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Abstract Background Lung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES. Design A retrospective study. Method The 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods and four machine learning algorithms (DT, RF, SVM, and XGBoost) were used to construct prognostic risk prediction models. Results The CoxPH regression-based nomogram demonstrated reliable predictive accuracy for survival at 60, 90, and 120 days. Among the machine learning models, XGBoost showed the best performance, whereas RF had the lowest accuracy at 60 days, DT at 90 days, and SVM at 120 days. Key predictors across all models included Karnofsky Performance Status (KPS) score, quality of life (QOL) score, and cough symptoms. Conclusions CoxPH, DT, RF, SVM, and XGBoost models are effective in predicting mortality risk over 60–120 days in patients with advanced lung cancer with low SES. Monitoring KPS, QOL, and cough symptoms is crucial for identifying high-risk patients who may require intensified care. Clinicians should select models tailored to individual patient needs and preferences due to varying prediction accuracies. Reporting method This study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline. Patient or public contribution No patient or public contribution.

Abstract Background Lymphangiogenesis has been reported to play crucial roles in the metastasis of thyroid cancer (THCA), but despite the significant research on lymphangiogenesis in THCA, the precise regulatory mechanism remains unclear. Methods Public databases including the Cancer Genome Atlas (TCGA), TIMER, and UALCAN were used to analyze and visualize the expression of TET3 and AHR in THCA, and the correlation between these molecules were used by TIMER. Additionally, RT-PCR and Western Blot were performed to determine the mRNA and protein expression of related proteins. Plate colony formation, wound healing, cell cycle, apoptosis, angiogenesis and transwell assay were used to examine the ability of proliferation, movement, lymphangiogenesis, migration and invasion of THCA cells. Results Analysis of the TCGA database revealed higher expression levels of TET3 and AHR in tumor tissue compared to normal tissue in THCA. Additionally, a strong correlation was observed between TET3 and AHR. UALCAN database demonstrated that high expression of TET3 and AHR was associated with advanced THCA TNM stages in THCA patients. Furthermore, TET3 activation accelerated THCA cell proliferation by inducing G2/M phase arrest and suppressing apoptosis, while AHR inactivation reduced THCA cell proliferation by decreasing G2/M phase arrest and promoting apoptosis in vitro. Notably, both TET3 and AHR significantly enhanced THCA cell lymphangiogenesis, migration and invasion. Moreover, TET3 activation and AHR inactivation regulated HIF-1α/VEGF signaling pathway, which ultimately, blocked the HIF-1α/VEGF in THCA cells and impaired their movement, migration and invasion abilities. Conclusions The combined action of TET3 and AHR to promote lymphangiogenesis in THCA through the HIF-1α/VEGF signaling pathway, and targeting them might provide a potential treatment strategy for THCA.

Abstract Background Mathematical expressions mainly include arithmetic (such as 8 − (1 + 3)) and algebra (such as a − (b + c)). Previous studies have shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although previous studies have revealed that algebra was dissociated from arithmetic, the neural bases of the dissociation between algebraic processing and arithmetic is still unclear. The present study uses functional magnetic resonance imaging (fMRI) to identify the specific brain networks for algebraic and arithmetic processing. Methods Using fMRI, this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. The functional connectivity was analyzed by a seed-based region of interest (ROI)-to-ROI analysis. Results Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. For algebra, significant positive functional connectivity was observed between the visuospatial network and semantic network, whereas for arithmetic, significant positive functional connectivity was observed only between the visuospatial network and phonological network. Conclusion These findings suggest that algebra relies on the semantic network and conversely, arithmetic relies on the phonological and visuospatial networks.