Lung cancer (LC) is one of the most prevalent cancers globally, with a high incidence among the elderly population. Elderly patients, particularly those with diabetes mellitus, are at an increased risk of postoperative complications, including pulmonary infections, due to weakened immune function and metabolic abnormalities. Postoperative pulmonary infection (PPI) is a predominant complication after thoracoscopic radical resection of LC, significantly affecting patient outcomes and increasing healthcare burdens. Determining risk factors for PPI in this vulnerable population is crucial for improving surgical outcomes and reducing infection rates.

To develop and validate a predictive model for PPI in elderly patients with diabetes undergoing thoracoscopic radical resection for LC and to assess its reliability and validity.

This retrospective study included 212 patients with LC who received treatment at our hospital from March 2015 to March 2022. General clinical information, surgical treatment details, and laboratory test results were collected and analyzed. Patients were grouped according to infection occurrence during the postoperative hospitalization period. Risk factors for PPIs were determined through logistic regression analysis, and a nomogram prediction model was established using R software to assess its predictive accuracy and performance.

Among the 212 patients [median age: 72 years (interquartile range: 60-82 years)], 41 developed PPI (19.34%), with Gram-negative bacteria being the predominant pathogens (64.14%). Factors, such as age of ≥ 70 years, presence of respiratory diseases, maximum tumor diameter of ≥ 4 cm, stages II-III, receiving neoadjuvant chemotherapy of ≥ 2 times preoperatively, surgery duration of ≥ 3 hours, chest drainage tube placement duration of ≥ 3.5 days, preoperative fasting blood glucose levels, hemoglobin A1c (HbA1c) levels, and multi-leaf resection, were markedly higher in the infection group than in the non-infection group. Conversely, forced expiratory volume in 1 second (FEV1) of ≥ 80% and albumin (Alb) levels were lower in the infection group. Multivariate logistic regression analysis revealed that receiving neoadjuvant chemotherapy of ≥ 2 times [odds ratio (OR) = 2.987; P = 0.036], maximum tumor diameter of ≥ 4 cm (OR = 3.959; P = 0.013), multi-leaf resection (OR = 3.18; P = 0.036), preoperative FEV1 of ≤ 80% (OR = 3.305; P = 0.029), and high HbA1c levels (OR = 2.39; P = 0.003) as key risk factors for PPI, whereas high Alb levels (OR = 0.507; P < 0.001) was protective. The nomogram model demonstrated excellent diagnostic ability (area under the curve = 0.901, 0.915), and calibration curves and decision curve analysis revealed good predictive performance and clinical applicability of the model.

The primary pathogens of PPI in elderly patients with diabetes and LC undergoing thoracoscopic radical resection are Gram-negative bacteria. The nomogram model, based on preoperative neoadjuvant chemotherapy cycles, maximum tumor diameter, range of resection, and preoperative FEV1, Alb, and HbA1c levels, shows high clinical value in predicting the risk of PPI in this patient population.

Type 2 diabetic nephropathy (T2DN) is a severe complication of diabetes mellitus, and identifying biomarkers for its prognosis remains a critical challenge. Previous studies have suggested potential roles of microRNAs (e.g., miR-495-3p), adiponectin (ADPN), and cardiometabolic index (CMI) in metabolic and renal pathologies. However, their combined predictive value for T2DN prognosis is not well understood.

To explore serum miR-495-3p, ADPN, and CMI levels in T2DN and their value in predicting prognosis.

A total of 98 T2DN patients (study group) and 49 type 2 diabetic patients with normal renal function (control group) were enrolled from February 2020 to February 2022. Serum levels of miR-495-3p, ADPN, and CMI were measured in both groups. Patients were followed up for 6 months to assess prognosis. Differences between groups were analyzed, and multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive value of these biomarkers.

The study group exhibited significantly lower miR-495-3p levels and higher ADPN and CMI levels compared to the control group (P < 0.05). Poor prognosis patients had even lower miR-495-3p and higher ADPN and CMI levels than those with good prognosis (P < 0.05). Multivariate analysis identified alanine aminotransferase, aspartate aminotransferase, urea nitrogen, serum creatinine, miR-495-3p, ADPN, and CMI as independent predictors of prognosis (P < 0.05). ROC analysis revealed area under the curve values of 0.762 (miR-495-3p), 0.902 (ADPN), 0.757 (CMI), 0.899 (alanine aminotransferase), 0.852 (aspartate aminotransferase), 0.916 (urea nitrogen), and 0.910 (serum creatinine) for predicting poor prognosis (P < 0.05).

Low miR-495-3p and high ADPN and CMI levels are linked to T2DN and poor prognosis, highlighting their potential for risk prediction and clinical management.

The prevalence of type 2 diabetes mellitus (T2DM) is rising, with hypertension as a common comorbidity that significantly increases cardiovascular and microvascular risks. Accurate prediction of hypertension in T2DM is essential for early intervention and personalized management. In this editorial, we comment on a recent retrospective study by Zhao et al, which developed a nomogram model using a large cohort of 26850 patients to predict hypertension risk in patients with T2DM. The model incorporated key independent risk factors, including age, body mass index, duration of diabetes, low-density lipoprotein cholesterol and urine protein levels, demonstrating promising discriminative power and predictive accuracy in internal validation. However, its external applicability requires further confirmation. This editorial discusses the clinical value and limitations of the predictive model, highlighting the unfavorable impact of hypertension on T2DM patients. Future research should evaluate the potential contribution of other risk factors to enhance risk prediction and improve the management of T2DM comorbidities.

The interplay between abnormal glucose metabolism and the progression of liver fibrosis in patients with both chronic hepatitis B (CHB) and type 2 diabetes mellitus (T2DM) remains unclear. Previous studies have suggested that the coexistence of these conditions may exacerbate liver inflammation and fibrosis; however, the impacts of dynamic changes in glucose metabolism indicators, hypoglycemic medication regimens, and glycemic control status on liver fibrosis require further elucidation.

To explore the effect of glycemic control on hepatic fibrosis in patients with CHB and T2DM.

A total of 420 patients with CHB and T2DM admitted to the Public Health Clinical Center of Chengdu between October 2018 and January 2022 were retrospectively included and classified according to liver stiffness measurement and glycemic control for between-group comparisons.

Significant differences were observed in the alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase, AST/ALT ratio, total bilirubin, direct bilirubin, diabetes treatment program, and thrombin time values among the liver fibrosis groups (adjusted P < 0.05). Significant differences in albumin and gamma-glutamyl transferase levels were observed among the groups categorized by glucose status at admission (adjusted P < 0.05). A positive correlation between fasting plasma glucose (FPG) and liver stiffness measurement was found to be mediated by ALT and AST. Fibrinogen and the international normalized ratio were positively correlated with glycated hemoglobin A1c, while the fibrosis-4 score, ALT, AST/ALT ratio, type III procollagen N-terminal peptide, ferritin, and activated partial thromboplastin time were correlated with FPG at admission. Additionally, AST was positively correlated with FPG at discharge (P < 0.05).

Specific glucose metabolic parameters, hypoglycemic agents, and glycemic control status markers are associated with hepatic fibrosis in patients with both CHB and T2DM. Close blood glucose monitoring, optimized use of hypoglycemic agents, and continuous maintenance of good glycemic control may slow the progression of liver fibrosis in patients with CHB and T2DM.

This article explores the bidirectional relationship between type 2 diabetes mellitus (T2DM) and depression, focusing on their shared pathophysiological mechanisms, including immune-inflammatory responses, gut-brain axis dysregulation, metabolic abnormalities, and neuroendocrine modulation. Research indicates that T2DM contributes to anxiety and depression through chronic low-grade inflammation, insulin resistance, gut microbiota imbalance, and hyperactivation of the hypothalamic-pituitary-adrenal axis. Conversely, depression may increase the risk of T2DM via lifestyle disruption, immune activation, and neurotransmitter imbalance. Additionally, metabolic pathway disturbances - such as reduced adiponectin, impaired insulin signaling, and altered amino acid metabolism - may influence mood regulation and cognition. The article further examines emerging therapeutic strategies targeting these shared mechanisms, including anti-inflammatory treatments, gut microbiota modulation, hypothalamic-pituitary-adrenal axis interventions, metabolic therapies (e.g., glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors), and multidisciplinary integrative management. Emphasizing the multisystem nature of diabetes-depression comorbidity, this work highlights the importance of incorporating mental health strategies into diabetes care to optimize outcomes and enhance patient quality of life.

Upper tract urothelial carcinoma (UTUC) is one of the common urothelial cancers. Although large cohorts have illustrated spectrum of germline mutations, landscape of somatic alterations has not been revealed in Chinese patients with UTUC.

This study involved 122 Chinese patients with UTUC. Paired tumor and germline DNA was sequenced to elucidate somatic mutation landscape, using targeted next-generation sequencing panel covering 520 cancer-related genes. We correlated specific gene mutations with clinicopathologic features, compared mutational signatures with previously reported data in other populations, and explored the similarities and differences in mutational signatures of UTUC originated from renal pelvis or ureter.

Somatic mutations were detected in 120/122 (98.4%) patients using this 520-gene panel sequencing. The top-ranking altered genes in Chinese patients with UTUC were KMT2D (48.4%), TERT promoter (46.7%), FGFR3 (41.0%), and TP53 (37.7%). Renal pelvis tumors had significantly higher TERT promoter (60.5% v 30.8%; P < .05) and CDKN2A (29.6% v 7.7%; P < .05) alteration frequency, while TP53 (56.4% v 30.9%; P < .05) and KMT2D (76.9% v 35.8%; P < .001) gene mutations were enriched in patients with ureteral tumors. By comparing the mutational spectrum with Japanese and Western populations, we identified the unique genetic characteristics and potential precision treatment targets of the Chinese UTUC population. Finally, we found that patients with TP53/MDM2-mutant subtype or triple-negative subtype had significantly worse progression-free survival than other subtypes.

This study elucidated genomic landscape of Chinese patients with UTUC and revealed similarity and disparity of genomic alterations with other populations. Our research lays the foundation for understanding UTUC in China and designing precise diagnosis and treatment.

To summarize clinically relevant genomic alterations in solid tumor samples from over 10,000 patients.

Descriptive statistics were used to summarize findings of retrospectively analyzed OncoExTra assay data from solid tumor samples.

The analysis cohort included 11,091 solid tumor samples from 10,768 patients. Therapeutically actionable alterations were present in 92.0% of patient samples. Biomarkers associated with on- or off-label FDA-approved therapies were detected in 29.2% and 28.0% of samples, respectively. The prevalence of hotspot alterations detected at variant allele frequency (VAF) <5% was analyzed among 7,481 samples (67.5%) harboring ≥1 of these events: 13.7% (1,022 of 7,481) had ≥1 alteration detected at VAF <5%, and 9.8% (558 of 5,690) of hotspot alterations associated with an on- or off-label FDA-approved therapy were detected at VAF <5%. Common and rare mutations in the TERT promoter were found in 8.4% (933) of samples. Whole transcriptome sequencing detected clinically relevant fusions in 7.5% of samples, with highest frequencies in prostate cancer (42.0%). The METe14 transcript was found in 14 NSCLC samples (2.7%).

The broad capabilities of the OncoExTra assay detected therapeutically actionable and other clinically relevant genomic events that can inform clinical decision-making for patients with advanced solid tumors.

Death after cytoreductive surgery for advanced-stage ovarian cancer is more frequent in low-volume hospitals. Neoadjuvant chemotherapy (NACT) has been shown to reduce surgical complexity, complications, and surgical mortality without compromising oncologic outcomes.

To measure whether more frequent NACT utilization is associated with postoperative mortality and overall survival after cytoreductive surgery, especially in low-volume hospitals.

This cross-sectional study included patients treated for newly diagnosed stage III or IV epithelial ovarian cancer at Commission on Cancer-accredited cancer programs in the United States between January 2010 and December 2019. Data were analyzed from August 2023 to April 2025.

The main exposures of interest were cancer program-level rates of NACT and tertile of program mean annual volume of cytoreductive surgery (<12.0, 12.0-23.9, or ≥24.0 cases/y).

Standardized rates and odds ratios (ORs) for 90-day perioperative morality and differences in 60-month life expectancy (restricted mean survival time).

A total of 70 707 patients (mean [SD] age, 63.1 [12.1] years; 5807 [8.2%] Black, 4745 [6.7%] Hispanic, and 56 336 [79.7%] White) treated in 1333 programs were identified. After adjusting for observed demographic and clinical covariates, 90-day surgical mortality was lower in centers with higher NACT rates, and the magnitude of this association differed by hospital volume (P for interaction < .001). High utilization of NACT (59%) compared with low utilization (22%) was associated with a larger decrease in 90-day mortality in high-volume centers (OR, 0.26; 95% CI, 0.17-0.41; rates, 10.0% vs 2.9%) compared with average-volume centers (OR, 0.49; 95% CI, 0.33-0.72; rates, 7.3% vs 3.7%) or low-volume centers (OR, 0.48; 95% CI, 0.39-0.60; rates, 9.5% vs 4.8%). Among high-volume centers, high utilization was associated with a 4.0 month (95% CI, 1.6-6.5 month)-improvement in 60-month life expectancy compared with low NACT utilization (42.2 vs 38.1 months).

In this cross-sectional study, treatment in high-volume centers with high NACT utilization was associated with the lowest 90-day surgical mortality and longest 60-month survival for patients with advanced stage ovarian cancer.

A major emphasis in personalized medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. One relevant study design is the targeted design, in which patients have consented for their specimens to be obtained at baseline and the specimens are sent to a laboratory for assessing the biomarker status prior to randomization. Here, only biomarker-positive patients will be randomized to either an experimental or the standard of care arms. Many biomarkers, however, are derived from patient tissue specimens, which are heterogeneous leading to variability in the biomarker levels and status. This heterogeneity would have an adverse impact on the power of an enriched biomarker clinical trial. In this article, we show the adverse effect of using the uncorrected sample size and overcome this challenge by presenting an approach to adjust for misclassification for the targeted design. Specifically, we propose a sample size formula that adjusts for misclassification and apply it in the design of two phase III clinical trials in renal and prostate cancer.

Neoadjuvant chemotherapy (NACT) plays a pivotal role in modulating the immune microenvironment. However, its impact on immune checkpoint expression and the prognostic significance of immune checkpoints in breast cancer (BC) remain unclear. In this retrospective study, we used immunohistochemistry assays to evaluate PD-L1, CD155, FGL1, and Galectin-9 expression in pre- and post-NACT BC samples. CD155 expression before NACT was significantly elevated in triple-negative breast cancer (TNBC) and showed a tendency to be associated with pathological complete and partial responses. FGL1 was highly expressed in HER2-positive BC and TNBC before NACT. After treatment, higher CD155 expression was more frequently observed in patients with advanced pathological lymph node stages (pN2-N3) than in those with lower stages (pN0-N1). Comparison of paired pre-treatment and residual cancer tissues revealed a significant decrease in PD-L1, CD155, and Galectin-9 expression following NACT. Notably, decreased CD155 expression significantly correlated with improved therapeutic response, particularly in patients with high Ki-67 expression. Patients with reduced CD155 expression after NACT had more favourable disease-specific survival than those with unchanged and increased expression. Moreover, decreased CD155 expression in residual BC showed a trend toward improved overall survival. Changes in PD-L1 and Galectin-9 expression after therapy were not associated with patient survival or pathological response. We conducted further analysis using the METABRIC database and found high CD155 expression after chemotherapy was related to decreased CD8+ T cell infiltration and poor outcome in TNBC. Our findings indicated that NACT induced significant changes in immune checkpoint expression in BC. PD-L1, CD155, and Galectin-9 expression were reduced in post-treatment samples compared to pre-treatment samples. Specifically, unchanged or elevated CD155 expression after NACT was associated with poor disease-specific survival. Further more, high CD155 expression after chemotherapy was related to decreased CD8+ T cell infiltration and poor outcome in TNBC.