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According to 2019 statistical data, the nationwide surgical volume reached 69.3 million patient cases, with pulmonary surgeries constituting a vital component and showing an annual increase. However, as the number of surgeries rises, so does the incidence of perioperative complications, including thrombosis, which adversely affects patient outcomes. Therefore, understanding of the causes, risk factors, and mechanisms of thrombosis in the context of pulmonary surgeries, as well as implementing effective preventive measures, can mitigate the adverse effects of thrombosis related to pulmonary surgeries. This article discusses the incidence, risk factors, clinical manifestations, treatment, and prognosis of postoperative thrombosis in pulmonary surgeries, aiming to provide new insights for related clinical research.

Open in new tab Download slide OBJECTIVES Neoadjuvant therapy has gained widespread acceptance as the standard modality for locally advanced non-small cell lung cancer. However, the clinical benefit of sleeve lobectomy (SL) or pneumonectomy (PN) following neoadjuvant therapy remains controversial. METHODS The clinical and pathological characteristics of non-small cell lung cancer patients who underwent SL or PN after neoadjuvant therapy at a high-volume single centre between December 2019 and March 2023 were retrospectively collected. The SL group was matched 4:1 with the PN group by propensity score matching. The surgical outcomes were systematically collected and analysed. RESULTS During a 5-year study period, the majority of patients (175 of 215, 81.4%) underwent the SL procedure, while 40 patients (18.6%) underwent PN. Following propensity score matching, the SL group exhibited lower postoperative arrythmia (4.8% vs 26.9%, P  <   0.001), lower 30-day mortality (1.0% vs 7.7%, P  =   0.046) and a shorter length of postoperative hospital stay (6.0 days vs 10.0 days, P  <   0.001), compared with the PN group. In addition, no significant difference was observed between the two groups in terms of disease-free survival or overall survival (P  =   0.977 and P  =   0.913, respectively). CONCLUSIONS SL stands as a safe and feasible option for patients with centrally located non-small-cell lung cancer who have undergone neoadjuvant therapy, in comparison to PN. This finding suggests that SL remains the preferable choice when feasible in the context of the widespread utilization of neoadjuvant therapy. [ABSTRACT FROM AUTHOR]

Background: One of the most prevalent malignancies in the world is lung adenocarcinoma (LUAD), with a large number of people dying from lung cancer each year. Anoikis has a crucial function in tumor metastasis, promoting cancer cell shedding and survival from the primary tumor site. However, the role of anoikis in LUAD is still unclear. Methods: The GeneCard database (https://www.genecards.org/) was utilized to obtain anoikis‐related genes with correlation greater than 0.4. Differential analysis was employed to acquire differential genes. Univariate, multifactorial Cox analyses and the least absolute shrinkage and selection operator were then utilized to capture genes connected to overall survival time. These genes were used to build prognostic models. The predictive model was analyzed and visualized. Survival analysis was conducted on the model and risk scores were calculated. The TCGA samples were split into groups of low and high risk depending on risk scores. A Gene Expression Omnibus database sample was used for external verification. Immunization estimates were performed using ESTIMATE, CiberSort and single sample gene set enrichment analysis. The connection between the prognostic gene model and immune cells was analyzed. Drug susceptibility prediction analysis was performed. The clinical information for samples was extracted and analyzed. Results: We selected six genes related to anoikis in LUAD to construct a prognosis model (CDC25C, ITPRIP, SLCO1B3, CDX2, CSPG4 and PIK3CG). Compared with cases of high‐risk scores, the overall survival of those with low risk was significantly elevated based on Kaplan–Meier survival analysis. Immune function analysis exhibited that different risk groups had different immune states. The results of ESTIMATE, CiberSort and single sample gene set enrichment analysis showed great gaps in immunization between patients in the two groups. The normogram of the risk score and the LUAD clinicopathological features was constructed. Principal component analysis showed that this model could effectively distinguish the two groups of LUAD patients. Conclusions: We integrated multiple anoikis‐related genes to build a prognostic model. This investigation demonstrates that anoikis‐related genes can be used as a stratification element for fine therapy of individuals with LUAD. [ABSTRACT FROM AUTHOR]