Combined Small Cell Lung Cancer (C-SCLC) is a relatively rare type of lung cancer, which involves a combination of small cell carcinoma (SCLC) and any type of non-small cell lung cancer (NSCLC) histological components. The incidence of C-SCLC is increasing, with a higher proportion of affected patients being male and having a history of smoking. Currently, the diagnosis of C-SCLC is mainly based on pathology, and the most common pathological types of the coexisting non-small cell carcinoma components are squamous cell carcinoma and adenocarcinoma. Several studies have identified EGFR, TP53, and RB1 gene mutations, as well as high expression of YAP1, are potential biomarkers of C-SCLC. Treatment options for C-SCLC include surgery, chemotherapy, and radiotherapy in combination. For early-stage C-SCLC, surgical resection is the most effective method, while for patients in the middle or late stages who miss the surgical opportunity, chemotherapy and radiotherapy offer the most benefit. Currently, immunotherapy and targeted therapy show certain potential in the treatment of C-SCLC patients.
ObjectiveTo compare the efficacy of mediastinoscope-assisted transhiatal esophagectomy (MATHE) and functional minimally invasive esophagectomy (FMIE) for esophageal cancer. MethodsPatients who underwent minimally invasive esophagectomy at Jining No.1 Hospital from March 2018 to September 2022 were retrospectively included. The patients were divided into a MATHE group and a FMIE group according to the procedures. The patients were matched via propensity score matching (PSM) with a ratio of 1 : 1 and a caliper value of 0.2. The clinical data of the patients were compared after the matching. ResultsA total of 73 patients were include in the study, including 54 males and 19 females, with an average age of (65.12±7.87) years. There were 37 patients in the MATHE group and 36 patients in the FMIE group. Thirty pairs were successfully matched. Compared with the FMIE group, MATHE group had shorter operation time (P=0.022), lower postoperative 24 h pain score (P=0.031), and less drainage on postoperative 1-3 days (P<0.001). FMIE group had more lymph node dissection (P<0.001), lower incidence of postoperative hoarseness (P=0.038), lower white blood cell and neutrophil counts on postoperative 1 day (P<0.001). There was no statistically significant difference in the bleeding volume, R0 resection, hospital mortality, postoperative hospital stay, anastomotic leak, chylothorax, or pulmonary infection between the two groups (P>0.05). ConclusionCompared with the FMIE, MATHE has shorter operation time, less postoperative pain and drainage, but removes less lymph nodes, which is deficient in oncology. For some special patients such as those with early cancer or extensive pleural adhesions, MATHE may be a suitable surgical method.
ObjectiveTo reveal the expression patterns of tertiary lymphoid structure (TLS)-related gene features in non-small cell lung cancer (NSCLC), and further construct a prognostic prediction model for NSCLC patients based on machine learning, as well as evaluate the correlation between the TLS risk score and tumor immune microenvironment characteristics and potential immunotherapy benefits. MethodsThe training cohort was derived from the NSCLC dataset of The Cancer Genome Atlas (TCGA) database, including 994 tumor samples with survival time >0 days (and 110 normal tissue samples for differential expression analysis). External validation cohorts were obtained from the Gene Expression Omnibus (GEO) database, including GSE30219 (n=289) and GSE72094 (n=398). Based on the expression levels of TLS-related genes, consensus clustering was performed to identify molecular subtypes associated with TLS. Weighted gene co-expression network analysis (WGCNA) was applied to screen co-expression modules significantly correlated with TLS subtypes. To construct the TLS prognostic model, 101 algorithm combinations comprising 10 machine learning algorithms were employed for model training and selection. A high-confidence TLS prognostic model was established and systematically evaluated for its predictive performance in both the training cohort and external validation cohorts. Additionally, associations between the model and clinical characteristics as well as immune microenvironment indicators were analyzed. ResultsConsensus clustering identified three TLS molecular subtypes in the TCGA-NSCLC cohort (n=994): C1 (n=441), C2 (n=263), and C3 (n=290). These subtypes exhibited distinct overall survival outcomes and demonstrated differences in clinical characteristics and immune infiltration levels. Under the soft threshold β=9 condition, WGCNA identified seven co-expression modules, among which the blue module (r=0.32) and yellow module (r=0.44) showed the highest correlations with TLS subtypes. From these two modules containing 758 genes, univariate Cox regression analysis selected 32 prognosis-related genes. Through optimization across 101 algorithm combinations, the optimal TLS prognostic model was established and validated in external cohorts. This model stratified patients into high-risk and low-risk groups, demonstrating stable prognostic discrimination capability in TCGA, GSE30219, and GSE72094 datasets. Immune infiltration analysis revealed significantly higher infiltration levels of multiple immune cell types in the low-risk group. Drug sensitivity analysis indicated that the low-risk group exhibited greater sensitivity to cisplatin, docetaxel, gemcitabine, and paclitaxel. Additionally, pharmacological screening identified four potential candidate drugs (BI-2536, GSK461364, Paclitaxel, SB-743921) in the Cancer Therapeutics Response Portal (CTRP) database and three candidates (Epothilone-b, Mitoxantrone, Volasertib) in the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) database for high-risk group patients. ConclusionThe TLS risk score serves as an independent prognostic factor effectively predicting NSCLC patient outcomes, representing a potential biomarker for NSCLC.
