW), supplemented by MR-Egger regression and weighted median methods to enhance the reliability of the results. Heterogeneity and sensitivity analyses were conducted using Cochran\'s Q and leave-one-out approaches, respectively.
RESULTS: The IVW analysis confirmed a positive causal relationship between genetic variation in 25(OH)D levels and DM (OR = 2.36, 95% CI = 1.01-5.52, p = .048). Although not statistically significant (all p > .05), the other methods also suggested a protective effect of 25(OH)D on DM. Based on MR-Egger intercepts and Cochran\'s Q analysis, the selected SNPs showed no horizontal pleiotropy and heterogeneity. Sensitivity analysis demonstrated the robustness of the results against individual SNPs.
CONCLUSIONS: We provide the first evidence of a causal relationship between 25(OH)D levels and DM. Our findings support the importance of measuring serum 25(OH)D levels and considering vitamin D supplementation in clinical practice for patients with DM.

Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.