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Background: Mitochondria have always been considered too be closely related to the occurrence and development of malignant tumors. However, the bioinformatic analysis of mitochondria in lung adenocarcinoma (LUAD) has not been reported yet. Methods: In the present study, we constructed a novel and reliable algorithm, comprising a consensus cluster analysis and risk assessment model, to predict the survival outcomes and tumor immunity for patients with terminal LUAD. Results: Patients with LUAD were classified into three clusters, and patients in cluster 1 exhibited the best survival outcomes. The patients in cluster 3 had the highest expression of PDL1 (encoding programmed cell death 1 ligand 11) and HAVCR2 (encoding Hepatitis A virus cellular receptor 2), and the highest tumor mutation burden (TMB). In the risk assessment model, patients in the low-risk group tended to have a significantly better survival outcome. Furthermore, the risk score combined with stage could act as a reliable independent prognostic indicator for patients with LUAD. The prognostic signature is a novel and effective biomarker to select anti-tumor drugs. Low-risk patients tended to have a higher expression of CTLA4 (encoding cytotoxic T-lymphocyte associated protein 4) and HAVCR2. Moreover, patients in the high-risk group were more sensitive to Cisplatin, Docetaxel, Erlotinib, Gemcitabine, and Paclitaxel, while low-risk patients would probably benefit more from Gefitinib. Conclusion: We constructed a novel and reliable algorithm comprising a consensus cluster analysis and risk assessment model to predict survival outcomes, which functions as a reliable guideline for anti-tumor drug treatment for patients with terminal LUAD.
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BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) have large fluctuations in blood glucose (BG), abnormal metabolic function and low immunity to varying degrees, which increases the risk of malignant tumor diseases and affects the efficacy of tumor chemotherapy. Controlling hyperglycemia may have important therapeutic implications for cancer patients. AIM: To clarify the influence of BG fluctuations on chemotherapy efficacy and safety in T2DM patients complicated with lung carcinoma (LC). METHODS: The clinical data of 60 T2DM + LC patients who presented to the First Affiliated Hospital of Ningbo University between January 2019 and January 2021 were retrospectively analyzed. All patients underwent chemotherapy and were grouped as a control group (CG; normal BG fluctuation with a mean fluctuation < 3.9 mmol/L) and an observation group (OG; high BG fluctuation with a mean fluctuation ≥ 3.9 mmol/L) based on their BG fluctuations, with 30 cases each. BG-related indices, tumor markers, serum inflammatory cytokines and adverse reactions were comparatively analyzed. Pearson correlation analysis was performed to analyze the correlation between BG fluctuations and tumor markers. RESULTS: The fasting blood glucose and 2-hour postprandial blood glucose levels in the OG were notably elevated compared with those in the CG, together with markedly higher mean amplitude of glycemic excursions (MAGE), mean of daily differences, largest amplitude of glycemic excursions and standard deviation of blood glucose (P < 0.05). In addition, the OG exhibited evidently higher levels of carbohydrate antigen 19-9, carbohydrate antigen 125, carcinoembryonic antigen, neuron-specific enolase, cytokeratin 19, tumor necrosis factor-α, interleukin-6, and high-sensitivity C-reactive protein than the CG (P < 0.05). Pearson analysis revealed a positive association of MAGE with serum tumor markers. The incidence of adverse reactions was significantly higher in the OG than in the CG (P < 0.05). CONCLUSION: The greater the BG fluctuation in LC patients after chemotherapy, the more unfavorable the therapeutic effect of chemotherapy; the higher the level of tumor markers and inflammatory cytokines, the more adverse reactions the patient experiences.
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BACKGROUND: Spread through air spaces (STAS) is a pattern of invasion recently identified in non-small cell lung cancer (NSCLC), with a poor prognosis. However, the predictive impact of STAS in stage IB NSCLC is not well understood. This investigation aims to assess the prognostic influence of STAS in stage IB NSCLC. METHODS: We reviewed 130 resected stage IB NSCLC between 2010 and 2015. Beyond the central tumor edge, lung parenchymal air gaps containing cancer cells were identified as STAS. In order to estimate recurrence-free survival (RFS) and overall survival (OS), Cox models and Kaplan-Meier techniques were utilized. Logistic regression analysis was employed to define the factors influencing STAS. RESULTS: Of 130 patients, 72 (55.4%) had STAS. STAS was a significant prognosticator. Kaplan-Meier method showed that STAS-positive patients had a significantly lower OS and RFS than STAS-negative patients (5-year OS, 66.5% vs. 90.4%, p = 0.02; 5-year RFS, 59.5% vs. 89.7%, p = 0.004) In a semiquantitative assessment, the RFS and OS were shorter in survival analysis when STAS increased (5-year RFS, 89.7%, no STAS, 61.8%, low STAS, 57.2%, high STAS, p = 0.013; 5-year OS, 90.4%, no STAS, 78.3%, low STAS, 57.2%, high STAS, p = 0.002). The association between STAS and poor differentiation, adenocarcinoma, and vascular invasion (p value was <0.001, 0.047, and 0.041, respectively) was statistically significant. CONCLUSIONS: The STAS is an aggressive pathological feature. RFS and OS could be significantly reduced by STAS, while it also serves as an independent predictor.