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.

Not all soil carbon is created equal: Labile and stable pools under nitrogen input.

Anthropogenic activities have raised nitrogen (N) input worldwide with profound implications for soil carbon (C) cycling in ecosystems. The specific impacts of N input on soil organic matter (SOM) pools differing in microbial availability remain debatable. For the first time, we used a much-improved approach by effectively combining the 13C natural abundance in SOM with 21 years of C3-C4 vegetation conversion and long-term incubation. This allows to distinguish the impact of N input on SOM pools with various turnover times. We found that N input reduced the mineralization of all SOM pools, with labile pools having greater sensitivity to N than stable ones. The suppression in SOM mineralization was notably higher in the very labile pool (18%-52%) than the labile and stable (11%-47%) and the very stable pool (3%-21%) compared to that in the unfertilized control soil. The very labile C pool made a strong contribution (up to 60%) to total CO2 release and also contributed to 74%-96% of suppressed CO2 with N input. This suppression of SOM mineralization by N was initially attributed to the decreased microbial biomass and soil functions. Over the long-term, the shift in bacterial community toward Proteobacteria and reduction in functional genes for labile C degradation were the primary drivers. In conclusion, the higher the availability of the SOM pools, the stronger the suppression of their mineralization by N input. Labile SOM pools are highly sensitive to N availability and may hold a greater potential for C sequestration under N input at global scale.

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.

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.

Sequence and haplotypes of ankyrin 1 gene (ANK1) and their association with carcass and meat quality traits in yak.

Ankyrin 1 (ANK1) gene has been demonstrated to be a functional candidate gene for meat quality that helps to constitute and maintain the structure of the cell skeleton. In this study, three contiguous ANK1 regions from yak were analyzed using polymerase chain reaction-single-stranded conformational polymorphism (PCR-SSCP). As a result, nine single-nucleotide polymorphisms (SNPs) were identified, four of them in the coding region and three (c.179 C/A, c.250 G/C, and c.313 C/T) putatively resulting in amino acid changes (p. Ala 60 Glu, p. Asp 84 His, and p. Pro 105 Ser). Some SNPs in promoter region were located within or nearby the putative transcription factor binding sites, such as Sp1 and GATA, which might have an impact on the expression of the yak ANK1 gene. The presence of C1-D3 and C1-A3 were associated with an increased hot carcass weight (p = 0.0045) and a decreased drip loss rate (p = 0.0046). The presence of B1-B3, C1-A3 and C1-D3 had decreased Warner-Bratzler shear force (p = 0.0066, p = 0.0343 and p = 0.0004). The presence of one and two copies of B1-B3 and C1-A3 had decreased Warner-Bratzler shear force (p = 0.0005 and p = 0.0443), and C1-A3 had also decreased drip loss rate (p = 0.0164). These findings indicated that genetic variations of the ANK1 gene would be a preferable biomarker for the improvement of yak meat quality.

Cathepsin B serves as a potential prognostic biomarker and correlates with ferroptosis in rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a long-term, systemic, and progressive autoimmune disorder. It has been established that ferroptosis, a type of iron-dependent lipid peroxidation cell death, is closely associated with RA. Fibroblast-like synoviocytes (FLS) are the main drivers of RA joint destruction, and they possess a high concentration of endoplasmic reticulum structure. Therefore, targeting ferroptosis and RA-FLS may be a potential treatment for RA. METHODS: Four machine learning algorithms were utilized to detect the essential genes linked to RA, and an XGBoost model was created based on the identified genes. SHAP values were then used to visualize the factors that affect the development and progression of RA, and to analyze the importance of individual features in predicting the outcomes. Moreover, WGCNA and PPI were employed to identify the key genes related to RA, and CIBERSORT was used to analyze the correlation between the chosen genes and immune cells. Finally, the findings were validated through in vitro cell experiments, such as CCK-8 assay, lipid peroxidation assay, iron assay, GSH assay, and Western blot. RESULTS: Bioinformatics and machine learning were employed to identify cathepsin B (CTSB) as a potential biomarker for RA. CTSB is highly expressed in RA patients and has been found to have a positive correlation with macrophages M2, neutrophils, and T cell follicular helper cells, and a negative correlation with CD8 T cells, monocytes, Tregs, and CD4 memory T cells. To investigate the effect of CTSB on RA-FLS from RA patients, the CTSB inhibitor CA-074Me was used and it was observed to reduce the proliferation and migration of RA-FLS, as indicated by the accumulation of lipid ROS and ferrous ions, and induce ferroptosis in RA-FLS. CONCLUSIONS: This study identified CTSB, a gene associated with ferroptosis, as a potential biomarker for diagnosing and managing RA. Moreover, CA-074Me, a CTSB inhibitor, was observed to cause ferroptosis and reduce the migratory capacity of RA-FLS.

Soil quality and ecosystem multifunctionality after 13-year of organic and nitrogen fertilization.

Organic and mineral fertilization increase crop productivity, but their combined effects on soil quality index (SQI) and ecosystem multifunctionality (EMF, defined as the capacity of soils to simultaneously provide multiple functions and services) are not clear. We conducted a 13-year field trial in North China Plain to examine how five maize-derived organic fertilizers (straw, manure, compost, biogas residue, and biochar) at equal C input rate (3.2 t C ha-1), with or without nitrogen (N) fertilization influenced topsoil (0-15 cm) physico-chemical properties, activities of enzymes responsible for carbon (C), N, and phosphorus (P) cycling, as well as SQI and soil EMF. Organic fertilizers with or without N increased SQI by 51-187 % and EMF by 31-351 % through the enhancement of soil physical (mean weight diameter of soil aggregates) and chemical properties (C, N, and P contents) as well as C, N, and P acquisition enzyme activities, albeit the biochar effects were of minor importance. N application increased EMF compared to soil without N. Soil quality increased with EMF. Random forest analysis revealed that microbial biomass C and N, available P, permanganate oxidizable C, dissolved organic C and N, mean weight diameter of aggregates, hot water extractable C, and electrical conductivity were the main contributions to soil EMF. We conclude that application of maize-derived organic fertilizers, especially compost and straw, with optimal N fertilization is a plausible strategy to increase SQI and EMF under a wheat/maize system.

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.

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.

hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

BACKGROUND: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of ß-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis. METHODS: In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors. RESULTS: We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes. CONCLUSION: This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

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.

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.

Identification and experimental validation of ferroptosis-related gene SLC2A3 is involved in rheumatoid arthritis.

BACKGROUND: Ferroptosis, an iron-dependent manner of lipid peroxidative cell death, has recently been reported to be strongly associated with rheumatoid arthritis (RA). Targeted ferroptosis may be a potential treatment for RA. METHODS: We combined bioinformatics analysis and machine learning algorithm to screen the characteristic gene of RA. Moreover, we used gene set enrichment analysis (GSEA) to investigate the biological function of feature gene and CIBERSORT algorithm to analyze the correlation between selected hub gene and immune cells. The CellMiner database was used to predict potential drugs for RA. Finally, it was further verified by in vitro cell experiment. RESULTS: SLC2A3 was identified as an important potential biomarker based on bioinformatics methods and machine learning algorithms. SLC2A3 encodes the predominantly neuronal glucose transporter 3 (GLUT3). GSEA showed that SLC2A3 high-expression group was correlated with metabolic pathways. Immune cell infiltration analysis showed that SLC2A3 was positively correlated with activated mast cell expression. RSL3 is an activator of ferroptosis that binds to and inactivates GPX4, mediating ferroptosis regulated by GPX4. In our experiment, we treated synovial fibroblast-like cells of RA (RA-FLS) with RSL3 (Ferroptosis inducers) and found that RSL3 can downregulate SLC2A3 expression and induce ferroptosis in RA-FLS. CONCLUSIONS: Our study identifies and validates ferroptosis-related gene SLC2A3 as a potential biomarker for the diagnosis and treatment of RA. It was also found that RSL3 can induce ferroptosis in RA-FLS via lead to the downregulation of SLC2A3.

Fertilization regimes affect crop yields through changes of diazotrophic community and gene abundance in soil aggregation.

Soil aggregates are extremely vulnerable to agricultural intensification and are important drivers of soil health, microbial diversity, and biogeochemical cycling. Despite its importance, there is a dearth of studies revealing how fertilization regimes influence diazotrophic community behind soil aggregates, as well as the potential consequences for crop yields. To do this, a two-decade fertilization of wheat-maize intercropping field experiment was conducted in Loess Plateau of China semiarid area under three treatments: no fertilizer, chemical and organic fertilizer. Moreover, we categorized soil aggregates as large macroaggregates (>2 mm), medium macroaggregates (1-2 mm), small macroaggregates (0.25-1 mm), microaggregates (< 0.25 mm) and rhizosphere soils aggregates. We found that soil aggregates exerted a much more influence on the nifH gene abundance than fertilization practices. Particularly, nifH gene abundance has been promoted with increasing the size of soil aggregates fraction without blank soil in the organic fertilization while its abundance presented contrast patterns in the chemical fertilization. Bipartite association networks indicated that different soil aggregates shaped niche differentiation of diazotrophic community behind fertilization regimes. Additionally, we found that organic fertilization strengthens the robustness of diazotrophic communities as well as increases the complexity of microbial networks by harboring keystone taxa. Mantel test results suggested that specific soil factors exerted more selective power on diazotrophic community and nifH gene abundance in the chemical fertilization. Furthermore, ß-diversity and nifH gene abundance of diazotrophic communities in the soil microaggregates jointly determine the crop yields. Collectively, our findings emphasize the key role of functional community diversity in sustaining soil cycling process and crop yields under long-term fertilization, and facilitate our understanding of the mechanisms underlying diazotrophic community in response to agricultural intensification, which could pave the way to sustainable agriculture through manipulating the functional taxa.

Simulation and optimization of scrap wagon dismantling system based on Plant Simulation.

Based on the existing plant layout and process flow, a simulation analysis was conducted using the Plant Simulation platform with the utilization efficiency of each station and production capacity of the dismantling system as indicators. A problem with long-term suspension in the disassembly process was determined. Based on the two optimization directions of increasing material transportation equipment and expanding the buffer capacity, a cost-oriented optimization model is established. A genetic algorithm and model simulation were used to solve the model. An optimization scheme that satisfies the production needs and has the lowest cost is proposed. The results show that the optimized dismantling system solves the suspended work problem at the dismantling station and a significant improvement in productivity and station utilization efficiency compared with the previous system.

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.

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.

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.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks.

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.