High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level.

BACKGROUND: Despite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022. METHODS: Geographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran's I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters. RESULTS: Between 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013. CONCLUSIONS: In this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

Transmission network of Hepatitis C virus subtype 2a in Huazhou County, Shaanxi Province, China.

BACKGROUND: Huazhou County has one of the highest rates of hepatitis C virus (HCV) infection incidence and prevalence in Shaanxi Province, northwest China. Understanding the characteristics of HCV transmission patterns in this area could help guide targeted prevention strategies. This study employed phylogenetic analysis and the construction of a molecular transmission network of HCV-infected people in Huazhou County to describe the predominant strains of HCV and identify factors associated with onward transmission. METHODS: Whole blood samples were obtained from HCV RNA-positive individuals for sequencing of the non-structural protein 5B region. A maximum-likelihood (ML) phylogenetic tree was constructed to determine HCV subgenotypes, and Bayesian phylogenetic analysis was employed to estimate the evolutionary history. The transmission network was constructed using the ML phylogenetic tree and pairwise distances. Logistic regression was used to identify factors associated with clustering in the transmission network. RESULTS: ML phylogenetic analysis confirmed that the 61 sequences analyzed in the study belonged to subtype 2a. Bayesian phylogenetic analysis showed that the majority of subtype 2a sequences originated in the northwest of China and had descended approximately 8 to 20 years before sampling. Overall, 26.2% of participant sequences were grouped into phylogenetic network clusters. Multivariate logistic regression showed that individuals who had a history of blood transfusions and were living in Shi Village, Huazhou County, were more likely to form clusters within the transmission network. CONCLUSION: HCV transmission in Huazhou County was predominantly associated with subtype 2a. Having a history of blood transfusions and living in residential Shi Village, Huazhou County, were factors associated with a high risk of HCV infection transmission. Prioritizing targeted interventions for these patient groups may help to prevent further infections.

Characterization of maltose-binding protein-fused heparinases with enhanced thermostability by application of rigid and flexible linkers.

Heparinases, including heparinases I-III (HepI, HepII, and HepIII, respectively), are important tools for producing low-molecular-weight heparin, an improved anticoagulant. The poor thermostability of heparinases significantly hinders their industrial and laboratory applications. To improve the thermostability of heparinases, we applied a rigid linker (EAAAK)5 (R) and a flexible linker (GGGGS)5 (F) to fuse maltose-binding protein (MBP) and HepI, HepII, and HepIII from Pedobacter heparinus, replacing the original linker from the plasmid pMAL-c2X. Compared with their parental fusion protein, MBP-fused HepIs, HepIIs, and HepIIIs with linkers (EAAAK)5 or (GGGGS)5 all displayed enhanced thermostability (half-lives at 30°C: 242%-464%). MBP-fused HepIs and HepIIs exhibited higher specific activity (127%-324%), whereas MBP-fused HepIIIs displayed activity similar to that of their parental fusion protein. Kinetics analysis revealed that MBP-fused HepIIs showed a significantly decreased affinity toward heparin with increased Km values (397%-480%) after the linker replacement, whereas the substrate affinity did not change significantly for MBP-fused HepIs and HepIIIs. Furthermore, it preliminarily appeared that the depolymerization mechanism of these fusion proteins may not change after linker replacement. These findings suggest the superior enzymatic properties of MBP-fused heparinases with suitable linker designs and their potential for the bioproduction of low-molecular-weight heparin.

Precise Molecular Subtyping Reveals Heterogeneity of Lung Adenocarcinoma Based on DNA Methylation.

BACKGROUND: Due to the high heterogeneity of lung adenocarcinoma (LUAD), which restricts the effectiveness of therapy, precise molecular subgrouping of LUAD is of great significance. Clinical research has demonstrated the significant potential of DNA methylation as a classification indicator for human malignancies. METHODS: WGML framework (which was developed based on weighted gene correlation network analysis (WGCNA), Gene Ontology (GO), and machine learning) was developed to precisely subgroup molecular subtypes of LUAD. This framework included two parts: the WG algorithm and the machine learning part. The WG algorithm part was an original algorithm used to obtain a crucial module, which was characterized by weighted correlation network analysis, functional annotation, and mathematical algorithms. The machine learning part utilized the Boruta algorithm, random forest algorithm, and Gradient Boosting Regression Tree algorithm to select feature genes. Then, based on the results of the WGML framework, subtypes were computed by the hierarchical clustering algorithm. A series of analyses, including dimensionality reduction methods, survival analysis, clinical stage analysis, immune infiltration analysis, tumor environment analysis, immune checkpoints analysis, TIDE analysis, CYT analysis, somatic mutation analysis, and drug sensitivity analysis, were utilized to demonstrate the effectiveness of subgrouping. GEO datasets were used to externally validate the results. Meanwhile, another subgrouping method of LUAD from another study was employed to compare with the WGML framework. RESULT: By importing DNA methylation data into the WGML framework, nine genes were obtained to further subgroup LUAD. Three subtypes, the Carcinogenesis subtype, Immune-infiltration subtype, and Chemoresistance subtype, were identified. The dimensionality reduction method exhibited great distinctness between subtypes. A series of analyses were employed to exhibit the difference among the three subtypes and to demonstrate the accuracy of the definition of subtypes. Besides, the WGML framework was compared with a LUAD subgrouping method from another research, which demonstrated that WGML had better efficiency for subgrouping LUAD. CONCLUSION: This study provides a novel LUAD subgrouping framework named WGML for the accurate subgrouping of lung adenocarcinoma.

Comparative population genomics reveals convergent and divergent selection in the apricot-peach-plum-mei complex.

The economically significant genus Prunus includes fruit and nut crops that have been domesticated for shared and specific agronomic traits; however, the genomic signals of convergent and divergent selection have not been elucidated. In this study, we aimed to detect genomic signatures of convergent and divergent selection by conducting comparative population genomic analyses of the apricot-peach-plum-mei (APPM) complex, utilizing a haplotype-resolved telomere-to-telomere (T2T) genome assembly and population resequencing data. The haplotype-resolved T2T reference genome for the plum cultivar was assembled through HiFi and Hi-C reads, resulting in two haplotypes 251.25 and 251.29 Mb in size, respectively. Comparative genomics reveals a chromosomal translocation of ~1.17 Mb in the apricot genomes compared with peach, plum, and mei. Notably, the translocation involves the D locus, significantly impacting titratable acidity (TA), pH, and sugar content. Population genetic analysis detected substantial gene flow between plum and apricot, with introgression regions enriched in post-embryonic development and pollen germination processes. Comparative population genetic analyses revealed convergent selection for stress tolerance, flower development, and fruit ripening, along with divergent selection shaping specific crop, such as somatic embryogenesis in plum, pollen germination in mei, and hormone regulation in peach. Notably, selective sweeps on chromosome 7 coincide with a chromosomal collinearity from the comparative genomics, impacting key fruit-softening genes such as PG, regulated by ERF and RMA1H1. Overall, this study provides insights into the genetic diversity, evolutionary history, and domestication of the APPM complex, offering valuable implications for genetic studies and breeding programs of Prunus crops.

Phased telomere-to-telomere reference genome and pangenome reveal an expansion of resistance genes during apple domestication.

The cultivated apple (Malus domestica Borkh.) is a cross-pollinated perennial fruit tree of great economic importance. Earlier versions of apple reference genomes were unphased, fragmented, and lacked comprehensive insights into the apple's highly heterozygous genome, which impeded advances in genetic studies and breeding programs. In this study, we assembled a haplotype-resolved telomere-to-telomere (T2T) reference genome for the diploid apple cultivar Golden Delicious. Subsequently, we constructed a pangenome based on 12 assemblies from wild and cultivated species to investigate the dynamic changes of functional genes. Our results revealed the gene gain and loss events during apple domestication. Compared with cultivated species, more gene families in wild species were significantly enriched in oxidative phosphorylation, pentose metabolic process, responses to salt, and abscisic acid biosynthesis process. Our analyses also demonstrated a higher prevalence of different types of resistance gene analogs (RGAs) in cultivars than their wild relatives, partially attributed to segmental and tandem duplication events in certain RGAs classes. Structural variations, mainly deletions and insertions, have affected the presence and absence of TIR-NB-ARC-LRR, NB-ARC-LRR, and CC-NB-ARC-LRR genes. Additionally, hybridization/introgression from wild species has also contributed to the expansion of resistance genes in domesticated apples. Our haplotype-resolved T2T genome and pangenome provide important resources for genetic studies of apples, emphasizing the need to study the evolutionary mechanisms of resistance genes in apple breeding.

Ultrasound Viscosity Imaging in Breast Lesions: A Multicenter Prospective Study.

RATIONALE AND OBJECTIVES: To explore and validate the clinical value of ultrasound (US) viscosity imaging in differentiating breast lesions by combining with BI-RADS, and then comparing the diagnostic performances with BI-RADS alone. MATERIALS AND METHODS: This multicenter, prospective study enrolled participants with breast lesions from June 2021 to November 2022. A development cohort (DC) and validation cohort (VC) were established. Using histological results as reference standard, the viscosity-related parameter with the highest area under the receiver operating curve (AUC) was selected as the optimal one. Then the original BI-RADS would upgrade or not based on the value of this parameter. Finally, the results were validated in the VC and total cohorts. In the DC, VC and total cohorts, all breast lesions were divided into the large lesion, small lesion and overall groups respectively. RESULTS: A total of 639 participants (mean age, 46 years ± 14) with 639 breast lesions (372 benign and 267 malignant lesions) were finally enrolled in this study including 392 participants in the DC and 247 in the VC. In the DC, the optimal viscosity-related parameter in differentiating breast lesions was calculated to be A'-S2-Vmax, with the AUC of 0.88 (95% CI: 0.84, 0.91). Using > 9.97 Pa.s as the cutoff value, the BI-RADS was then modified. The AUC of modified BI-RADS significantly increased from 0.85 (95% CI: 0.81, 0.88) to 0.91 (95% CI: 0.87, 0.93), 0.85 (95% CI: 0.80, 0.89) to 0.90 (95% CI: 0.85, 0.93) and 0.85 (95% CI: 0.82, 0.87) to 0.90 (95% CI: 0.88, 0.92) in the DC, VC and total cohorts respectively (P < .05 for all). CONCLUSION: The quantitative viscous parameters evaluated by US viscosity imaging contribute to breast cancer diagnosis when combined with BI-RADS.

STEAP3 promotes colon cancer cell proliferation and migration via regulating histone acetylation.

Colorectal cancer (CRC) is the third most prevalent diagnosed cancer in men and second most prevalent cancer in women. H3K27ac alterations are more commonly than gene mutations in colorectal cancer. Most colorectal cancer genes have significant H3K27ac changes, which leads to an over-expression disorder in gene transcription. Over-expression of STEAP3 is involved in a variety of tumors, participating in the regulation of cancer cell proliferation and migration. The purpose of this work is to investigate the role of STEAP3 in the regulation of histone modification (H3K27ac) expression in colon cancer. Bioinformatic ChIP-seq, ChIP-qPCR and ATAC-seq were used to analyze the histone modification properties and gene accessibility of STEAP3. Western blot and qRT-PCR were used to evaluate relative protein and gene expression, respectively. CRISPR/Cas9 technology was used to knockout STEAP3 on colon cancer cells to analyze the effect of ATF3 on STEAP3. STEAP3 was over-expressed in colon cancer and associated with higher metastases and more invasive and worse stage of colon cancer. ChIP-seq and ChIP-qPCR analyses revealed significant enrichment of H3K27ac in the STEAP3 gene. In addition, knocking down STEAP3 significantly inhibits colon cancer cell proliferation and migration and down-regulates H3K27ac expression. ChIP-seq found that ATF3 is enriched in the STEAP3 gene and CRISPR/Cas9 technology used for the deletion of the ATF3 binding site suppresses the expression of STEAP3. Over-expression of STEAP3 promotes colon cancer cell proliferation and migration. Mechanical studies have indicated that H3K27ac and ATF3 are significantly enriched in the STEAP3 gene and regulate the over-expression of STEAP3.

Spatiotemporal pattern of leprosy in southwest China from 2010 to 2020: an ecological study.

BACKGROUND: Despite many efforts to control leprosy worldwide, it is still a significant public health problem in low- and middle-income regions. It has been endemic in China for thousands of years, and southwest China has the highest leprosy burden in the country. METHODS: This observational study was conducted with all newly detected leprosy cases in southwest China from 2010 to 2020. Data were extracted from the Leprosy Management Information System (LEPMIS) database in China. The Joinpoint model was used to determine the time trends in the study area. Spatial autocorrelation statistics was performed to understand spatial distribution of leprosy cases. Spatial scan statistics was applied to identify significant clusters with high rate. RESULTS: A total of 4801 newly detected leprosy cases were reported in southwest China over 11 years. The temporal trends declined stably. The new case detection rate (NCDR) dropped from 4.38/1,000,000 population in 2010 to 1.25/1,000,000 population in 2020, with an average decrease of 12.24% (95% CI: -14.0 to - 10.5; P < 0.001). Results of global spatial autocorrelation showed that leprosy cases presented clustering distribution in the study area. Most likely clusters were identified during the study period and were frequently located at Yunnan or the border areas between Yunnan and Guizhou Provinces. Secondary clusters were always located in the western counties, the border areas between Yunnan and Sichuan Provinces. CONCLUSIONS: Geographic regions characterized by clusters with high rates were considered as leprosy high-risk areas. The findings of this study could be used to design leprosy control measures and provide indications to strengthen the surveillance of high-risk areas. These areas should be prioritized in the allocation of resources.

SARS-CoV-2 reinfection with Omicron variant in Shaanxi Province, China: December 2022 to February 2023.

BACKGROUND: Prior to December 2022, there were no reports of reinfection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Shaanxi province, China. Since then, China has refined its strategy in response to coronaviruses. The purpose of this study was to determine the incidence of SARS-CoV-2 reinfections and its contributing factors, as well as to compare clinical characteristics between first and second episodes of infection in Shaanxi Province, China between December 2022 and February 2023. METHODS: We conducted a cross-sectional study using an epidemiological survey system and electronic questionnaires to investigate the incidence of SARS-CoV-2 reinfection among previously infected individuals during the epidemic wave owing to the Omicron variant that began in December 2022. A logistic regression model was used to determine those factors influencing SARS-CoV-2 reinfections. RESULTS: According to the virus variant that caused the first infection, the rate of reinfection for the Omicron variants was 1.28%, 1.96%, and 5.92% at 2-3 months, 4-5 months, and 7-9 months after the primary infection, respectively. The rate of reinfection for the Delta variants was 25.10% 11-12 months after the primary infection. Females, adults between 18 and 38 years and being a medical worker were associated with an increased risk of reinfection. Fever, cough, sore throat and fatigue were the four most common clinical symptoms during both first and second COVID-19 infections. CONCLUSIONS: In our study, the rate of SARS-CoV-2 reinfection increased over time during epidemic waves predominantly involving the Omicron variant in Shaanxi province, China. Large-scale infections are less likely in subsequent Omicron epidemic waves. Nevertheless, it is essential to continuously monitor cases of infection as well as continue surveillance for emerging SARS-CoV-2 variants.

Hypoxia-induced factor-1α promotes radioresistance of esophageal cancer cells by transcriptionally activating LINC01116 and suppressing miR-3612 under hypoxia.

Esophageal cancer (EC) is a challenging tumor to treat with radiotherapy, often exhibiting resistance to this treatment modality. To explore the factors influencing radioresistance, we focused on the role of hypoxia-induced factor-1α (HIF-1α), and its interaction with the long noncoding RNA long intergenic nonprotein coding RNA 1116 (LINC01116). We analyzed the LINC01116 expression in EC and EC cell lines/human normal esophageal epithelial cell line (Het-1A). LINC01116 was silenced/overexpressed in EC109/KYSE30 cells under hypoxia, followed by radioresistance assessment. We measured HIF-1α levels in hypoxic EC cells and further validated the binding of HIF-1α with LINC01116, analyzing their interaction in EC cells. We then performed experiments in EC109 cells by transfection them with sh-HIF-1α/oe-LINC01116 to verify the effects. Additonally, we analyzed the localization of LINC01116 and its binding with miR-3612, followed by a combined experiment performed to validate the results. Our findings indicated that LINC01116 was highly expressed in EC and further elevated in hypoxic EC cells. LINC01116 was expressed at a high level in EC, which was further elevated in EC cells under hypoxic conditions. Knockdown of LINC01116 triggered EC cell apoptosis, thus suppressing radioresistance. Further investigation revealed that HIF-1α transcriptionally activated LINC01116 expression under hypoxia, and silencing HIF-1α lowered EC cell radioresistance by downregulating LINC01116. Under hypoxic conditions, LINC01116 could function as a sponge for miR-3612 and inhibit its expression. This interaction between LINC01116 and miR-3612 played a crucial role in mediating radioresistance in EC cells. Briefly, under hypoxic conditions, HIF-1α facilitates radioresistance of EC cells by transcriptionally activating LINC01116 expression and downregulating miR-3612.

Preparation, Structural Characterization, and Enzymatic Properties of Alginate Lyase Immobilized on Magnetic Chitosan Microspheres.

Alginate lyase is an enzyme that catalyses the hydrolysis of alginate into alginate oligoalginates. To enhance enzyme stability and recovery, a facile strategy for alginate lyase immobilization was developed. Novel magnetic chitosan microspheres were synthesized and used as carriers to immobilize alginate lyase. The immobilization of alginate lyase on magnetic chitosan microspheres was successful, as proven by Fourier transform infrared spectroscopy and X-ray diffraction spectra. Enzyme immobilization exhibited the best performance at an MCM dosage of 1.5 g/L, adsorption time of 2.0 h, glutaraldehyde concentration of 0.2%, and immobilization time of 2.0 h. The optimal pH of the free alginate lyase was 7.5, and this pH value was shifted to 8.0 after immobilization. No difference was observed at the optimal temperature (45 °C) for the immobilized and free enzymes. The immobilized alginate lyase displayed better thermal stability than the free alginate lyase. The Km values of the free and immobilized enzymes were 0.05 mol/L and 0.09 mol/L, respectively. The immobilized alginate lyase retained 72% of its original activity after 10 batch reactions. This strategy was found to be a promising method for immobilizing alginate lyase.

High expression of NOLC1 as an independent prognostic factor for survival in patients with colorectal cancer.

BACKGROUND: As a phosphorylated protein, NOLC1 is mainly located in the nucleus and is highly expressed in a variety of tumors, participating in the regulation of cell proliferation and aging. This study further investigated the role of NOLC1 in colorectal cancer tumors, aiming to provide sufficient scientific evidence for the clinical treatment of colorectal cancer. METHODS: We used TCGA, GEO, TNMplot, GEPIA, and other databases to explore the expression level of NOLC1 in colorectal cancer patients, as well as the correlation between the clinical characteristics of colorectal cancer patients and their expression, and conducted the prognostic analysis. Immunohistofluorescence (IHF) staining verified the analytical results. Subsequently, KEGG and GO enrichment analysis was used to identify the potential molecular mechanism of NOLC1 promoting the occurrence and development of colorectal cancer. The influence of NOLC1 expression on the immune microenvironment of colorectal cancer patients was further investigated using the TIMER database. GDSC database analysis was used to screen out possible anti-colorectal cancer drugs against NOLC1. Finally, we demonstrated the effect of NOLC1 on the activity and migration of colorectal cancer cells by Edu Cell proliferation assay and Wound Healing assay in vitro. RESULTS: Our results suggest that NOLC1 is overexpressed in colorectal cancer, and that overexpression of NOLC1 is associated with relevant clinical features. NOLC1, as an independent risk factor affecting the prognosis of colorectal cancer patients, can lead to a poor prognosis of colorectal cancer. In addition, NOLC1 may be associated with MCM10, HELLS, NOC3L, and other genes through participating in Wnt signaling pathways and jointly regulate the occurrence and development of colorectal cancer under the influence of the tumor microenvironment and many other influencing factors. Related to NOLC1: Selumetinib, Imatinib, and targeted drugs such as Lapatinib have potential value in the clinical application of colorectal cancer. NOLC1 enhances the proliferation and migration of colorectal cancer cells. CONCLUSIONS: High expression of NOLC1 as an independent prognostic factor for survival in patients with colorectal cancer. NOLC1 enhances the proliferation and migration of colorectal cancer cells. Further studies and clinical trials are needed to confirm the role of NOLC1 in the development and progression of colorectal cancer.

Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images.

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.

Prognostic and immunological role of cancer-associated fibroblasts-derived exosomal protein in esophageal squamous cell carcinoma.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are a crucial component of the tumor microenvironment (TME) and play significant roles in tumor initiation, progression, and immune evasion. Despite this, the specific exosomal proteins derived from CAFs and their functions in esophageal squamous cell carcinoma (ESCC) remain unknown. Therefore, this study aims to investigate the impact and prognostic significance of CAFs-derived exosomal proteins in ESCC. MATERIALS AND METHODS: Exosomes obtained from CAFs and normal fibroblasts (NFs) were isolated using ultracentrifugation, and the protein expression profiles of the exosomes were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Tumor proliferation was assessed using CCK-8 and colony formation assays, while cell invasion and migration were evaluated using transwell assays. Lasso regression analysis was employed to establish a signature based on CAFs-derived exosomal proteins using the TCGA database. The immunological and prognostic roles of this signature were comprehensively investigated through survival analysis, gene set enrichment analysis (GSEA), immune analysis, immunotherapy response analysis, and drug sensitivity analysis. The GSE160269 dataset was utilized for single-cell transcriptome analysis to further elucidate the role of the signature in the TME. Additionally, cDNA microarray analysis was utilized to validate the prognostic value of the signature. RESULTS: Our findings demonstrate that exosomes derived from CAFs significantly enhance the proliferation, invasion, and migration of esophageal cancer cells. We identified 842 differentially expressed exosomal proteins through LC-MS/MS analysis, and two key proteins were utilized to establish a risk signature. Survival analysis revealed a significantly worse prognosis in the high-risk group, with multivariate analysis indicating that the risk score serves as an independent prognostic factor. Moreover, we observed a significant correlation between the risk score and immune cell infiltration, immunotherapy response, and sensitivity to chemotherapeutic treatments in the study population. Lastly, single-cell transcriptome analysis further revealed the expression patterns of TNFRSF10B and ILF3 in different cell subpopulations. CONCLUSION: In conclusion, our study has successfully established a robust prognostic signature based on CAFs-derived exosomal proteins, which can serve as a reliable biomarker for predicting prognosis and evaluating the immune microenvironment in ESCC.

Are high-risk heavy metal(loid)s contaminated vegetables detrimental to human health? A study of incorporating bioaccessibility and toxicity into accurate health risk assessment.

Heavy metal(loid)s in the environment threaten food safety and human health. Health risk assessment of vegetables based on total or bioaccessible heavy metal(loid)s was widely used but can overestimate their risks, so exploring accurate methods is urgent for food safety evaluation and management. In this study, a total of 224 frequently consumed vegetables and their corresponding grown soils were collected from Yunnan, Southwest China. The total contents and bioaccessibilities of heavy metal(loid)s in vegetables were measured, their health risks were evaluated using the non-carcinogenic and carcinogenic risk models provided by USEPA. Besides, the gastrotoxicity of high-risk vegetables was also evaluated using a human cell model. Results showed that 6.25-43.8 % of Cr, Cd, and Pb contents in Zea mays L., Coriandrum sativum L., or Allium sativum L. exceeded the maximum permissible level of China, which were not consistent with those in corresponding soils. The bioaccessibility of Cr, Cd, As, Pb, Cu, Zn, Ni, and Mn in vegetables in the gastric phase was 0.41-93.8 %. Health risks based on bioaccessibility were remarkably decreased compared with total heavy metal(loid)s, but the unacceptable carcinogenic risk (CR > 10-4) was found even considering the bioaccessibility. Interestingly, gastric digesta of high-risk vegetables did not trigger adverse effects on human gastric mucosa epithelial cells, indicating existing health risk assessment model should be adjusted by toxic data to accurately reflect its hazards. Taken together, both bioaccessibility and toxicity of heavy metal(loid)s in vegetables should be considered in accurate health risk assessment and food safety-related policy-making and management.

Refining health risk assessment of arsenic in wild edible boletus from typical high geochemical background areas: The role of As species, bioavailability, and enterotoxicity.

Arsenic (As) is easily accumulated in wild Boletus. However, the accurate health risks and adverse effects of As on humans were largely unknown. In this study, we analyzed the total concentration, bioavailability, and speciation of As in dried wild boletus from some typical high geochemical background areas using an in vitro digestion/Caco-2 model. The health risk assessment, enterotoxicity, and risk prevention strategy after consumption of As-contaminated wild Boletus were further investigated. The results showed that the average concentration of As was 3.41-95.87 mg/kg dw, being 1.29-56.3 folds of the Chinese food safety standard limit. DMA and MMA were the dominant chemical forms in raw and cooked boletus, while their total (3.76-281 mg/kg) and bioaccessible (0.69-153 mg/kg) concentrations decreased to 0.05-9.27 mg/kg and 0.01-2.38 mg/kg after cooking. The EDI value of total As was higher than the WHO/FAO limit value, while the bioaccessible or bioavailable EDI suggested no health risks. However, the intestinal extracts of raw wild boletus triggered cytotoxicity, inflammation, cell apoptosis, and DNA damage in Caco-2 cells, indicating existing health risk assessment models based on total, bioaccessible, or bioavailable As may be not accurate enough. Given that, the bioavailability, species, and cytotoxicity should be systematically considered in accurate risk assessment. In addition, cooking mitigated the enterotoxicity along with decreasing the total and bioavailable DMA and MMA in wild boletus, suggesting that cooking could be a simple and effective way to decrease the health risks of consumption of As-contaminated wild boletus.

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer.

Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tools in predicting cancer drug sensitivity. In this study, we proposed a novel method called Multi-Omics Integrated Collective Variational Autoencoders (MOICVAE), which leverages integrated omics knowledge, including genomic and transcriptomic data, to fill in missing cancer-drug associations and enhance drug sensitivity prediction. Our method employs an encoder-decoder network to learn latent feature representations from cell lines. These learned feature vectors are then fed into a collective variational autoencoder network to train an association matrix. We evaluated MOICVAE on the GDSC and CCLE benchmark datasets using 10-fold cross-validation and achieved impressive AUCs of 0.856 and 0.808, respectively, outperforming state-of-the-art methods. Furthermore, on the TCGA dataset, consisting of 25 drugs across 7 cancer types, MOICVAE exhibited an average AUC of 0.91 in predicting drug sensitivity. Additionally, significant differences were observed in survival, tumor inflammatory assessment, and tumor microenvironment between the predicted drug-sensitive and drug-resistant groups. These results are consistent with predictions made on the METABRIC dataset. Moreover, we discovered that fusing omics data based on mRNA and CNV (copy number variations) yielded superior results in drug sensitivity prediction. MOICVAE not only achieved higher accuracy in drug sensitivity prediction but also provided additional value for combining immunotherapy with chemotherapy, offering patients with more precise treatment options. The code and dataset for MOICVAE are freely available at https://github.com/wanggnoc/MOICVAE.

Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer.

A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.

Advanced Glycation End Products' Receptor DNA Methylation Associated with Immune Infiltration and Prognosis of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.

Background: Advanced glycation end products' receptor (AGER) is a multiligand receptor that interacts with a wide range of ligands. Previous studies have shown that abnormal AGER expression is closely related to immune infiltration and tumorigenesis. However, the AGER DNA methylation relationship between prognosis and infiltrating immune cells in LUAD and LUSC is still unclear. Methods: AGER expression in pan-cancer was obtained by using the UALCAN databases. Kaplan-Meier plotter showed the correlation of AGER mRNA expression levels and clinicopathological parameters. The protein expression levels for AGER were derived from Human Protein Atlas Database Analysis. The copy number, somatic mutation, and DNA methylation of AGER were presented with UCSC Xena database. TIMER platform and TISIDB website were used to show the correlation between AGER expression and tumor immune cell infiltration level. Results: The expression level of AGER was significantly reduced in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Low expression of AGER was significantly correlated with histology, stage, lymph node metastasis, and tumor protein 53 (TP53) mutation and could be used as a potential indicator of poor prognosis of LUAD and LUSC. Moreover, AGER expression was positively correlated with the infiltrating immune cells. Further analysis showed that copy number variation (CNV), mutation, and DNA methylation were involved in AGER downregulation. In addition, we also found that hypermethylated AGER was significantly correlated with tumor-infiltrating lymphocytes. Conclusion: AGER may be a candidate for the prognostic biomarker of LUAD and LUSC related to tumor immune microenvironment.