Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration.

 The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis­related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

Bioinformatics-based analysis of the dialog between COVID-19 and RSA.

Pregnant women infected with SARS-CoV-2 in early pregnancy may face an increased risk of miscarriage due to immune imbalance at the maternal-fetal interface. However, the molecular mechanisms underlying the crosstalk between COVID-19 infection and recurrent spontaneous abortion (RSA) remain poorly understood. This study aimed to elucidate the transcriptomic molecular dialog between COVID-19 and RSA. Based on bioinformatics analysis, 307 common differentially expressed genes were found between COVID-19 (GSE171110) and RSA (GSE165004). Common DEGs were mainly enriched in ribosome-related and cell cycle-related signaling pathways. Using degree algorithm, the top 10 hub genes (RPS27A, RPL5, RPS8, RPL4, RPS2, RPL30, RPL23A, RPL31, RPL26, RPL37A) were selected from the common DEGs based on their scores. The results of the qPCR were in general agreement with the results of the raw letter analysis. The top 10 candidate drugs were also selected based on P-values. In this study, we provide molecular markers, signaling pathways, and small molecule compounds that may associate COVID-19. These findings may increase the accurate diagnosis and treatment of COVID-19 patients.

Exploring the potential biological function of GRK2 in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a malignant tumor of the gastrointestinal tract with high morbidity and mortality. There is growing evidence that GRK2 plays a key role in the development and progression of several human cancers. However, the role and potential mechanisms of GRK2 in colon cancer (COAD) are unclear. METHODS: The expression data of GRK2 was downloaded from The Cancer Genome Atlas database (TCGA). Variation in GRK2 was explored based on the cBioPortal database. The TIMER and TISCH2 databases were used to analyse the relationship between GRK2 expression and tumor immune microenvironment (TME). A log-rank test was used to compare the prognosis of high and low expression of GRK2 groups. Detecting the effect of GRK2 on cell cycle and apoptosis induced by 5-Fluorouracil (5-FU) through the flow cytometry and detection of apoptosis-related molecules by Western blot. RESULTS: We demonstrated that GRK2 has a potential oncogenic role. GRK2 expression was upregulated in COAD, which predicted poorer overall survival in COAD patients. The cellular assays showed that GRK2 plays a role in the growth and proliferation of colon cancer cells, also the expression of GRK2 have relationship with the sensitivity of 5-FU and cell cycle progression. CONCLUSIONS: Our results suggest that high GRK2 expression is closely associated with the development of tumor and affects the 5-FU sensitivity.

Decreased expression of TXNIP is associated with poor prognosis and immune infiltration in kidney renal clear cell carcinoma.

The most prevalent and insidious type of kidney cancer is kidney clear cell carcinoma (KIRC). Thioredoxin-interacting protein (TXNIP) encodes a thioredoxin-binding protein involved in cellular energy metabolism, redox homeostasis, apoptosis induction and inflammatory responses. However, the relationship between TXNIP, immune infiltration and its prognostic value in KIRC remains unclear. Thus, the present study evaluated the potential for TXNIP as a prognostic marker in patients with KIRC. Data from The Cancer Genome Atlas were used to assess relative mRNA expression levels of TXNIP in different types of cancer. The protein expression levels of TXNIP were evaluated using the Human Protein Atlas. Enrichment analysis of genes co-expressed with TXNIP was performed to assess relevant biological processes that TXNIP may be involved in. CIBERSORT was used to predict the infiltration of 21 tumor-infiltrating immune cells (TIICs). Univariate and multivariate Cox regression analyses were used to assess the relationship between TXNIP expression and prognosis. Single-cell RNA-sequencing datasets were used to evaluate the mRNA expression levels of TXNIP in certain immune cells in KIRC. The CellMiner database was used to analyze the relationship between TXNIP mRNA expression and drug sensitivity in KIRC. The results from the present study demonstrated that TXNIP expression was significantly decreased in KIRC tissue compared with that in normal tissue, as confirmed by western blotting and reverse transcription-quantitative PCR. In addition, downregulated TXNIP expression was significantly associated with poor prognosis, a high histological grade and an advanced stage. The Cell Counting Kit-8 assay demonstrated that TXNIP overexpression significantly suppressed tumor cell proliferation. Univariate and multivariate Cox regression analyses indicated that TXNIP served as a separate prognostic factor in KIRC. Moreover, TXNIP expression was significantly correlated with the accumulation of several TIICs and its overexpression significantly downregulated the mRNA expression levels of CD25 and cytotoxic T-lymphocyte-associated protein 4, immune cell surface markers in CD4+ T lymphocytes. In conclusion, TXNIP may be used as a possible biomarker to assess unfavorable prognostic outcomes and identify immunotherapy targets in KIRC.

Fanconi Anemia Complementary Group A (FANCA) Facilitates the Occurrence and Progression of Liver Hepatocellular Carcinoma.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a serious liver disease worldwide, and its pathogenesis is complicated. AIMS: This study investigated the potential role of FANCA in the advancement and prognosis of LIHC. METHODS: Public databases, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blot (WB) and immunohistochemistry (IHC) were employed to measure FANCA expression between tumor and normal samples. The relationship between FANCA expression and prognosis of LIHC patients were examined. Functional enrichment of FANCA-related genes was performed. Furthermore, univariate and multivariate analyses were conducted to determine the independent prognosis value of FANCA in LIHC. Finally, influence of FANCA knockout on the proliferation, migration, and invasion of HepG2 cell was validated with cloning formation, CCK8, and Transwell assays. RESULTS: Expression analysis presented that FANCA had high expression level in LIHC tissues and cells. Receiver operating characteristic (ROC) curve analysis showed that FANCA was of great diagnosis value in LIHC. Clinicopathological analysis revealed that FANCA was significantly greater expressed in the advanced stage than in the early stage of LIHC. Univariate, multivariate, and Kaplan-Meier survival analysis confirmed that high expression of FANCA was strongly associated with poor survival of LIHC patients. In addition, high level of FANCA in LIHC showed a negative association with immunoinfiltrated B cells, T cells, and stromal scores. Moreover, Knockout of FANCA significantly inhibited HepG2 cell proliferative activity, migration, and invasion ability. CONCLUSIONS: Our data revealed that high level of FANCA was closely associated with LIHC malignant progression, suggesting its potential utility as a diagnostic, predictive indicator, and therapeutic target.

Focusing on the Abnormal Events of NPC1, NPC2, and NPC1L1 in Pan-Cancer and Further Constructing LUAD and KICH Prediction Models.

Cancer's high incidence and death rate jeopardize human health and life, and it has become a global public health issue. Some members of NPCs have been studied in a few cancers, but comprehensive and prognostic analysis is lacking in most cancers. In this study, we used the Cancer Genome Atlas (TCGA) data genomics and transcriptome technology to examine the differential expression and prognosis of NPCs in 33 cancer samples, as well as to investigate NPCs mutations and their effect on patient prognosis and to evaluate the methylation level of NPCs in cancer. The linked mechanisms and medication resistance were subsequently investigated in order to investigate prospective tumor therapy approaches. The relationships between NPCs and immune infiltration, immune cells, immunological regulatory substances, and immune pathways were also investigated. Finally, the LUAD and KICH prognostic prediction models were built using univariate and multivariate COX regression analysis. Additionally, the mRNA and protein levels of NPCs were also identified.

Machine learning algorithms for a novel cuproptosis-related gene signature of diagnostic and immune infiltration in endometriosis.

Endometriosis (EMT) is an aggressive disease of the reproductive system, also called "benign cancer". However, effective treatments for EMT are still lacking in clinical practice. Interestingly, immune infiltration is significantly involved in EMT pathogenesis. Currently, no studies have shown the involvement of cuproptosis-related genes (CRGs) in regulating immune infiltration in EMT. This study identified three CRGs such as GLS, NFE2L2, and PDHA1, associated with EMT using machine learning algorithms. These three CRGs were upregulated in the endometrium of patients with moderate/severe EMT and downregulated in patients with infertility. Single sample genomic enrichment analysis (ssGSEA) revealed that these CRGs were closely correlated with autoimmune diseases such as systemic lupus erythematosus. Furthermore, these CRGs were correlated with immune cells such as eosinophils, natural killer cells, and macrophages. Therefore, profiling patients based on these genes aid in a more accurate diagnosis of EMT progression. The mRNA and protein expression levels of GLS, NFE2L2 and PDHA1 were validated by qRT-PCR and WB studies in EMT samples. These findings provide a new idea for the pathology and treatment of endometriosis, suggesting that CRGs such as GLS, NFE2L2 and PDHA1 may play a key role in the occurrence and development of endometriosis.

A novel hypoxia-associated gene signature for prognosis prediction in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of head and neck, which seriously threatens human life and health. However, the mechanism of hypoxia-associated genes (HAGs) in HNSCC remains unelucidated. This study aims to establish a hypoxia-associated gene signature and the nomogram for predicting the prognosis of patients with HNSCC. METHODS: Previous literature reports provided a list of HAGs. The TCGA database provided genetic and clinical information on HNSCC patients. First, a hypoxia-associated gene risk model was constructed for predicting overall survival (OS) in HNSCC patients and externally validated in four GEO datasets (GSE27020, GSE41613, GSE42743, and GSE117973). Then, immune status and metabolic pathways were analyzed. A nomogram was constructed and assessed the predictive value. Finally, experimental validation of the core genes was performed by qRT-PCR. RESULTS: A HNSCC prognostic model was constructed based on 8 HAGs. This risk model was validated in four external datasets and exhibited high predictive value in various clinical subgroups. Significant differences in immune cell infiltration levels and metabolic pathways were found between high and low risk subgroups. The nomogram was highly accurate for predicting OS in HNSCC patients. CONCLUSIONS: The 8 hypoxia-associated gene signature can serve as novel independent prognostic indicators in HNSCC patients. The nomogram combining the risk score and clinical stage enhanced predictive performance in predicting OS compared to the risk model and clinical characteristics alone.

Nicotinamide Mononucleotide Improves Spermatogenic Disorders in Aluminum-Exposed Rats by Modulating the Glycolytic Pathway.

This study aimed to investigate the protective effect of nicotinamide mononucleotide (NMN) on testicular spermatogenesis in aluminum chloride (AlCl3)-exposed rats and to elucidate the potential underlying mechanism. The results indicated that AlCl3-induced testicular damage, leading to reduced sperm quality, increased apoptosis, decreased cell proliferation, and impaired Sertoli cell function in rats. Additionally, glycolytic metabolism was observed to be hindered. However, after NMN treatment, there was a noticeable improvement in testicular damage among the rats, marked by increased sperm quality, reduced apoptosis, enhanced cell proliferation, improved Sertoli cell function, and an activated glycolytic metabolism. The findings of this study suggest that NMN alleviates testicular spermatogenesis impairment induced by AlCl3 exposure through the inhibition of spermatogenic cell apoptosis, promotion of spermatogenic cell proliferation, and activation of glycolytic pathways. The study contributes an experimental foundation for potential future clinical applications of NMN in cases of AlCl3-exposed spermatogenic dysfunction.

A Four Amino Acid Metabolism-Associated Genes (AMGs) Signature for Predicting Overall Survival Outcomes and Immunotherapeutic Efficacy in Hepatocellular Carcinoma.

Metabolites are important indicators of cancer and mutations in genes involved in amino acid metabolism may influence tumorigenesis. Immunotherapy is an effective cancer treatment option; however, its relationship with amino acid metabolism has not been reported. In this study, RNA-seq data for 371 liver cancer patients were acquired from TCGA and used as the training set. Data for 231 liver cancer patients were obtained from ICGC and used as the validation set to establish a gene signature for predicting liver cancer overall survival outcomes and immunotherapeutic responses. Four reliable groups based on 132 amino acid metabolism-related DEGs were obtained by consistent clustering of 371 HCC patients and a four-gene signature for prediction of liver cancer survival outcomes was developed. Our data show that in different clinical groups, the overall survival outcomes in the high-risk group were markedly low relative to the low-risk group. Univariate and multivariate analyses revealed that the characteristics of the 4-gene signature were independent prognostic factors for liver cancer. The ROC curve revealed that the risk characteristic is an efficient predictor for 1-, 2-, and 3-year HCC survival outcomes. The GSVA and KEGG pathway analyses revealed that high-risk score tumors were associated with all aspects of the degree of malignancy in liver cancer. There were more mutant genes and greater immune infiltrations in the high-risk groups. Assessment of the three immunotherapeutic cohorts established that low-risk score patients significantly benefited from immunotherapy. Then, we established a prognostic nomogram based on the TCGA cohort. In conclusion, the 4-gene signature is a reliable diagnostic marker and predictor for immunotherapeutic efficacy.

Ferroptosis-related genes LPCAT3 and PGD are potential diagnostic biomarkers for osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is the most common chronic joint disease and how ferroptosis contributes to OA has garnered much attention recently. Bioinformatics promoted the discovery of ferroptosis-related biomarkers for OA. But since OA is a whole-joint disease, sensitive biomarkers for OA are still limited. We herein focused on subchondral bone, a joint component often-ignored by existing bioinformatic reports, to identify ferroptosis-related diagnostic biomarkers for OA. METHOD: Microarray datasets GSE51588 and GSE55457 were downloaded from Gene Expression Omnibus database. Ferroptosis-related differential expression genes (Ferr-DEGs) between OA and normal samples were identified and their functional enrichment was analyzed. Common genes for OA diagnosis were selected from Ferr-DEGs using the combination of SVM-RFE, LASSO regression, and RandomForest machine learning algorithms. Common genes' diagnostic value was verified by receiver operating characteristic (ROC) curve and their association with immune infiltration was analyzed by CIBERSORT. Finally, candidate gene's expression was verified in chondrocytes from OA patients and in an in vitro IL-1ß-induced OA model, by RT-PCR. RESULTS: Two ferroptosis-related genes, LPCAT3 and PGD, were identified as OA diagnostic biomarkers and confirmed by ROC diagnostic test. The association of LPCAT3 and PGD with the infiltration of several types of immune cells was identified. The decreased expression of LPCAT3 and PGD was both confirmed in OA chondrocytes and IL-1ß-induced OA condition. CONCLUSIONS: We identified ferroptosis-related genes LPCAT3 and PGD as potential diagnostic biomarkers for OA, which may offer insight into the role of ferroptosis in OA and provides useful information for the diagnosis and treatment of OA.

Exploring the diagnostic value, prognostic value, and biological functions of NPC gene family members in hepatocellular carcinoma based on a multi-omics analysis.

Liver cancer is a cunning malignancy with a high incidence and mortality rate among cancers worldwide. The NPC gene family members (NPCs: NPC1, NPC2, and NPC1L1) are closely linked to the development of multiple cancers, but their role in liver cancer remains unclear. As a result, we must investigate their functions in liver hepatocellular carcinoma (LIHC). NPCs were significantly differentially expressed between normal and LIHC tissues, with a high mutation frequency in LIHC. The ROC curve analysis revealed that NPC1/NPC2 had high diagnostic and prognostic values in LIHC. NPC1 expression was also found to be negatively correlated with its methylation level. The differentially expressed genes between high and low NPC1 expression groups in LIHC were mainly related to channel activity, transporter complexes, and plasma membrane adhesion molecules. Additionally, NPC1 expression was significantly associated with multiple immune cells and immunization checkpoints. It was hypothesized that a TUG1/SNHG4-miR-148a-3p-NPC1 regulatory axis is associated with hepatocarcinogenesis. Finally, the protein expression of NPC1 in LIHC tissues and paraneoplastic tissues was detected, and NPC1-knockdown HepG2 cells (NPC1KO) inhibited the proliferation, migration, and invasion. This study helped to identify new prognostic markers and potential immunotherapeutic targets for LIHC and revealed the molecular mechanisms underlying NPC1 regulation in LIHC. The NPCs play a key role in the prognosis and diagnosis of LIHC and may be an important indicator for LIHC prognosis and diagnosis; NPC1 might be a potential therapeutic target in LIHC.

A novel fluorescence probe for the recognition of Cd2+ and its application.

A facile fluorescence probe BQBH was synthesized and investigated on its spectrum property. The result showed that the BQBH had high sensitivity and selectivity for Cd2+ with lowest detection determined as 0.14 µM by fluorescence response. The 1: 1 binding ratio between BQBH and Cd2+ was determined by Job's plot, and the binding details were further confirmed by 1H NMR titration, FT-IR spectrum and HRMS analysis. The applications including on test paper, smart phone and cell image were all also investigated.

MeGATAs, functional generalists in interactions between cassava growth and development, and abiotic stresses.

The proteins with DNA-binding preference to the consensus DNA sequence (A/T) GATA (A/G) belong to a GATA transcription factor family, with a wide array of biological processes in plants. Cassava (Manihot esculenta) is an important food crop with high production of starch in storage roots. Little was however known about cassava GATA domain-containing genes (MeGATAs). Thirty-six MeGATAs, MeGATA1 to MeGATA36, were found in this study. Some MeGATAs showed a collinear relationship with orthologous genes of Arabidopsis, poplar and potato, rice, maize and sorghum. Eight MeGATA-encoded proteins (MeGATAs) analysed were all localized in the nucleus. Some MeGATAs had potentials of binding ligands and/or enzyme activity. One pair of tandem-duplicated MeGATA17-MeGATA18 and 30 pairs of whole genome-duplicated MeGATAs were found. Fourteen MeGATAs showed low or no expression in the tissues. Nine analysed MeGATAs showed expression responses to abiotic stresses and exogenous phytohormones. Three groups of MeGATA protein interactions were found. Fifty-three miRNAs which can target 18 MeGATAs were identified. Eight MeGATAs were found to target other 292 cassava genes, which were directed to radial pattern formation and phyllome development by gene ontology enrichment, and autophagy by Kyoto Encyclopaedia of Genes and Genomes enrichment. These data suggest that MeGATAs are functional generalists in interactions between cassava growth and development, abiotic stresses and starch metabolism.

Exploring a novel seven-gene marker and mitochondrial gene TMEM38A for predicting cervical cancer radiotherapy sensitivity using machine learning algorithms.

Background: Radiotherapy plays a crucial role in the management of Cervical cancer (CC), as the development of resistance by cancer cells to radiotherapeutic interventions is a significant factor contributing to treatment failure in patients. However, the specific mechanisms that contribute to this resistance remain unclear. Currently, molecular targeted therapy, including mitochondrial genes, has emerged as a new approach in treating different types of cancers, gaining significant attention as an area of research in addressing the challenge of radiotherapy resistance in cancer. Methods: The present study employed a rigorous screening methodology within the TCGA database to identify a cohort of patients diagnosed with CC who had received radiotherapy treatment. The control group consisted of individuals who demonstrated disease stability or progression after undergoing radiotherapy. In contrast, the treatment group consisted of patients who experienced complete or partial remission following radiotherapy. Following this, we identified and examined the differentially expressed genes (DEGs) in the two cohorts. Subsequently, we conducted additional analyses to refine the set of excluded DEGs by employing the least absolute shrinkage and selection operator regression and random forest techniques. Additionally, a comprehensive analysis was conducted in order to evaluate the potential correlation between the expression of core genes and the extent of immune cell infiltration in patients diagnosed with CC. The mitochondrial-associated genes were obtained from the MITOCARTA 3.0. Finally, the verification of increased expression of the mitochondrial gene TMEM38A in individuals with CC exhibiting sensitivity to radiotherapy was conducted using reverse transcription quantitative polymerase chain reaction and immunohistochemistry assays. Results: This process ultimately led to the identification of 7 crucial genes, viz., GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2, which were strongly associated with radiotherapy sensitivity. The enrichment analysis has unveiled a significant association between these 7 crucial genes and prominent signaling pathways, such as the p53 signaling pathway, KRAS signaling pathway, and PI3K/AKT/MTOR pathway. By utilizing these 7 core genes, an unsupervised clustering analysis was conducted on patients with CC, resulting in the categorization of patients into three distinct molecular subtypes. In addition, a predictive model for the sensitivity of CC radiotherapy was developed using a neural network approach, utilizing the expression levels of these 7 core genes. Moreover, the CellMiner database was utilized to predict drugs that are closely linked to these 7 core genes, which could potentially act as crucial agents in overcoming radiotherapy resistance in CC. Conclusion: To summarize, the genes GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2 were found to be closely correlated with the sensitivity of CC to radiotherapy. Notably, TMEM38A, a mitochondrial gene, exhibited the highest degree of correlation, indicating its potential as a crucial biomarker for the modulation of radiotherapy sensitivity in CC.

Identification and validation of a signature based on macrophage cell marker genes to predict recurrent miscarriage by integrated analysis of single-cell and bulk RNA-sequencing.

Recurrent miscarriage (RM) is a chronic, heterogeneous autoimmune disease that has serious social and personal consequences. No valid and reliable diagnostic markers or therapeutic targets for RM have been identified. Macrophages impact the innate immune system and can be used as diagnostic and prognostic markers for many diseases. We first collected 16 decidua and villi tissue samples from 5 normal patients and 3 RM patients for single-cell RNA sequencing data analysis and identified 1293 macrophage marker genes. We then screened a recurrent miscarriage cohort (GSE165004) for 186 macrophage-associated marker genes that were significantly differentially expressed between RM patients and the normal pregnancy endometrial tissues, and performed a functional enrichment analysis of differentially expressed genes. We then identified seven core genes (ACTR2, CD2AP, MBNL2, NCSTN, PUM1, RPN2, and TBC1D12) from the above differentially expressed gene group that are closely related to RM using the LASSO, Random Forest and SVM-RFE algorithms. We also used GSE26787 and our own collection of clinical specimens to further evaluate the diagnostic value of the target genes. A nomogram was constructed of the expression levels of these seven target genes to predict RM, and the ROC and calibration curves showed that our nomogram had a high diagnostic value for RM. These results suggest that ACTR2 and NCSTN may be potential targets for preventative RM treatments.

A cancer stem cell associated gene signature for predicting overall survival of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver cancer characterized by high mortality and morbidity rate. The lack of effective treatments and the high frequency of recurrence lead to poor prognosis of patients with HCC. Therefore, it is important to develop robust prediction tools for predicting the prognosis of HCC. Recent studies have shown that cancer stem cells (CSC) participate in HCC progression. The aim of this study was to explore the prognostic value of CSC-related genes and establish a prediction model based on data from The Cancer Genome Atlas (TCGA) database. In this study, 475 CSC-related genes were obtained from the Molecular Signature Database and 160 differentially expressed CSC-related genes in HCC patients were identified using the limma R package in the TCGA database. A total of 79 CSC-related genes were found to be associated with overall survival (OS). Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regressions, a 3-gene signature (RAB10, TCOF1, and PSMD14) was constructed. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were constructed to test the prediction performance of the signature. Performance of the signature was validated using the International Cancer Genome Consortium (ICGC) dataset. In addition, immune feature and functional enrichment analyses were carried out to explore the underlying mechanisms. Moreover, a co-expression network was constructed using the weighted gene correlation network analysis (WGCNA) method to select genes significantly associated with risk scores in HCC in the TCGA dataset. The SGO2 gene was found to be significantly associated with risk scores of HCC. In vitro experiments revealed that it can promote HCC cell proliferation. Therefore, SGO2 may be a potential therapeutic target for HCC treatment. The constructed nomogram can help clinicians make decisions about HCC treatment.

Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome.

Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.

Genetic association study of intron variants in the forkhead box protein P3 gene in Chinese patients diagnosed with cervical cancer.

The aim of this study was to investigate the effects of forkhead box protein P3 (FOXP3) intron single nucleotide variants (SNVs) in high-risk human papilloma virus (HR-HPV) infection and cervical cancer (CC) malignant lesions. We performed FOXP3 genotyping in 350 patients with CC and 350 healthy controls using the ImLDR multiple single nucleotide polymorphism genotyping technology. The heterozygous mutation TC in rs2294021 decreased the risk of HR-HPV infection and CC malignant lesions (TC vs. TT: OR = 0.71, 95% CI = 0.51-0.99); the dominant model TC+CC and allele C in rs2294021 decreased the risk of CC malignant lesions (TC+CC vs. TT: OR = 0.69, 95% CI = 0.50-0.95; C vs. T: OR = 0.78, 95% CI = 0.63-0.97). The heterozygous mutation GA, dominant model GA+AA and allele A in rs3761549 also decreased the risk of HR-HPV infection and CC malignant lesions (GA vs. GG: OR = 0.70, 95% CI = 0.51-0.96; GA+AA vs. GG: OR = 0.69, 95% CI = 0.51-0.94; A vs. G: OR = 0.75, 95% CI = 0.58-0.96). Patients with CC and HR-HPV infection carrying rs2294021 TC and rs3761549 GA had lower expression of FOXP3 protein. Haplotype analysis revealed that T-C-A decreased the risk of HR-HPV infection. Furthermore, we found a significant association between immune cells infiltration and prognosis in patients with CC. Our findings demonstrated that rs2294021 and rs3761549 variants may protect against HR-HPV and CC malignant lesions by downregulating FOXP3 and that FOXP3 was associated with immune cells infiltration, which affected the prognosis of CC.

NLK is required for Ras/ERK/SRF/ELK signaling to tune skeletal muscle development by phosphorylating SRF and antagonizing the SRF/MKL pathway.

Serum response factor (SRF) regulates differentiation and proliferation by binding to RhoA-actin-activated MKL or Ras-MAPK-activated ELK transcriptional coactivators, but the molecular mechanisms responsible for SRF regulation remain unclear. Here, we show that Nemo-like kinase (NLK) is required for the promotion of SRF/ELK signaling in human and mouse cells. NLK was found to interact with and phosphorylate SRF at serine residues 101/103, which in turn enhanced the association between SRF and ELK. The enhanced affinity of SRF/ELK antagonized the SRF/MKL pathway and inhibited mouse myoblast differentiation in vitro. In a skeletal muscle-specific Nlk conditional knockout mouse model, forming muscle myofibers underwent hypertrophic growth, resulting in an increased muscle and body mass phenotype. We propose that both phosphorylation of SRF by NLK and phosphorylation of ELKs by MAPK are required for RAS/ELK signaling, confirming the importance of this ancient pathway and identifying an important role for NLK in modulating muscle development in vivo.