Spatial distribution and enrichment characteristics of selenium in paddy soil and rice around the Dongting Lake.

Selenium (Se) deficiency is a major global health issue. Given that the Dongting Lake region is a significant agricultural production area in China, its soil and geographical properties have a marked influence on Se accumulation in rice. Investigating these factors and their importance can provide technical guidance for the production of Se-rich rice locally and in other similar regions worldwide. Such studies can foster Se-enriched agricultural practices on a global scale, contributing to improved human health and environmental quality. Therefore, in this study, we investigated 15,403 paddy soil samples and their corresponding rice grains from the Dongting Lake area, by analyzing their Se content, spatial distribution, and bioaccumulation factor (BCF). The effects of parent materials, soil characteristics (physicochemical), and geographical factors on Se content in soil, rice grains, and BCF were also assessed. We found that the average Se content in the paddy soil of the Dongting Lake area was 0.43 mg/kg, which was 1.48 folds higher than the background Se content (0.29 mg/kg) in Chinese soil. The average Se content in rice grains was 0.059 mg/kg, surpassing the Chinese standard for Se-rich rice (0.04 mg/kg). Se distribution in the paddy soil and rice were the highest in the western and central regions and lowest in the eastern region. Se-enriched rice and Se-enriched rice fields are widely distributed in Dongting Lake area. Seven parent materials significantly influenced soil Se and BCF. Correlation analysis revealed positive correlations between soil Se and soil organic matter (SOM), zinc, altitude, and mean annual precipitation. BCF was positively correlated with pH and mean annual temperature. The Random Forest model highlighted that SOM played a pivotal role in soil Se enrichment, being the most influential factor for both soil and rice enrichment (RR type), whereas pH exerted the most significant influence on soil enrichment without rice enrichment (RN type).

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022.

BACKGROUND: Gastrointestinal neoplasm (GN) significantly impact the global cancer burden and mortality, necessitating early detection and treatment. Understanding the evolution and current state of research in this field is vital. AIM: To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research, focusing on key contributors, institutions, and thematic evolution. METHODS: This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the "bibliometrix" R package, VOSviewer, and CiteSpace. The analysis focused on the distribution of publications, contributions by institutions and countries, and trends in keywords. The methods included data synthesis, network analysis, and visualization of international collaboration networks. RESULTS: This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration. It highlights the United States' critical role in advancing this field, with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute. The last five years, substantial advancements have been made, representing nearly 45% of the examined literature. Publication rates have dramatically increased, from 20 articles in 2002 to 112 in 2022, reflecting intensified research efforts. This study underscores a growing trend toward interdisciplinary and international collaboration, with the Journal of Clinical Oncology standing out as a key publication outlet. This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks. CONCLUSION: This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis. This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy, ultimately enhancing worldwide patient care.

Ilizarov technique for treating elbow stiffness caused by myositis ossificans: A case report.

BACKGROUND: Myositis ossificans (MO) is a rare disease involving the formation of bone outside the musculoskeletal system. While surgical intervention is the main treatment approach, preventing recurrence and standardized rehabilitation are also crucial. Here, we present a surgical strategy to prevent the recurrence of MO. CASE SUMMARY: A 28-year-old female patient was admitted for the first time for a comminuted fracture of the left olecranon. However, incorrect postoperative rehabilitation resulted in the development of elbow joint stiffness with ectopic ossification, causing a loss of normal range of motion. The patient was diagnosed with MO based on physical examination, X-ray findings, and clinical presentation. We devised a surgical strategy to remove MO, followed by fixation with an Ilizarov frame, and implemented a scientifically reasonable rehabilitation plan. The surgery lasted for 3 h with an estimated blood loss of 45 mL. A drainage tube was placed after surgery, and fluid was aspirated through ultrasound-guided puncture. The patient experienced a significant reduction in joint stiffness after surgery. In the final follow-up at 9 mouths, there was evident improvement in the range of motion of the elbow joint, and no other symptoms were reported. CONCLUSION: The Ilizarov frame is an advantageous surgical technique for facilitating rehabilitation after MO removal. It offers benefits such as passive recovery, individualized treatment, and prompt recovery.

Deciphering hepatoma cell resistance to tyrosine kinase inhibitors: insights from a Liver-on-a-Chip model unveiling tumor endothelial cell mechanisms.

Liver cancer represents a significant global burden in terms of cancer-related mortality, with resistance to anti-angiogenic drugs such as Sorafenib and Lenvatinib presenting a formidable challenge. Tumor angiogenesis, characterized by the formation of new blood vessels within tumors, plays a pivotal role in cancer progression and metastasis. Tumor endothelial cells, specialized endothelial cells lining tumor blood vessels, exhibit unique phenotypic and functional traits that drive aberrant vessel formation and contribute to therapy resistance. CD105, a cell-surface glycoprotein that is highly expressed on endothelial cells during angiogenesis, including tumor endothelial cells, regulates endothelial cell proliferation, migration, and vessel formation by modulating transforming growth factor-beta (TGF-ß) signaling pathways. Elevated CD105 expression on tumor endothelial cells correlates with increased angiogenic activity and poor prognosis in cancer patients. Targeting CD105 with antibodies presents a promising strategy to inhibit tumor angiogenesis and disrupt tumor vasculature, offering potential therapeutic benefits by interfering with the tumor microenvironment and inhibiting its progression. This study investigates tumor angiogenesis through a three-dimensional (3D) microfluidic co-culture system incorporating endothelial cells and hepatocellular carcinoma (HCC) cells. The primary focus is on the role of CD105 expression within the liver tumor microenvironment and its contribution to increased chemoresistance. Additionally, this research examines the influence of CD105 expression on the efficacy of tyrosine kinase inhibitors (TKIs) and its pivotal function in facilitating angiogenesis in liver tumors. The proposed microfluidic chip model investigates liver cancer cell interactions within a microfluidic chip model designed to simulate aspects of liver tumor angiogenesis.

An SNP Marker Predicts Colorectal Cancer Outcomes with 5-Fluorouracil-Based Adjuvant Chemotherapy Post-Resection.

Colorectal cancer (CRC) is a global health concern, necessitating adjuvant chemotherapy post-curative surgery to mitigate recurrence and enhance survival, particularly in intermediate-stage patients. However, existing therapeutic disparities highlight the need for biomarker-guided adjuvant chemotherapy to achieve better CRC inhibition. This study explores the molecular mechanisms underlying the inhibition of CRC through a genome-wide association study (GWAS) focused on 5-fluorouracil (5-FU)-based adjuvant therapy in intermediate-stage CRC patients, a domain previously unexplored. We retrospectively included 226 intermediate-stage CRC patients undergoing surgical resection followed by 5-FU-based adjuvant chemotherapy. The exploration cohort comprised 31 patients, and the validation cohort included 195 individuals. Genotyping was carried out using either Axiom Genome-Wide TWB 2.0 Array Plate-based or polymerase chain reaction-based methods on genomic DNA derived from collected tissue samples. Statistical analyses involved descriptive statistics, Kaplan-Meier analyses, and Cox proportional hazard analyses. From the GWAS, potential genetic predictors, GALNT14-rs62139523 and DNMBP-rs10786578 genotypes, of 5-FU-based adjuvant therapy following surgery in intermediate-stage CRC patients were identified. Validation in a larger cohort of 195 patients emphasized the predictive significance of GALNT14-rs62139523 genotypes, especially the "A/G" genotype, for improved overall and progression-free survival. This predictive association remained robust across various subgroups, with exceptions for specific demographic and clinical parameters such as age < 58 years old, CEA ≤ 2.5 ng/mL, tumor diameter > 44.0 mm, and tumor-free margin ≥ 50 mm. This study identifies that the GALNT14-rs62139523 "A/G" genotype modulates therapeutic outcomes, establishing it as a promising biomarker for predicting favorable responses to 5-FU-based adjuvant chemotherapy in intermediate-stage CRC patients, although further investigations are needed to detail these mechanisms.

UBE2C as an Immune-Related Biomarker for Breast Cancer: A Study Based on Multiple Databases.

Objective To screen the target gene UBE2C and explore its prognostic value and immune correlation in breast cancer (BRCA) using multiple databases. Methods The microarray expression datasets of BRCA were downloaded from the Gene Expresssion Omnibus database (GEO) and analyzed to obtain differentially expressed genes (DEGs). Hub genes were obtained by constructing and visualizing the protein-protein interaction network of DEGs. Then the key gene UBE2C was determined using R language, STRING, and Cytoscape, and the differential expression of UBE2C was verified using the external datasets, The Cancer Genome Atlas (TCGA) , and quantitative real-time PCR (qRT-PCR). The prognostic value and immunological correlation of UBE2C in BRCA were explored using R language, TIMER, and Gene Set Enrichment Analysis (GSEA).Results The expression of UBE2C was differentially upregulated in BRCA, as verified by TCGA and qRT-PCR. Prognostic analysis revealed that UBE2C served as an independent prognostic factor. High expression of UBE2C was associated with decreased immune infiltration levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, and myeloid dendritic cells in BRCA tissue. The expression of UBE2C in BRCA showed a significant correlation with PDCD1, CD274, and CTLA4 expressions. There was a positive correlation between the expression of UBE2C and the tumor mutational burden and microsatellite instability. GSEA demonstrated that UBE2C expression significantly enriched 786 immune-related gene sets.Conclusions UBE2C expression in BRCA tissues can predict the survivals and prognosis of BRCA patients. Also, it is closely related to the BRCA immune microenvironment and can predict the effecacy of immunotherapy in BRCA patients. Therefore, UBE2C may be an potential immune-related prognostic biomarker for BRCA.

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Two new iridoid glycosides from the whole plant of Rehmania piasezkii.

Two new iridoid glycosides, piasezkiiosides A (1) and B (2), were isolated from aqueous extract of the whole plant of Rehmannia piasezkii. Their structures were established from the spectroscopic data, chemical transformation, and X-ray diffraction analysis. Compound 1 exhibited weak hepatoprotective activity against APAP-induced HepG2 cell damage.

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling.

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10-19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Nutritional support therapy for liver transplantation in an adult-onset type II citrullinemia patient: a case report.

Liver transplantation is an effective measure to treat adult-onset type II citrullinemia (CTLN2). Active and effective perioperative nutrition support is a very important treatment for the prognosis of such patients. In this paper, we analyzed the process, results, and outcome of nutritional support therapy in a case of CTLN2, and concluded that the perioperative nutritional support program for CTLN2 patients should be followed prior to surgery:1.because of the prevalence of severe malnutrition in CTLN2 patients, Enteral nutrition (EN) combined with Parenteral nutrition (PN) should be the first choice for nutritional support; 2. daily energy intake should be 35 ~ 40 kcal/kg; 3. the nutritional formula should be composed of low-carbohydrates and high medium-chain triglyceride (MCT). Postoperative: initiating EN as soon as possible is recommended to restore intestinal function and adjuvant PN might be taken into consideration in the early stage. The purpose of this case was to provide experience for the development and adjustment of the perioperative nutritional support regimen for CTLN2 patients.

Effect of beta-cypermethrin on the reproductive capacity of female mice in advanced age.

The aim of the present study was to investigate whether exposure to pesticides beta-cypermethrin (ß-CYP) harms the reproductive capacity of advanced-age female mice. The results evidenced that peri-implantation ß-CYP exposure significantly reduced the number of fetuses per advanced-age female in the first litter, and the number and weight of implantation sites. The levels of decidualization markers were significantly reduced in ß-CYP-administered advanced-age mice. Lower expression of Pcna, Cdk6, Foxo1, Ki67, and p62 protein and mRNA was found in the decidua of ß-CYP-treated advanced-age mice. The levels of Bax, cleaved caspase-3, Lc3a/b, Atg, mTOR, and p-mTOR protein, and the ratio of p-mTOR/mTOR protein expression were clearly downregulated by peri-implantation ß-CYP exposure. These results indicated that peri-implantation ß-CYP exposure may elevate the decline in reproductive capacity of early pregnant mice in advanced age.

Proteomic and metabolomic features in patients with HCC responding to lenvatinib and anti-PD1 therapy.

Combination therapy (lenvatinib/programmed death-1 inhibitor) is effective for treating unresectable hepatocellular carcinoma (uHCC). We reveal that responders have better overall and progression-free survival, as well as high tumor mutation burden and special somatic variants. We analyze the proteome and metabolome of 82 plasma samples from patients with hepatocellular carcinoma (HCC; n = 51) and normal controls (n = 15), revealing that individual differences outweigh treatment differences. Responders exhibit enhanced activity in the alternative/lectin complement pathway and higher levels of lysophosphatidylcholines (LysoPCs), predicting a favorable prognosis. Non-responders are enriched for immunoglobulins, predicting worse outcomes. Compared to normal controls, HCC plasma proteins show acute inflammatory response and platelet activation, while LysoPCs decrease. Combination therapy increases LysoPCs/phosphocholines in responders. Logistic regression/random forest models using metabolomic features achieve good performance in the prediction of responders. Proteomic analysis of cancer tissues unveils molecular features that are associated with side effects in responders receiving combination therapy. In conclusion, our analysis identifies plasma features associated with uHCC responders to combination therapy.

A 10-year review on older patients undergoing colorectal cancer surgery: surgeon-led geriatric service and good quality surgery are drivers of good outcomes.

BACKGROUND: Older age and frailty are associated with worse postoperative outcomes and prolonged length of stay (LOS). In this study, we aimed to analyze the long-term outcomes after the implementation of our geriatric surgical service (GSS). METHODS: This was a single-center retrospective study from July 2010 to December 2021 on patients aged ≥75 years or patients aged ≥65 years with frailty. Our GSS includes multidisciplinary assessment and optimization by specialized nurses, physiotherapists, anesthetists, dietitians, and geriatricians. Cumulative sum (CUSUM) analysis was used to assess the performance of our GSS. Our primary outcome was defined as the presence of 30-day mortality, prolonged LOS ≥ 14 days, and/or >10% decrease in the modified Barthel Index at 6 weeks, which depicts the failure of GSS. A downsloping CUSUM curve implies consecutive cases of success. RESULTS: There were 233 patients with a mean age of 79.0 ± 4.9 years; of these, 73 patients (31.3%) were frail. The overall 30-day mortality (1.7%), Clavien-Dindo ≥ grade IIIA complications (12.0%), and LOS (median, 7.0 days) were low. The CUSUM analysis showed 3 phases with overall sustained improvement in outcomes. Transient inconsistency in the second phase (during midimplementation of GSS) may be due to the early adoption of laparoscopic surgery (44.6% vs 24.1%; adjusted P =.031) and expansion of service to include patients with higher perioperative risks (weighted Charlson Comorbidity Index score ≥4: 64.9% vs 38.0%; adjusted P =.002) in the second period compared with the first period. The outcomes subsequently improved in the third phase after overcoming the learning curve. CONCLUSION: Our GSS showed sustained performance over the past decade. Good quality surgery and surgeon-led geriatric service are paramount for good postoperative outcomes.

Seven new pentasaccharides from the roots of Rehmannia glutinosa.

Seven new pentasaccharides (1-7), rehmaglupentasaccharides A-G, were isolated from the air-dried roots of Rehmannia glutinosa. Their structures were established from the spectroscopic data obtained and by chemical evidence. The known verbascose (8) and stachyose (9) were also obtained in the current investigation, and the structure of stachyose was unequivocally defined using X-ray diffraction data. Compounds 1-9 were tested for their cytotoxicity against five human tumor cell lines, influence on dopamine receptor activation, and proliferation effects against Lactobacillus reuteri.

DriverDBv4: a multi-omics integration database for cancer driver gene research.

Advancements in high-throughput technology offer researchers an extensive range of multi-omics data that provide deep insights into the complex landscape of cancer biology. However, traditional statistical models and databases are inadequate to interpret these high-dimensional data within a multi-omics framework. To address this limitation, we introduce DriverDBv4, an updated iteration of the DriverDB cancer driver gene database (http://driverdb.bioinfomics.org/). This updated version offers several significant enhancements: (i) an increase in the number of cohorts from 33 to 70, encompassing approximately 24 000 samples; (ii) inclusion of proteomics data, augmenting the existing types of omics data and thus expanding the analytical scope; (iii) implementation of multiple multi-omics algorithms for identification of cancer drivers; (iv) new visualization features designed to succinctly summarize high-context data and redesigned existing sections to accommodate the increased volume of datasets and (v) two new functions in Customized Analysis, specifically designed for multi-omics driver identification and subgroup expression analysis. DriverDBv4 facilitates comprehensive interpretation of multi-omics data across diverse cancer types, thereby enriching the understanding of cancer heterogeneity and aiding in the development of personalized clinical approaches. The database is designed to foster a more nuanced understanding of the multi-faceted nature of cancer.

Four new iridoid glycosides from the roots of Rehmannia glutinosa.

Four new iridoid glycosides (1-4), rehmaglutosides L-O, were isolated from the air-dried roots of Rehmannia glutinosa. Their structures were established from the spectroscopic data obtained and by chemical evidence. The known mellittoside (5) and ajugol (6) were also obtained in the current investigation, and the structure of mellittoside was unequivocally defined using X-ray diffraction data. Compounds 1-6 were tested for their cytotoxicity against five human tumor cell lines and proliferation effects on Lactobacillus Reuteri.

Identifying the source and fate of boron in geothermal water: Evidence from B/Na and B isotopes.

High level dissolved B, which poses risks to human health, has been widely observed in geothermal water. In the Guide Basin, NW China, a series of geothermal water samples along a fault show a wide range of B contents ranging from 3.14 to 8.33 mg/L, which are higher than the WHO Guideline value equaling 2.4 mg/L in drinking water. To identify the sources and fate of B, we conduct a comprehensive analysis of hydrochemistry and stable isotopes (D, 18O and 11B) of three thermal fields representing three stages of hydrogeochemical evolution (stages I, II and III). From stage I to III, there are trends of increasing mineral dissolution, which is supported by increasing mean reservoir temperature and concentrations of conservative elements (Cl, Na, K, Li and Si). Geothermal water in stage I with meteoric origin and the lowest reservoir temperature has the highest B/Na resulting from silicate dissolution and falls on the mixing line between granitoids and cold water on the plot of δ11B versus 1/B, showing the control of silicate dissolution. However, geothermal water in stage III has lower Ca, B Sr and B/Na than that in stage II. Because of the occurrence of other processes, geothermal water in stages II and III deviates from the LMWL. Compared with geothermal water in stage I, the increased Sr/Ca and decreased B/Ca show that B are removed by both coprecipitation and vapor separation. With the aid of B isotopes, we find vapor separation dominates in stage II, whereas carbonate precipitation dominates in stage III. Overall, a combined use of three isotopes (H, O and B) and three element ratios (B/Na, B/Ca and Sr/Ca) leads to a complete understanding of B cycle and hydrogeochemical evolution in hydrothermal systems.

Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer.

BACKGROUND: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients' clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. METHODS: A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image's region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier's effectiveness, the ROC curve area and confusion matrix were employed. RESULTS: Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. CONCLUSIONS: The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS.

Development and External Validation of a Nomogram Including Body Composition Parameters for Predicting Early Recurrence of Hepatocellular Carcinoma After Hepatectomy.

RATIONALE AND OBJECTIVES: Body composition, including adipose and muscle tissues, evaluated by computer tomography is correlated with the prognosis of hepatocellular carcinoma (HCC). However, its relationship with early recurrence (ER) remains unclear. This study aimed at establishing and validating a nomogram based on body composition and clinicopathological indices to predict ER of HCC. MATERIALS AND METHODS: One hundred ninety-five patients from institution A formed the training cohort and internal validation cohort, and 50 patients from institution B formed the external validation cohort. Independent predictors of ER were identified using LASSO and Cox regression analyses. The performance of nomogram was evaluated using the calibration curve, concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). RESULTS: After data screening, the nomogram was constructed using eight independent predictors of ER, including the tumor size, alpha fetoprotein, body mass index, Edmondson Steiner grade, visceral adipose tissue radiodensity, intermuscular adipose tissue index, intramuscular adipose tissue content, and skeletal muscle area. The calibration curve exhibited excellent concordances, with C-indices of 0.808 (95%CI: 0.771-0.860), 0.802 (95%CI: 0.747-0.942), and 0.804 (95%CI: 0.701-0.861) in training, internal validation, and external validation cohorts, respectively. In addition, compared to conventional staging systems and pure clinical model, the nomogram exhibited a higher AUC and wider range of threshold probabilities in DCA, which indicated better discriminative ability and greater clinical benefit. Finally, patients with nomogram scores of <183.07, 183.07-243.09, and >243.09 were considered to have low, moderate, and high risks of ER, respectively. CONCLUSION: The nomogram exhibits excellent ER predictive ability for patients with HCC who underwent hepatectomy.

Aldolase B-driven lactagenesis and CEACAM6 activation promote cell renewal and chemoresistance in colorectal cancer through the Warburg effect.

Colorectal cancer (CRC) is a prevalent malignancy worldwide and is associated with a high mortality rate. Changes in bioenergy metabolism, such as the Warburg effect, are often observed in CRC. Aldolase B (ALDOB) has been identified as a potential regulator of these changes, but its exact role in CRC cell behavior and bioenergetic homeostasis is not fully understood. To investigate this, two cohorts of CRC patients were analyzed independently. The results showed that higher ALDOB expression was linked to unfavorable prognosis, increased circulating carcinoembryonic antigen (CEA) levels, and altered bioenergetics in CRC. Further analysis using cell-based assays demonstrated that ALDOB promoted cell proliferation, chemoresistance, and increased expression of CEA in CRC cells. The activation of pyruvate dehydrogenase kinase-1 (PDK1) by ALDOB-induced lactagenesis and secretion, which in turn mediated the effects on CEA expression. Secreted lactate was found to enhance lactate dehydrogenase B (LDHB) expression in adjacent cells and to be a crucial modulator of ALDOB-mediated phenotypes. Additionally, the effect of ALDOB on CEA expression was downstream of the bioenergetic changes mediated by secreted lactate. The study also identified CEA cell adhesion molecule-6 (CEACAM6) as a downstream effector of ALDOB that controlled CRC cell proliferation and chemoresistance. Notably, CEACAM6 activation was shown to enhance protein stability through lysine lactylation, downstream of ALDOB-mediated lactagenesis. The ALDOB/PDK1/lactate/CEACAM6 axis plays an essential role in CRC cell behavior and bioenergetic homeostasis, providing new insights into the involvement of CEACAM6 in CRC and the Warburg effect. These findings may lead to the development of new treatment strategies for CRC patients.