bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolic reprogramming and immunological changes in the microenvironment of esophageal cancer: future directions and prospects
Authors: Guo Zhi-Xun , Ma Jia-Li , Zhang Jin-Qiu , Yan Ling-Ling , Zhou Ying , Mao Xin-li , Li Shao-Wei , Zhou Xian-Bin 
Published: January 2025

BackgroundEsophageal cancer (EC) is the seventh-most prevalent cancer worldwide and is a significant contributor to cancer-related mortality. Metabolic reprogramming in tumors frequently coincides with aberrant immune function alterations, and extensive research has demonstrated that perturbations in energy metabolism within the tumor microenvironment influence the occurrence and progression of esophageal cancer. Current treatment modalities for esophageal cancer primarily include encompass chemotherapy and a limited array of targeted therapies, which are hampered by toxicity and drug resistance issues. Immunotherapy, particularly immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 pathway, has exhibited promising results; however, a substantial proportion of patients remain unresponsive. The optimization of these immunotherapies requires further investigation. Mounting evidence underscores the importance of modulating metabolic traits within the tumor microenvironment (TME) to augment anti-tumor immunotherapy.MethodsWe selected relevant studies on the metabolism of the esophageal cancer tumor microenvironment and immune cells based on our searches of MEDLINE and PubMed, focusing on screening experimental articles and reviews related to glucose metabolism, amino acid metabolism, and lipid metabolism, as well their interactions with tumor cells and immune cells, published within the last five years. We analyzed and discussed these studies, while also expressing our own insights and opinions.ResultsA total of 137 articles were included in the review: 21 articles focused on the tumor microenvironment of esophageal cancer, 33 delved into research related to glucose metabolism and tumor immunology, 30 introduced amino acid metabolism and immune responses, and 17 focused on the relationship between lipid metabolism in the tumor microenvironment and both tumor cells and immune cells.ConclusionThis article delves into metabolic reprogramming and immune alterations within the TME of EC, systematically synthesizes the metabolic characteristics of the TME, dissects the interactions between tumor and immune cells, and consolidates and harnesses pertinent immunotherapy targets, with the goal of enhancing anti-tumor immunotherapy for esophageal cancer and thereby offering insights into the development of novel therapeutic strategies.
Gene mutation in diabetic patients with lung adenocarcinoma: a real-world retrospective cohort study
Authors: Yang Lei , Hong Yang , Zeng TingTing , Yue HongMei , Jiang DePeng 
Published: January 2025

PurposeThe incidence of lung cancer is closely associated with diabetes; however, it remains unclear whether diabetes influences the genetic mutations present in lung cancer. Therefore, we will compare the genetic mutations in patients with lung adenocarcinoma (ADC) who have diabetes against those who do not.MethodsWe included 279 patients diagnosed with lung adenocarcinoma (143 with diabetes and 136 without diabetes) at the Second Affiliated Hospital of Chongqing Medical University between 2016 and 2023, and analyzed the clinical characteristics and genetic mutation profiles of all participants.ResultsIn comparison to ADC patients without diabetes, those with diabetes exhibited a lower overall gene mutation rate (49.7% vs. 65.4%, P = 0.008). Female ADC patients demonstrated a higher total gene mutation rate and EGFR gene mutation rate than their male counterparts (49.3% vs. 66.9%, P = 0.003; 27.6% vs. 58.3%, P &lt; 0.001, respectively), although their TP53 gene mutation rate was lower (8.6% vs. 2.4%, P = 0.027). ADC patients without a smoking history had a higher gene mutation rate and EGFR gene mutation rate than those with a smoking history (62.6% vs. 47.4%, P = 0.014; 51.6% vs. 22.7%, P &lt; 0.001, respectively), but a lower KRAS gene mutation rate (4.4% vs. 14.4%, P = 0.003). Conversely, ADC patients with a drinking history had a lower EGFR gene mutation rate than those without (48% vs. 62.6%, P = 0.018; 31.0% vs. 47.5%, P = 0.007), yet a higher KRAS gene mutation rate (14.0% vs. 4.5%, P = 0.005). Univariate and multivariate linear regression analyses revealed that being female, having no smoking history, and being in phase II or IV of tumor stage were associated with gene mutation. Subgroup analysis indicated that the rate of gene mutation in male smoking lung adenocarcinoma patients with diabetes was significantly lower than in those without diabetes.ConclusionThis retrospective study of real-world data suggests that patients with lung adenocarcinoma and diabetes may have a reduced likelihood of developing genetic mutations, particularly among male smokers. Furthermore, gender, smoking history, and tumor stage may be correlated with the presence of gene mutations.
Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy
Authors: Liang Min , Luo Fuyuan 
Published: January 2025

IntroductionSmall cell lung cancer (SCLC) is a highly aggressive form of lung cancer, and chemotherapy remains a cornerstone of its management. However, the treatment is associated with significant risks, including heightened toxicity and early mortality. This study aimed to quantify the 90-day mortality rate post-chemotherapy in SCLC patients, identify associated features, and develop a predictive machine learning model.MethodsThis study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2018) to identify prognostic features influencing early mortality in SCLC patients. Prognostic features were selected through univariate logistic regression and Lasso analyses. Predictive modeling was performed using advanced machine learning algorithms, including XGBoost, Multilayer Perceptron, K-Nearest Neighbor, and Random Forest. Additionally, traditional models, such as logistic regression and AJCC staging, were employed for comparison. Model performance was evaluated using key metrics, including the Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, the Kolmogorov–Smirnov (KS) statistic, and Decision Curve Analysis (DCA).ResultsAnalysis of 12,500 eligible patients revealed 10 clinical features significantly impacting outcomes. The XGBoost model demonstrated superior discriminatory capability, achieving AUC scores of 0.95 in the training set and 0.78 in the validation set. It outperformed comparative models across all datasets, as evidenced by its AUC, KS score, calibration, and DCA results. Additionally, the model was integrated into a web-based platform to improve accessibility.ConclusionThis study introduces a machine learning model alongside a web-based support system as critical resources for healthcare professionals, facilitating personalized clinical decision-making and enhancing treatment strategies for SCLC patients post-chemotherapy.
Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images
Authors: Song Lei , Zhang Xuesong , Zhang Jian , Wu Jie , Wang Jinkai , Wang Feng 
Published: January 2025

PurposeAcute Mesenteric Ischemia (AMI) is a critical condition marked by restricted blood flow to the intestine, which can lead to tissue necrosis and fatal outcomes. We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.MethodsA retrospective study was conducted on 228 patients suspected of AMI, divided into training and test sets. Clinical data (medical history and laboratory indicators) was included in a multivariate logistic regression analysis to identify the independent factors associated with AMI and establish a clinical factors model. The arterial and venous CTA images were utilized to construct DL model. A Fusion Model was constructed by integrating clinical factors into the DL model. The performance of the models was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).ResultsAlbumin and International Normalized Ratio (INR) were associated with AMI by univariate and multivariate logistic regression (P &lt; 0.05). In the test set, the area under ROC curve (AUC) of the clinical factor model was 0.60 (sensitivity 0.47, specificity 0.86). The AUC of the DL model based on CTA images reached 0.90, which was significantly higher than the AUC values of the clinical factor model, as confirmed by the DeLong test (P &lt; 0.05). The Fusion Model also showed exceptional performance in terms of AUC, accuracy, sensitivity, specificity, and precision, with values of 0.96, 0.94, 0.94, 0.95, and 0.98, respectively. DCA indicated that the Fusion Model provided a greater net benefit than those of models based solely on imaging and clinical information across the majority of the reasonable threshold probabilities.ConclusionThe incorporation of CTA images and clinical information into the model markedly enhances the diagnostic accuracy and efficiency of AMI. This approach provides a reliable tool for the early diagnosis of AMI and the subsequent implementation of appropriate clinical intervention.
Comparative transcriptome analysis reveals molecular mechanisms of resistance in Chinese cabbage to Plasmodiophora brassicae pathotype 11
Authors: Qiu Yue , Zhang Jinhao , Deng Chunju , Yuan Jiasheng , Wang Bowen , Meng Han , Mohany Mohamed , Zeng Liting , Wei Lanfang , Ahmed Waqar , Ji Guanghai 
Published: January 2025

Background and aimsClubroot caused by the soilborne obligate parasite Plasmodiophora brassicae, is a devastating disease of Chinese cabbage and other crucifers. The innate diversity and adaptability of this pathogen pose significant challenges to effective control measures. However, the varied response mechanisms exhibited by hosts to pathotype 11 at a molecular level are still unclear.Methods and resultsThis study investigated the resistance response and underlying molecular mechanism of two Chinese cabbage (Brassica rapa) varieties (JP and 83-1) to P. brassicae pathotype 11 through comparative transcriptome analysis and microscopic study. Results demonstrated that 14 days after inoculation (dai) is a critical time point of the infection process for resistant variety to inhibit the proliferation of P. brassica. Although the highly resistant variety JP did not exhibit a complete immune response to pathotype 11, it demonstrated a significant resistance level against P. brassicae pathotype 11 by restricting its proliferation in the xylem vessels. Microscopic analysis at 21 dai revealed that the resistant cultivar (JP) root structure remained largely unaffected, while the roots of the susceptible cultivar (83-1) exhibited significant tissue distortion and gall formation, underscoring the effectiveness of the resistance mechanisms. Comparative transcriptome analysis revealed substantial differences in the number and types of differentially expressed genes (DEGs) between the two cultivars, highlighting the key pathways involved in the resistance response. In the resistant cultivar (JP), a total of 9,433 DEGs were identified, with 4,211 up-regulated and 5,222 down-regulated. In contrast, the susceptible cultivar (83-1) exhibited 6,456 DEGs, with 2,781 up-regulated and 3,675 down-regulated. The resistant cultivar showed a pronounced activation of genes involved in hormone signaling, cell wall, secondary metabolism, redox state, and signaling process. Therefore, our speculation revolves around the potential resistant mechanism of this variety, which inhibits the proliferation of P. brassicae in the roots via secondary metabolites, cell wall, and ROS and also regulates physiological mechanisms mediated by plant hormones such as ABA to adapt to adverse environmental conditions such as water scarcity induced by the pathogen.ConclusionThis study unveils the intricate defense mechanisms potentially activated within Chinese cabbage when confronted with P. brassicae pathotype 11, offering valuable insights for breeding programs and the development of novel strategies for managing clubroot disease in Brassica crops. Furthermore, this study highlights the pivotal role of host-specific molecular defense mechanisms that underlie resistance to P. brassicae pathotype 11.
Baseline liver fibrosis-4 score correlates to the progression of anxiety and cognitive impairment in patients with Parkinson’s disease
Authors: Cheng Yongqing , Chen Li , Zhu Honghong , Ge Yingchao , Li Lei , Guo Yan , Wang Xin , You Shuangfei , He Guojun , Xue Shouru 
Published: January 2025

BackgroundNon-alcoholic fatty liver disease (NAFLD) or liver fibrosis may share similar pathophysiological features with Parkinson’s disease (PD), yet their correlation was unclear. This study aimed to explore their correlation between PD and liver fibrosis using the fibrosis-4 score (FIB-4) as a surrogate marker.MethodsWe analyzed Parkinson’s Progression Markers Initiative (PPMI) data and enrolled PD patients with comprehensive baseline and 5-year follow-up time-point clinical data. Participants were categorized based on FIB-4 levels to assess the association between FIB-4 scores and various clinical scales, controlling for potential confounders. Differences in the progression of clinical scales over five years were compared using generalized linear mixed models (GLMM).ResultsBaseline FIB-4 levels positively correlated to scores of baseline section III of the Unified-Parkinson Disease Rating Scale (UPDRS III) (r = 0.145, p = 0.017), Epworth Sleepiness Scale (EPSS) (r = 0.140, P = 0.022), Hopkins Verbal Learning Test (HVLT)-delayed recall (r = 0.128, P = 0.036) and HVLT-retention (r = 0.128, p = 0.036). GLMM analysis revealed an independent correlation between FIB-4 subgroup*time and several clinical scales including the State-trait Anxiety Inventory (STAI), Symbol Digit Modalities Test (SDMT), Semantic Fluency Test (SF), HVLT-total recall, and HVLT-delayed recall, with the high FIB-4 subgroup exhibiting a greater decline in these scores compared to the low FIB-4 subgroup (all p&lt;0.05).ConclusionElevated baseline FIB-4 correlated to more severe baseline daytime sleepiness, motor symptoms, and memory function in PD patients, along with a more rapid decline in cognitive functions such as executive function, information processing ability, and memory. Additionally, a high FIB-4 might confer a protective effect against anxiety.
Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients
Authors: Zhang Mei-Ning , Zhou Yi-Chen , Zeng Zhu , Zeng Cun-Liang , Hou Bo-Tao , Wu Gui-Rong , Jiao Qiao , Ma Dai-Yuan 
Published: January 2025

PurposeThe paper aims to investigate the factors influencing cancer-related fatigue (CRF) in lung cancer patients and construct a CRF risk prediction model, providing effective intervention strategies for clinical medical staff.MethodsThis paper employs convenience sampling to select 400 lung cancer patients who visited a tertiary hospital in Dazhou, Sichuan Province, from January 2021 to January 2022. A questionnaire survey was conducted using the Revised Piper Fatigue Scale (PFS-R), Pittsburgh Sleep Quality Index (PSQI), and Hospital Anxiety and Depression Scale (HADS) to collect data on patient demographics and sociological characteristics, disease-related information, physiological indicators, sleep quality, mental health, and other relevant factors. To explore the factors influencing CRF in lung cancer patients, single-factor analysis and multiple logistic regression analysis were performed. A CRF risk prediction model was then established, with its predictive performance and calibration evaluated using ROC curves.FindingsThe results of multivariate logistic regression analysis showed that gender, age, education level, living status, daily exercise, clinical stage, course of disease, treatment mode, chronic disease, BMI, hemoglobin, serum albumin, blood glucose, potassium concentration, magnesium concentration, PSQI score and HAD score were the influencing factors of CRF in lung cancer patients (P&lt;0.05). The AUC of the model construction group and the model validation group were 0.863 and 0.838, respectively, and the results of Hosmer-Lemeshow fit test showed that χ2 = 7.540, P=0.378&gt;0.05 of the model construction group and χ2 = 8.120, P=0.320&gt;0.05 of the model validation group indicated that the model had high prediction accuracy.Originality/valueThe risk prediction model for CRF holds significant clinical value. It can help medical staff to promptly identify high-risk patients, develop personalized intervention strategies, alleviate fatigue symptoms, and improve overall patient quality of life.
Baseline (derived) neutrophil-lymphocyte ratio associated with survival in gastroesophageal junction or gastric cancer treated with ICIs
Authors: Yu Chengyang , Jiang Hao , Wang Liezhi , Jiang Zufu , Jin Chong 
Published: January 2025

ObjectiveWe carried out the meta-analysis to determine the predictive value of baseline neutrophil to lymphocyte ratio (NLR) and derived neutrophil to lymphocyte ratio (dNLR) levels in patients with gastroesophageal junction or gastric cancer (GJGC) who underwent immune checkpoint inhibitor (ICI) treatment.MethodsEligible articles were obtained through PubMed, the Cochrane Library, EMBASE, and Google Scholar, until April 15, 2023. The clinical outcomes evaluated in this study encompassed overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR)ResultsA total of 24 articles with 2221 patients were included in this meta-analysis. The pooled results demonstrated that patients with high NLR levels had significantly poorer OS (HR: 1.860, 95% CI: 1.564-2.213, p &lt; 0.001) and PFS (HR: 1.678, 95% CI: 1.354-2.079, p &lt; 0.001), and lower ORR (OR: 0.754, 95% CI: 0.621-0.915, p = 0.004) and DCR (OR: 0.391, 95% CI: 0.262-0.582, p &lt; 0.001). Besides, we also found that high dNLR levels were significantly associated with shorter OS (HR: 2.117, 95% CI: 1.590-2.820, p &lt; 0.001) and PFS (HR: 1.803, 95% CI: 1.415-2.297, p &lt; 0.001).ConclusionLow baseline (Derived) NLR has the potential to predict the good efficacy of ICIs and survival outcomes in patients with GJGC. (Derived) NLR could be useful in determining the optimal treatment strategies for these patients.
Disulfidptosis-related gene SLC3A2: a novel prognostic biomarker in nasopharyngeal carcinoma and head and neck squamous cell carcinoma
Authors: Zhang Xinyi , Lin Yiqi , Shi Liang , Zhai Aixia , Wu Chao , Zhu Qian-Ying 
Published: January 2025

IntroductionNasopharyngeal carcinoma (NPC), one of the most common malignancies of the head and neck, is characterised by a complex pathogenesis and an unfavourable prognosis. Recently, disulfidoptosis, a novel form of cell death, has been proposed. Several studies in recent years have extensively investigated the function of the disulfidoptosis-related SLC7A11 gene in cancer, but the role of its partner protein, SLC3A2, remains unknown unclear in NPC.MethodsGEO database analysis confirmed SLC3A2's prognostic impact on nasopharyngeal carcinoma. ROC, Kaplan-Meier analyses, and stage-specific expression studies showed a strong correlation with poor HNSC prognosis. GO and KEGG analyses pinpointed relevant signaling pathways. In vitro, SLC3A2's influence on cell proliferation, migration, and invasion was evaluated through CCK8, wound healing, colony formation, transwell assays, and cell cycle analysis.ResultsIn this study, we identified the high expression of SLC3A2 in NPC and head and neck squamous cell carcinoma (HNSC) and analyzed its potential mechanism and correlation with patient prognosis. Furthermore, a negative relationship was found between the expression level of SLC3A2 and the extent of immune cell infiltration and immune checkpoint. Differentially expressed genes (DEGs) between the high and low SLC3A2 expression groups were primarily involved in cytokine-cytokine receptor interaction and immune response. Finally, in vitro experiments demonstrated that SLC3A2 stimulates tumor cell proliferation and migration. DiscussionIn conclusion, these results indicated a strong association between SLC3A2 and progression in both NPC and HNSC, suggesting it as a promising biomarker for predicting adverse prognosis in NPC and HNSC patients.
Survival and prognostic factors for primary lung extranodal NK/T-cell lymphoma: a retrospective study of data from China and the SEER database
Authors: Li Qiuyu , Zuo Haoyu , Liu Chengyang , Yang Jing , Dai Nini 
Published: January 2025

Background and aimExtranodal NK/T-cell lymphoma (ENKTL) is a rare and aggressive subtype of non-Hodgkin’s lymphoma that most commonly affects the nasal cavity and nasopharynx. The lung is a rare site for ENKTL involvement, and its clinical behavior and prognostic factors are not well understood. This study aimed to analyze survival outcomes and identify prognostic factors in patients with primary lung ENKTL.MethodsA retrospective analysis was conducted using data from 20 cases of primary lung ENKTL, including four patients who were treated at Peking University Third Hospital in Beijing and 16 patients were extracted from the Surveillance, Epidemiology, and End Results Program database. Clinical characteristics, treatment modalities, and survival data were collected and analyzed using Kaplan–Meier and Cox regression models to identify potential prognostic factors.ResultsThe study cohort included 13 male (65%) and 7 female (35%) patients with a median age of 57 years. Sex was a significant predictor of survival (P = 0.030), with female patients having lower survival rates. Other factors, including age, race, and disease stage, were not significantly associated with survival. Most patients received chemotherapy (45%) or a combination of chemotherapy and radiotherapy (5%), but treatment data were incomplete for 40% of the cohort. The median overall survival was poor, reflecting the aggressive nature of primary lung ENKTL.ConclusionsPrimary lung ENKTL is a rare, aggressive malignancy with limited available data. In this cohort, sex was a significant prognostic factor, while other demographic and clinical variables did not show significant associations with survival. Future research should focus on understanding the molecular and immunological drivers of this disease, with an emphasis on discovering novel therapeutic approaches. Large-scale multicenter studies are needed to improve diagnostic and treatment strategies for primary lung ENKTL.