Serum 25-hydroxyvitamin D level and erectile dysfunction: a causal relationship? Findings from a two-sample Mendelian randomization study.

Background: Serum 25-hydroxyvitamin D level is associated with erectile dysfunction (ED) in observational studies. However, whether there is a causal association between them remains uncertain. Objective: Conduct a two-sample Mendelian randomization (MR) analysis to investigate the causal effect between serum 25-hydroxyvitamin D level and ED risk. Method: Genome-wide association study (GWAS) data of serum 25-hydroxyvitamin D levels comprising 6,896,093 single nucleotide polymorphisms (SNP) from 496,949 people of European ancestry were regarded as exposure for the MR analysis. Additional GWAS data involving 9,310,196 SNPs of 6,175 European ED cases and 217,630 controls were used as outcome data. The MR-Egger, inverse variance weighted (IVW) method, weighted median, simple mode, and weighted mode were employed to evaluate causal effects, among which IVW was the primary MR analysis method. The stability of the MR analysis results was confirmed by a heterogeneity test, a horizontal pleiotropy test, and the leave-one-out method. Result: There were 103 SNPs utilized as instrumental variables (p < 5 × 10-8). The results of MR analysis showed no causal effects of serum 25(OH) D concentration on ED risks (IVW; OR = 0.9516, 95% CI = 0.7994 to 1.1328, p = 0.5772). There was no heterogeneity and pleiotropy in the statistical models. Conclusion: The present MR study did not support a causal association for genetically predicted serum 25-hydroxyvitamin D concentration in the risk of ED in individuals of European descent.

The causal relationship between blood cell indices and 28-day mortality in sepsis: a retrospective study and bidirectional Mendelian randomization analysis.

BACKGROUND: Despite emerging evidence linking blood cell indices (BCIs) to sepsis mortality, the inconsistency of observational studies obscures the clarity of these associations. This study aims to clarify the causal influence of BCIs on 28-day mortality rates in sepsis patients. METHODS: Utilizing univariable and multivariable Mendelian randomization (MR) analyses, we examined the impact of BCIs on sepsis mortality by analyzing data from extensive genome-wide association studies. The inverse-variance weighted (IVW) method was our primary analytic tool, complemented by several robustness checks to mitigate pleiotropy, including weighted median, mode-based estimates, MR-Egger regression, and MR-PRESSO. Subsequently, we conducted a retrospective study to further explore the correlation between platelet indices and 28-day mortality of sepsis using real-world data. RESULTS: Our findings highlight a significant causal relationship between platelet distribution width (PDW) and 28-day mortality in sepsis, with the univariable Mendelian randomization approach yielding an odds ratio of 1.12 (95% CI, 1.06-1.26; P < 0.05). Multivariable analysis further substantiated PDW's robust association with mortality risk (OR 1.23; 95% CI, 1.03-1.48; P < 0.05). Conversely, our analysis did not uncover significant correlations between the genetic predispositions to other BCIs-including red blood cell count, erythrocyte distribution width, platelet count, mean platelet volume, white blood cell count, neutrophil count, neutrophil percentage, lymphocyte count, and lymphocyte percentage-and 28-day mortality in sepsis. Additionally, an inverse MR analysis did not establish a causal impact of 28-day mortality in sepsis on PDW (OR 1.00; 95% CI, 1.00-1.07; P = 0.29). Moreover, a similar result was observed in the retrospective study. CONCLUSIONS: The study underscores the independent causal role of PDW in predicting 28-day mortality in sepsis, suggesting its potential utility in early patient assessment, risk stratification, and tailoring of therapeutic interventions.

Corrigendum: A comprehensive analysis of HAVCR1 as a prognostic and diagnostic marker for pan-cancer.

[This corrects the article DOI: 10.3389/fgene.2022.904114.].

Near-infrared Light-Induced Polymerizations: Mechanisms and Applications.

Photopolymerizations have garnered significant attention in polymer science due to their low polymerization temperature, high production efficiency, environmental friendliness, and spatial controllability. Despite these merits, the poor penetration and severe chemical damage from ultraviolet/visible (UV/Vis) light resources pose significant barriers to their success in conventional photopolymerizations. A recent breakthrough involving the utilization of near-infrared (NIR) laser with long wavelength has been exploited for diverse applications. With the combination of a NIR photosensitizer (PS), NIR-induced photopolymerizations have been successfully developed to alleviate the challenges in conventional methods. The enhancement of penetration depth and safety of NIR-induced photopolymerizations can contribute significantly to improving the efficiency of polymerization for production of intricate structures across various scales. In this concept, the typical types of PSs and polymerization mechanisms (PMs) within the NIR-induced photopolymerization systems have been classified in detail. Additionally, the applications of various polymers achieved by NIR-induced photopolymerizations are summarized. Furthermore, research directions and future challenges of this field are also discussed comprehensively.

E2F1-mediated Up-regulation of NCAPG Promotes Hepatocellular Carcinoma Development by Inhibiting Pyroptosis.

Background and Aims: As a subunit of the condensin complex, NCAPG has an important role in maintaining chromosome condensation, but its biological function and regulatory mechanism in hepatocellular carcinoma (HCC) remains undefined. Methods: The prognostic ability of NCAPG in HCC patients was examined by univariate and multivariate Cox regression analysis. ROC curves were plotted to compare the predictive ability of NCAPG and AFP. Double luciferase reporter system, and ChIP were used to investigate transcriptional potential of E2F1 to NCAPG. Pyroptosis was observed by scanning electron microscopy. Protein expression of NCAPG, E2F1, and major proteins constituting NLRP3 inflammasome was determined by western blotting and ELISA. An in vivo tumor formation assay was conducted to verify the in vitro results. Results: Up-regulated NCAPG was identified in HCC tissues compared with adjacent tissue and high NCAPG was positively correlated with poor prognosis. Serum NCAPG mRNA level was a prognostic factor in HCC patients and also a diagnostic factor with higher predictive ability compared with AFP [AUROC 0.766 (95% CI: 0.650-0.881) vs. 0.649 (95% CI 0.506-0.793)]. HBx transfection resulted in concomitant up-regulation of E2F1 and NCAPG. E2F1 significantly increased the activity of luciferase reporter fused with NCAPG reporter, and the interaction of E2F1 and NCAPG gene was confirmed by ChIP. Silencing of E2F1 resulted in significant down-regulation of NCAPG. Knockdown of NCAPG promote pyroptosis mediated by NLRP3 inflammasome activation in multiple HCC cell lines and also suppressed tumorigenesis in vitro. Conclusions: We identified a novel role of NCAPG in the regulation of NLRP3 inflammasome-mediated pyroptosis, which was regulated by its upstream transactivator, E2F1. The role of E2F1-NCAPG-NLRP3 regulation of pyroptosis network may be a potential target in HCC treatment.

Systemic analysis of the prognostic significance and interaction network of miR-26b-3p in cholangiocarcinoma.

MicroRNAs (miRNAs) reportedly play significant roles in the progression of various cancers and hold huge potential as both diagnostic tools and therapeutic targets. Given the ongoing uncertainty surrounding the precise functions of several miRNAs in cholangiocarcinoma (CCA), this research undertakes a comprehensive analysis of CCA data sourced from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The present study identified a novel miRNA, specifically miR-26b-3p, which exhibited prognostic value for individuals with CCA. Notably, miR-26b-3p was upregulated within CCA samples, with an inverse correlation established with patient prognosis (Hazard Ratio = 8.19, p = 0.018). Through a combination of functional enrichment analysis, analysis of the LncRNA-miR-26b-3p-mRNA interaction network, and validation by qRT PCR and western blotting, this study uncovered the potential of miR-26b-3p in potentiating the malignant progression of CCA via regulation of essential genes (including PSMD14, XAB2, SLC4A4) implicated in processes such as endoplasmic reticulum (ER) stress and responses to misfolded proteins. Our findings introduce novel and valuable insights that position miR-26b-3p-associated genes as promising biomarkers for the diagnosis and treatment of CCA.

Pan-cancer analysis reveals that G6PD is a prognostic biomarker and therapeutic target for a variety of cancers.

Background: Despite accumulating evidence revealing that Glucose-6-phosphate dehydrogenase (G6PD) is highly expressed in many tumor tissues and plays a remarkable role in cancer tumorigenesis and progression, there is still a lack of G6PD pan-cancer analysis. This study was designed to analyze the expression status and prognostic significance of G6PD in pan-cancer. Methods: G6PD expression data were obtained from multiple data resources including the Genotype-Tissue Expression, the Cancer Genome Atlas, and the Tumor Immunity Estimation Resource. These data were used to assess the G6PD expression, prognostic value, and clinical characteristics. The ESTIMATE algorithms were used to analyze the association between G6PD expression and immune-infiltrating cells and the tumor microenvironment. The functional enrichment analysis was also performed across pan-cancer. In addition, the GDSC1 database containing 403 drugs was utilized to explore the relationship between drug sensitivity and G6PD expression levels. Furthermore, we also performed clinical validation and in vitro experiments to further validate the role of G6PD in hepatocellular carcinoma (HCC) cells and its correlation with prognosis. The R software was used for statistical analysis and data visualization. Results: G6PD expression was upregulated in most cancers compared to their normal counterparts. The study also revealed that G6PD expression was a prognostic indicator and high levels of G6PD expression were correlated with worse clinical prognosis including overall survival, disease-specific survival, and progression-free interval in multiple cancers. Furthermore, the G6PD level was also related to cancer immunity infiltration in most of the cancers, especially in KIRC, LGG, and LIHC. In addition to this, G6PD expression was positively related to pathological stages of KIRP, BRCA, KIRC, and LIHC. Functional analysis and protein-protein interactions network results revealed that G6PD was involved in metabolism-related activities, immune responses, proliferation, and apoptosis. Drug sensitivity analysis showed that IC50 values of most identified anti-cancer drugs were positively correlated with the G6PD expression. Notably, in vitro functional validation showed that G6PD knockdown attenuated the phenotypes of proliferation in HCC. Conclusion: G6PD may serve as a potential prognostic biomarker for cancers and may be a potential therapeutic target gene for tumor therapy.

Pulmonary interleukin 1 beta/serum amyloid A3 axis promotes lung metastasis of hepatocellular carcinoma by facilitating the pre-metastatic niche formation.

BACKGROUND: Increasing evidence suggests a vital role of the pre-metastatic niche in the formation of distant metastasis of many cancers. However, how the pre-metastatic niche is formed and promotes pulmonary metastasis of hepatocellular carcinoma (HCC) remains unknown. METHODS: Orthotopic liver tumor models and RNA-Seq were used to identify dysregulated genes in the pre-metastatic lung. Il1b knockout (Il1b-/-) mice and lentivirus-mediated gene knockdown/overexpression were utilized to demonstrate the role of interleukin 1 beta (IL-1ß)/serum amyloid A3 (SAA3) in the pre-metastatic niche formation and pulmonary metastasis. The potential molecular mechanisms were investigated by RNA-Seq, real-time quantitative PCR (qPCR), western blotting, immunohistochemistry (IHC), flow cytometry, luciferase reporter assay, double immunofluorescent staining and H&E staining. RESULTS: Accumulation of myeloid cells and upregulation of IL-1ß were observed in the pre-metastatic lung of orthotopic liver tumor models. Myeloid cells accumulation and pulmonary metastasis were suppressed in Il1b-/- mice and Il1r1-silencing mice. Mechanistically, SAA3 and matrix metallopeptidase 9 (MMP9) were identified as potential downstream targets of IL-1ß. Overexpression of SAA3 in the lungs of Il1b-/- mice restored myeloid cells accumulation and pulmonary metastasis of the orthotopic HCC xenografts. Moreover, alveolar macrophages-derived IL-1ß dramatically enhanced SAA3 expression in alveolar epithelial cells in an NF-κB dependent manner and increased MMP9 levels in an autocrine manner. Furthermore, SAA3 recruited myeloid cells to the lung without affecting the expression of MMP9 in myeloid cells. CONCLUSIONS: Our study suggests a key role of pulmonary IL-1ß and SAA3 in creating a permissive lung pre-metastatic niche by enhancing MMP9 expression and recruiting myeloid cells, respectively, thus promoting pulmonary metastasis of HCC.

Tumor necrosis factor-α-inducible protein 8-like protein 3 (TIPE3): a novel prognostic factor in colorectal cancer.

BACKGROUND: To explore the correlation of tumor necrosis factor-α-induced protein 8-like protein 3 (TIPE3) expressions in colorectal cancer (CRC) with tumor-immune infiltration and patient prognosis. METHODS: Formalin-fixed paraffin-embedded tumor samples from CRC patients (n = 110) were used in this study. Immunohistochemistry staining of TIPE3 and three prognostic immune biomarkers (CD8, CD20, and CD66b) was conducted in the tumor tissues and adjacent normal tissues. A Cox regression analysis of univariate and multivariate variables was performed to assess the correlation between TIPE3 and patient prognosis. RESULT: We found that TIPE3 was mainly expressed in the cytoplasm, with a small amount in the nucleus. The expression of TIPE3 in tumor tissues is significantly higher than in adjacent normal tissues, and it is significantly correlated with the survival rate of patients in tumor tissues (p = 0.0038) and adjacent normal tissues (p<0.0001). Patients with a high TIPE3 expression had a lower survival rate, while patients with a low TIPE3 expression had a higher survival rate. Univariate regression analysis showed that the TIPE3 expression in tumor tissues (p = 0.007), the TIPE3 expression in adjacent normal tissues (p<0.001), the number of CD8+ T cells in tumor tissues (p = 0.020), the number of CD20+ B cells in tumor tissues (p = 0.023), the number of CD20+ B cells in adjacent normal tissues (p = 0.023), the number of CD66b+ neutrophils in tumor tissues (p = 0.005), the number of CD66b+ neutrophils in adjacent normal tissues (p<0.001), lymphatic metastasis (p = 0.010), TNM stage (p = 0.013), and tumor grade (p = 0.027) were significantly correlated with overall survival (OS). These prognostic factors were then subjected to multivariate regression analysis, and the results showed that the expression of TIPE3, the number of CD8+ T cells, and the number of CD66b+ neutrophils were prognostic factors affecting the OS rate of CRC patients. CONCLUSION: We found that the TIPE3 protein is upregulated in CRC cancer tissues and is correlated with survival rate.

Classification of colorectal cancer into clinically relevant subtypes based on genes and mesenchymal cells

Background Most studies on subtype identification of colorectal cancer (CRC) were based on expressions of either genes or immune cells. However, few studies have hitherto used the combination of genes with immune and stroma cells for subtype identification. Methods Dataset GSE17536 was obtained from the Gene Expression Omnibus (GEO) database. The xCell algorithm was used to estimate the composition and density of 64 cell types, including immune and stroma cell types. Clustering analysis was then conducted on the top 3000 most variable genes from a total of 20,174 genes for CRC subtype identification. We employed the ensemble method of Similarity network fusion and 112 Consensus Clustering (SNF-CC) for cancer subtype identification. Reactome pathway analysis was conducted to identify the impact of the representative genes on prognosis. The results were validated in independent gene expression data from dataset GSE17537. Results In this study, we identified 3 clinically relevant subtypes and their representative genes, immune and stroma cells. Moreover, we confirmed the correlation of these subtypes with their clinical characteristics. The representative genes of the subtype with poor prognosis correlated with extracellular matrix structural constituent, while the subtype with good prognosis correlated with Toll-like receptor signaling pathway or chemokine signaling pathway. However, different subtypes were associated with distinct cell subtypes; the subtype with poor prognosis had a high abundance of fibroblasts and endothelial cells; the subtype with median prognosis had a higher abundance of immune cells, such as CD4 + T-cell, Th2 cells and aDC; the subtype with good prognosis had a higher abundance of NKT. Conclusion This study highlights the utility of immune and innate cells, especially during gene analysis, to provide the theoretical basis for personalized treatment in colorectal cancer patients (AU)

Classification of colorectal cancer into clinically relevant subtypes based on genes and mesenchymal cells.

BACKGROUND: Most studies on subtype identification of colorectal cancer (CRC) were based on expressions of either genes or immune cells. However, few studies have hitherto used the combination of genes with immune and stroma cells for subtype identification. METHODS: Dataset GSE17536 was obtained from the Gene Expression Omnibus (GEO) database. The xCell algorithm was used to estimate the composition and density of 64 cell types, including immune and stroma cell types. Clustering analysis was then conducted on the top 3000 most variable genes from a total of 20,174 genes for CRC subtype identification. We employed the ensemble method of Similarity network fusion and 112 Consensus Clustering (SNF-CC) for cancer subtype identification. Reactome pathway analysis was conducted to identify the impact of the representative genes on prognosis. The results were validated in independent gene expression data from dataset GSE17537. RESULTS: In this study, we identified 3 clinically relevant subtypes and their representative genes, immune and stroma cells. Moreover, we confirmed the correlation of these subtypes with their clinical characteristics. The representative genes of the subtype with poor prognosis correlated with extracellular matrix structural constituent, while the subtype with good prognosis correlated with Toll-like receptor signaling pathway or chemokine signaling pathway. However, different subtypes were associated with distinct cell subtypes; the subtype with poor prognosis had a high abundance of fibroblasts and endothelial cells; the subtype with median prognosis had a higher abundance of immune cells, such as CD4 + T-cell, Th2 cells and aDC; the subtype with good prognosis had a higher abundance of NKT. CONCLUSION: This study highlights the utility of immune and innate cells, especially during gene analysis, to provide the theoretical basis for personalized treatment in colorectal cancer patients.

PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform.

Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.

Classification and survival prediction in early-stage cirrhosis by gene expression profiling.

Liver cirrhosis has been increasingly diagnosed at an early stage owing to the non-invasive diagnostic techniques. However, it is difficult to identify patients at high risk of disease progression. Screening cirrhotic patients with poor prognosis who are most in need of surveillance is still challenging. Gene expression data GSE15654 and GSE14520 were downloaded for performing unsupervised clustering analysis. The prognostic differences between the different clusters were explored by Cox regression. Integrative analysis of gene expression signature, immune cell enrichments and clinical characterization was performed for different clusters. Two distinctive subclasses were identified in HCV-related GSE15654, and Kaplan-Meier analysis indicated that subtype 2 had lower survival rates than subtype 1 (p = 0.0399). Further analysis revealed subtype 2 had a higher density of follicular T helper cells, resting natural killer cells and M0, M2 macrophages while subtype 1 with a higher fraction of naive B cells, memory B cells, resting memory CD 4 T cells, activated natural killer cells and monocytes. 226 differentially expressed genes were identified between the two subtypes, and Reactome analysis showed the mainly enriched pathways were biological oxidations and fatty acid metabolism. Five hub genes (AKT1, RPS16, CDC42, CCND1 and PCBP2) and three significant modules were extracted from the PPI network. The results were validated in HBV-related GSE14520 cohort. We identified two subtypes of patients with different prognosis for hepatitis C-related early-stage liver cirrhosis. Bioinformatics analysis of the gene expression and immune cell profile may provide fresh insight into understanding the prognosis difference.

An early novel prognostic model for predicting 80-day survival of patients with COVID-19.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.

Identification of molecular subtypes in lung adenocarcinoma based on DNA methylation and gene expression profiling-a bioinformatic analysis.

Background: Molecular typing based on deoxyribonucleic acid (DNA) methylation and gene expression can extend understandings of the molecular mechanisms involved in lung adenocarcinoma (LUAD) and enhance current diagnostic, treatment, and prognosis prediction approaches. Methods: Gene expression and DNA methylation data sets of LUAD were obtained from The Cancer Genome Atlas (TCGA), and the differential gene and methylation expression levels were analyzed. Results: We successfully divided the LUAD samples into 2 clinically relevant subtypes with significantly different survival times and tumor stages according to the transcriptome and methylation data. We found significant differences in the survival status, age, gender, tumor stage, node stage, and clinical stage between the 2 subtypes. The hub genes identified in the subnetworks, including NCAPG, CCNB1, DLGAP5, HLA-DQA1, HLA-DPA1, HLA-DPB1, SFTP, SCGBA1A, and SFTPD, were correlated with the cell cycle and immune system. The Gene Ontology annotation of the hub genes showed that the biological processes included organelle fission mitotic nuclear division, and sister chromatid segregation. The cellular components included chromosomal region, spindle, and kinetochore. The molecular functions included tubulin-binding, microtubule-binding, and DNA replication origin binding. The Kyoto Encyclopedia of Genes and Genomes signaling pathways related to the hub genes mainly included the cell cycle, human T-cell leukemia virus (type 1) infection, inflammatory bowel disease, and the intestinal immune network for immunoglobulin A production. The clinical stage difference was also confirmed in the validation group using the GSE32863 data set. Conclusions: Our findings extend understandings of the pathogenesis of LUAD and can be used to improve current diagnosis, treatment, and prognosis prediction strategies.

The Performance of Serum Alpha-Fetoprotein for Detecting Early-Stage Hepatocellular Carcinoma Is Influenced by Antiviral Therapy and Serum Aspartate Aminotransferase: A Study in a Large Cohort of Hepatitis B Virus-Infected Patients.

Background and aims: Factors associated with abnormally elevated alpha-fetoprotein (AFP) in hepatitis B virus (HBV)-infected patients remain to be studied. We aimed to identify factors associated with elevated serum AFP in patients with non-hepatocellular carcinoma (HCC) and early-stage HCC and their influences on the performance of AFP for detecting early-stage HCC. Methods: This multicenter, retrospective study was conducted in 4401 patients with chronic HBV infection, including 3680 patients with non-HCC and 721 patients with early-stage HCC. Factors associated with elevated AFP were analyzed. Diagnostic performance of AFP for early-stage HCC were compared among groups through area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: When analyzed by multivariate logistic regression, antiviral therapy was negatively associated with elevated AFP, while hepatitis B e antigen (HBeAg) and aspartate aminotransferase (AST) > 1× upper limit of normal (ULN) were positively associated with elevated AFP both in patients with non-HCC and early-stage HCC (all p < 0.05). The AUCs of AFP for detecting early-stage HCC in patients with antiviral therapy, HBV DNA (−), alanine aminotransferase (ALT) ≤ 1× ULN, and AST ≤ 1× ULN were significantly higher compared to those in non-antiviral therapy, HBV DNA (+), ALT > 1× ULN, and AST > 1× ULN groups, respectively. When categorizing patients into AST ≤ 1× ULN and > 1× ULN, AFP achieved the highest AUCs in patients with AST ≤ 1× ULN regardless of antiviral treatment (AUCs = 0.813 and 0.806, respectively). Furthermore, there were considerable differences in the cut-off values of AFP in detecting early-stage HCC in different subgroups when applying similar sensitivity and specificity. Conclusions: Antiviral therapy and serum AST might be used to help judge and select the specific cut-off values of serum AFP for HCC surveillance in different at-risk populations.

A Comprehensive Analysis of HAVCR1 as a Prognostic and Diagnostic Marker for Pan-Cancer.

Hepatitis A virus cellular receptor (HAVCR1) is a type-1 integral membrane glycoprotein that plays a key role in immunity and renal regeneration and is abnormally expressed in various tumor types. Nonetheless, the function of HAVCR1 in pan-cancer remains unknown. In this study, we comprehensively analyzed the expression and promoter methylation level of HAVCR1 and assessed the immune cell infiltration, correlation between stromal and immune cell admixture, CD (Cluster of Differentiation) and HAVCR1 expression and prognostic value of HAVCR1 mRNA expression in Liver hepatocellular carcinoma (LIHC) and Pancreatic adenocarcinoma (PAAD). Our results showed that HAVCR1 was overexpressed while the promoter methylation of HAVCR1 was decreased in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma. HAVCR1 was associated with increased infiltration of B cells, CD8 cells, macrophages, neutrophils and Dendritic cells in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma. HAVCR1 expression was positively correlated with the immune, stromal and estimate scores of Pancreatic adenocarcinoma and the stromal and estimate scores of Liver hepatocellular carcinoma. Furthermore, HAVCR1 expression was correlated with other immune molecules such as HHLA2 (Human endogenous retrovirus-H long terminal repeat-associating protein 2), CD44 and TNFRSF4 (TNF Receptor Superfamily Member 4) in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma. During Kaplan-Meier analysis, high HAVCR1 expression in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma correlated with poor survival. A marginally significant p-value (p = 0.051) was obtained when the relationship between HAVCR1 expression in Liver hepatocellular carcinoma and prognosis was analyzed, attributed to the small sample size. Overall, we provided compelling evidence that HAVCR1 could be a prognostic and diagnostic marker for Liver hepatocellular carcinoma and Pancreatic adenocarcinoma.

Expression and Regulatory Network Analysis of Function of Small Nucleolar RNA Host Gene 4 in Hepatocellular Carcinoma.

Background and Aims: Long non-coding RNA small nucleolar RNA host genes (SNHGs) play a critical role in the occurrence and development of tumors. In this study, we aimed to investigate the role of SNHG4 in hepatocellular carcinoma (HCC) and its underlining mechanism. Methods: Datasets were acquired from The Cancer Genome Atlas (TCGA) database. lncLocator 2.0 was used to identify the distribution of SNHG4 in HCC cells. Gene expression, Kaplan-Meier survival, microRNA and transcription factor target analyses were performed with the University of Alabama Cancer (UALCAN) Database, Kaplan-Meier Plotter, LinkedOmics, WebGestalt and gene set enrichment analysis, respectively. Gene Ontology and pathway enrichment analyses and assessment of RNA binding proteins were performed by R software, circlncRNAnet and Encyclopedia of RNA Interactomes (ENCORI). In addition, CirclncRNAnet and ENCORI were used to find the correlation between SNHG4 and important proteins, while the prognostic value was assessed with the Human Protein Atlas database and Kaplan-Meier Plotter. Results: Expression of SNHG4 in HCC is higher in HCC tissue than in normal healthy liver tissues and is mainly distributed in the nucleus. SNHG4 positively correlated with poor prognosis (p<0.01 for overall survival and recurrence-free survival). Functional enrichment analysis revealed SNHG4 involvement with regulation of ribosomal RNA synthesis and the RNA processing and surveillance pathway. SNHG4 is closely associated with miR-154 and miR-206, transcription factor target E2F family and the signaling pathway for MAPK/ERK and mTOR. U2 auxiliary factor 2 (U2AF2) showed strong correlation with SNHG4, while low-expression of U2AF2 showed good prognosis. Conclusions: Based on our findings, we infer SNHG4 may play a role in the formation of HCC via regulation of tumor-related pathways.

Machine learning-based screening of the diagnostic genes and their relationship with immune-cell infiltration in patients with lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer, and has a dismal mortality rate of 80%, mainly due to diagnosis at an advanced stage. Biomarkers with high specificity and sensitivity for the early diagnosis of LUAD are sparse. This study aimed to identify markers for the early diagnosis of LUAD. Methods: The GSE32863 and GSE75037 data sets were standardized and merged to screen for differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The intersected DEGs from the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression analyses were considered the hub genes. Then the diagnostic ability and expression of hub genes was tested in GSE63459 data set, Finally, CIBERSORT was used to analyze the correlation between the immune-infiltrating cells and hub genes. Results: The following 7 DEGs were intersected by the LASSO and SVM regression analyses: Locus 401286 (LOC401286), flavin-containing monooxygenase 2 (FMO2), XLKD1, Ras homolog family member J (RHOJ), scavenger receptor Class A member 5 (SCARA5), heat shock protein beta-2 (HSPB2), and serine incorporator 2 (SERINC2). The area under the receiver operating characteristic curve (AUC) of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.99, 1.00, 0.99, 1.00, 0.99, 0.99, and 0.98, respectively in the training groups. The AUC of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.97, 0.96, 0.94, 0.88, 0.85, 0.94 and 0.89, respectively in the validation group. The immune-cell infiltrations of naive B cells, memory B cells, plasma cells, naive cluster of differentiation (CD) 4 T cells, T follicular helper cells, regulatory T cells, gamma delta T cells, monocytes, M0 macrophages, M1 macrophages, resting mast cells, activated mast cells, and neutrophils were different between the normal and tumor tissues. Notably, these immune cells were correlated with the above-mentioned 7 diagnostic genes. Conclusions: We identified 7 DEGs in LUAD tissue that can be considered diagnostic genes based on 2 machine-learning regression methods, which could be very helpful for the early diagnosis of LUAD in clinical practice.

Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis.

BACKGROUND: The host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique. METHODS: The training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA. RESULTS: Two immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA. CONCLUSIONS: The identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis.