Revealing the association between East Asian oral microbiome and colorectal cancer through Mendelian randomization and multi-omics analysis.

Background: Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development. Methods: We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics. Results: MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene. Conclusion: This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.

Genomic and immune heterogeneity of multiple synchronous lung adenocarcinoma at different developmental stages.

Multiple synchronous lung cancers (MSLCs) constitute a unique subtype of lung cancer. To explore the genomic and immune heterogeneity across different pathological stages of MSLCs, we analyse 16 MSLCs from 8 patients using single-cell RNA-seq, single-cell TCR sequencing, and bulk whole-exome sequencing. Our investigation indicates clonally independent tumours with convergent evolution driven by shared driver mutations. However, tumours from the same individual exhibit few shared mutations, indicating independent origins. During the transition from pre-invasive to invasive adenocarcinoma, we observe a shift in T cell phenotypes characterized by increased Treg cells and exhausted CD8+ T cells, accompanied by diminished cytotoxicity. Additionally, invasive adenocarcinomas exhibit greater neoantigen abundance and a more diverse TCR repertoire, indicating heightened heterogeneity. In summary, despite having a common genetic background and environmental exposure, our study emphasizes the individuality of MSLCs at different stages, highlighting their unique genomic and immune characteristics.

HADHA promotes esophageal cancer progression by activating mTOR signaling and the SP1/MDM2 axis.

Esophageal cancer (EC) is one of the most recalcitrant cancers, with a 5-year survival rate of <30%. The hydroxyacyl-CoA dehydrogenase alpha subunit (HADHA) plays an essential role in long-chain fatty acid metabolism, and dysregulation of HADHA has been demonstrated to be involved in a series of metabolic diseases and cancers. However, its role in cancers remains controversial. HADHA has seldom been investigated in EC, and little is known about how HADHA regulates the malignant progression of EC. In this study, we find that HADHA is significantly upregulated in EC tissues and is correlated with poor survival. HADHA knockdown markedly inhibits EC cell proliferation both in vitro and in vivo. The loss of HADHA also induces EC cell apoptosis, causes cell cycle arrest and inhibits cell migration. Additionally, RNA profiling reveals that mTOR signaling is significantly suppressed after HADHA knockdown. Mechanistically, HADHA interacts with SP1 and induces MDM2 expression. In conclusion, both mTOR signaling and the SP1-MDM2 axis participate in the HADHA-induced malignant behavior of EC cells.

Role of glycosylation-related gene MGAT1 in pancreatic ductal adenocarcinoma.

Background: pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a very poor prognosis and a complex tumor microenvironment, which plays a key role in tumor progression and treatment resistance. Glycosylation plays an important role in processes such as cell signaling, immune response and protein stability. Materials and methods: single-cell RNA sequencing data and spatial transcriptome data were obtained from GSE197177 and GSE224411, respectively, and RNA-seq data and survival information were obtained from UCSC Xena and TCGA. Multiple transcriptomic data were comprehensively analyzed to explore the role of glycosylation processes in tumor progression, and functional experiments were performed to assess the effects of MGAT1 overexpression on PDAC cell proliferation and migration. Results: In PDAC tumor samples, the glycosylation level of macrophages was significantly higher than that of normal samples. MGAT1 was identified as a key glycosylation-related gene, and its high expression was associated with better patient prognosis. Overexpression of MGAT1 significantly inhibited the proliferation and migration of PDAC cells and affected intercellular interactions in the tumor microenvironment. Conclusion: MGAT1 plays an important role in PDAC by regulating glycosylation levels in macrophages, influencing tumor progression and improving prognosis.MGAT1 is a potential therapeutic target for PDAC and further studies are needed to develop targeted therapeutic strategies against MGAT1 to improve clinical outcomes.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Mitophagy and clear cell renal cell carcinoma: insights from single-cell and spatial transcriptomics analysis.

Background: Clear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies. Methods: Comprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments. Results: Compared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control. Conclusion: This study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study.

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling.

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

Zhuang-Gu-Fang promotes osteoblast differentiation via myoblasts and myoblast-derived exosomal miRNAs:miR-5100, miR-126a-3p, miR-450b-5p, and miR-669a-5p.

BACKGROUND: Senile osteoporosis (SOP) is an age-related systemic metabolic bone disorder. Previous studies have proved that Zhuang-Gu-Fang (ZGF) modulates myokines, stimulates osteogenic differentiation, and mitigates osteoporosis. OBJECTIVE: To elucidate the mechanism by which ZGF promotes osteogenic differentiation via myoblast and myoblast exosomal microRNAs (miRNAs) and investigate its potential implications in senile osteoporosis. METHODS: Characterization of ZGF and ZGF serum using UHPLC-MS/MS. An alkaline phosphatase (ALP) activity assay and staining techniques were employed to corroborate the impacts of ZGF on the osteogenic differentiation of bone marrow-derived mesenchymal stem cells (BMSCs) via myoblasts. Subsequently, exosomes derived from myoblasts were isolated through ultracentrifugation. The effects of ZGF on the BMSCs' osteogenic differentiation were substantiated through ALP activity, alizarin red staining, and a quantitative real-time polymerase reaction system (qRT-PCR). Selected miRNAs were identified via high-throughput sequencing and subjected to differential expression analysis, and subsequently validated through qRT-PCR. The senescence-accelerated (SAMP6) mice were selected as the SOP models. qRT-PCR analyses were further conducted to confirm the expression levels of these selected miRNAs in the muscle and bone tissues of the SAMP6 mice, and the protein expression of osteogenesis-related transcription factors OCN and Osterix in its bone tissue was evaluated by immunofluorescence staining analysis (IF). RESULTS: ZGF may enhance the osteogenic differentiation of BMSCs through myoblasts and myoblast-derived exosomes. High-throughput sequencing, differential expression analysis, and subsequent qRT-PCR validation identified four miRNAs that stood out due to their significant differential expression: miR-5100, miR-142a-3p, miR-126a-3p, miR-450b-5p and miR-669a-5p. Moreover, the mice experiment corroborated these findings, which revealed that ZGF not only up-regulated the expression of miR-5100, miR-450b-5p and miR-126a-3p in muscle and bone tissues but also concurrently down-regulated the expression of miR-669a-5p in these tissues. IF staining analysis indicated that ZGF can significantly increase the protein expression of the osteogenic transcription factors OCN and Osterix in the bone tissue of mice with SOP. CONCLUSIONS: ZGF can promote osteogenic differentiation of osteoblasts, regulate bone metabolism, and thereby delay the process of SOP. Perhaps, its mechanism is to upregulate myoblast-derived exosomes miR-5100, miR-126a-3p, and miR-450b-5p or downregulate miR-669a-5p. This study reports for the first time that myoblast exosomes miR-669a-5p and miR-450b-5p are novel targets for the regulation of osteoblastic differentiation and the treatment of SOP.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics.

BACKGROUND: Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. METHODS: Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. RESULTS: Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. CONCLUSION: Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms.

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

Pattern of tumor regression after neoadjuvant chemoimmunotherapy for esophageal squamous cell carcinoma.

Background: Immune checkpoint inhibitors have been increasingly applied for esophageal cancer. The aims of this study were to evaluate the pattern of tumor regression after neoadjuvant chemoimmunotherapy. Methods: From January 2020 to December 2021, 138 patients with esophageal squamous cell carcinoma who had esophagectomy after neoadjuvant chemoimmunotherapy were reviewed. Surgical and pathological results were analyzed, and tumor regression pattern was evaluated. Results: Of the 138 patients, 65 (47.1%) patients had chemotherapy combined with camrelizumab, 48 (34.8%) with pembrolizumab, 13 (9.4%) with tislelizumab, and 12 (8.7%) with sintilimab. Sixty-four patients (46.4%) underwent McKewon procedure, and 74 (53.6%) Ivor-Lewis procedure, respectively. There were 131/138 patients (94.9%) who had R0 resections, and the median number of resected lymph nodes was 28. Pneumonia was the most common complication after surgery (14.5%). Pathological complete regression occurred in 28 patients (20.3%). Regarding to residual tumor, there were 50 patients (36.2%) with residual tumor in the mucosa, 81 (58.7%) in the submucosa, 85 (61.6%) in the muscularis propria, 47 (34.1%) in the adventitia and 71 (51.4%) in the lymph nodes. There were 88 patients with no residual tumor in the mucosa, of whom 60 (68.2%) had residual tumors in other layers or in the lymph nodes. Conclusions: In this retrospective study, esophagectomy after neoadjuvant chemoimmunotherapy is safe with acceptable surgical risk. Preferential clearing of tumor cells in mucosa layer is common after immunotherapy, while the rate of complete pathological response is relatively low, indicating surgery is still necessary.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay.

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.

Background: Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications. Methods: In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method. Results: Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age. Conclusion: Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma.

Background: Clear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC. Methods: Employing both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments. Results: By analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs. Conclusions: In this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

Systematic analysis of the pectin methylesterase gene family in Nicotiana tabacum and reveal their multiple roles in plant development and abiotic stresses.

The pectin methylesterases (PMEs) play multiple roles in regulating plant development and responses to various stresses. In our study, a total of 121 PME genes were identified in the tobacco genome, which were clustered into two groups based on phylogenetic analysis together with Arabidopsis members. The investigations of gene structure and conserved motif indicated that exon/intron and motif organizations were relatively conserved in each group. Additionally, several stress-related elements were identified in the promoter region of these genes. The survey of duplication events revealed that segmental duplications were critical to the expansion of the PME gene family in tobacco. The expression profiles analysis revealed that these genes were expressed in various tissues and could be induced by diverse abiotic stresses. Notably, NtPME029 and NtPME043, were identified as homologues with AtPME3 and AtPME31, respectively. Furthermore, NtPME029 was highly expressed in roots and the over-expression of the NtPME029 gene could promote the development of roots. While NtPME043 could be induced by salt and ABA treatments, and the over-expression of the NtPME043 gene could significantly enhance the salt-stress tolerance in tobacco. Overall, these findings may shed light on the biological and functional characterization of NtPME genes in tobacco.

Enhancing the thermostability of transglutaminase from Streptomyces mobaraensis based on the rational design of a disulfide bond.

Transglutaminase (TGase), a transferase, is widely adopted in the food industry and other biological fields due to its unique characteristics of modifying proteins by intra- or intermolecular cross-linking. However, obtaining a mutant TGase that is highly thermostable and active would significantly aid in food processing. Therefore, this study sought to improve the thermostability of TGase by introducing an artificial disulfide bridge through a structure-based rational enzyme engineering approach. After the rational screening, six disulfide mutants (E139C/G143C, R146C/E149C, A182C/N195C, L200C/R208C, T223C/F226C, and E139C/G143C+L200C/R208C) of the transglutaminase gene from Streptomyces mobaraensis (Sm-TGase) were selected and constructed by rationally designed mutations in cysteine. Of them, a mutant (E139C/G143C) with enhanced thermostability was selected and characterized for further analysis. The results indicated that the mutant E139C/G143C had a similar specific activity, optimal temperature, and pH but a lower Km and higher Vmax than the wild-type. Its half-life (t1/2) at 55 °C was 10.7 min, which was 1.69-fold higher than the wild-type, while its melting temperature (Tm) was 3.52 °C higher than the wild-type. These results proved that the introduction of disulfide bonds into TGase by rational design could be an effective approach to improve the thermostability of TGase and other food enzymes for food processing.

The Prognostic Value of Preoperative Serum Tumor Markers in Non-Small Cell Lung Cancer Varies With Radiological Features and Histological Types.

OBJECTIVES: To assess the association between common-used serum tumor markers and recurrence of lung adenocarcinoma and squamous cell carcinoma separately and determine the prognostic value of serum tumor markers in lung adenocarcinoma featured as ground glass opacities. METHODS: A total of 2,654 non-small cell lung cancer patients undergoing surgical resection between January 2008 and September 2014 were analyzed. The serum levels of carcinoma embryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), neuron-specific enolase (NSE), carbohydrate antigen 125 (CA125), carbohydrate antigen 153 (CA153) and carbohydrate antigen 199 (CA199) were tested preoperatively. Survival analyses were performed with COX proportional hazard regression. RESULTS: Among patients with lung adenocarcinoma, elevated preoperative serum CEA(HR=1.246, 95%CI:1.043-1.488, P=0.015), CYFRA21-1(HR=1.209, 95%CI:1.015-1.441, P=0.034) and CA125(HR=1.361, 95%CI:1.053-1.757, P=0.018) were significantly associated with poorer recurrence free survival (RFS). Elevated preoperative serum CA199 predicted worse RFS in patients diagnosed with lung squamous cell carcinoma (HR=1.833, 95%CI: 1.216-2.762, P=0.004). Preoperative serum CYFRA21-1(HR=1.256, 95%CI:1.044-1.512, P=0.016) and CA125(HR=1.373, 95%CI: 1.050-1.795, P=0.020) were independent prognostic factors for patients with adenocarcinoma presenting as solid nodules while serum CEA (HR=2.160,95%CI:1.311-3.558, P=0.003) and CA125(HR=2.475,95%CI:1.163-5.266, P=0.019) were independent prognostic factors for patients with adenocarcinoma featured as ground glass opacities. CONCLUSIONS: The prognostic significances of preoperative serum tumor markers in non-small cell lung cancer were associated with radiological features and histological types.

Characterization of gene expression profiles of esophageal cancer patients with different nonsynonymous tumor mutation burden.

BACKGROUND: Nonsynonymous tumor mutation burden (NSTMB) could affect the prognosis of esophageal cancer (EC) patients, but differentially expressed genes between EC patients with different NSTMB have not been explored. Our study aimed to compare differentially expressed genes between EC patients with different NSTMB (high vs. low). METHODS: RNA-seq data for EC patients were downloaded from The Cancer Genome Atlas (TCGA). The edgeR package was used to identify differentially expressed genes between patients with different NSTMB. Cell type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) software was employed to underscore immune cell differences between patients with different NSTMB. RESULTS: In total, we discovered 2215 differentially expressed genes between patients with different NSTMB, among which 842 genes were upregulated and 1373 downregulated in patients with high NSTMB. The differentially expressed genes were enriched in pathways such as heme binding and structural molecule activity. We built a logistic model that may be used to predict patients' NSTMB. We found that tumors with high NSTMB had a significantly higher percentage of resting natural killer (NK) cells than those with low NSTMB (P = 0.028). The percentages of regulatory T (Treg) and CD8+ T cells were also higher in those with high NSTMB, although it was not statistically significant (P = 0.064 for Treg cells and P = 0.12 for CD8+ T cells). CONCLUSIONS: NSTMB may cause changes in gene expression and immune cell infiltration in EC patients, and affect the overall survival of EC patients. KEY POINTS: Significant findings of the study This study found differentially expressed genes and differences in infiltration of immune cells between esophageal cancer (EC) with different NSTMB. What this study adds This study highlights differences between EC patients with different NSTMB.