Non-small cell lung cancer (NSCLC) is one of the most common types of cancer in the world and is an important cause for cancer death. Although the application of immunotherapy in recent years has greatly improved the prognosis of NSCLC, there are still huge challenges in the treatment of NSCLC. The immune microenvironment plays an important role in the process of NSCLC development, infiltration and metastasis, and they can interact and influence each other, forming a vicious circle. Notably, single-cell RNA sequencing enables high-resolution analysis of individual cells and is of great value in revealing cell types, cell evolution trajectories, molecular mechanisms of cell differentiation, and intercellular regulation within the immune microenvironment. Single-cell RNA sequencing is expected to uncover more promising immunotherapies. This article reviews the important researches and latest achievements of single-cell RNA sequencing in the immune microenvironment of NSCLC, and aims to explore the significance of applying single-cell RNA sequencing to analyze the immune microenvironment of NSCLC.
Lung cancer is a malignant tumor with the highest mortality worldwide, and its early diagnosis and evaluation have a crucial impact on the comprehensive treatment of patients. Early preoperative diagnosis of lung cancer depends on a variety of imaging and tumor marker indicators, but it cannot be accurately assessed due to its high false positive rate. Liquid biopsy biomarkers can detect circulating tumor cells and DNA in peripheral blood by non-invasive methods and are gradually becoming a powerful diagnostic tool in the field of precision medicine for tumors. This article reviews the research progress of liquid biopsy biomarkers and their combination with clinical imaging features in the early diagnosis of lung cancer.
ObjectiveTo establish a machine learning model based on computed tomography (CT) radiomics for preoperatively predicting invasive degree of lung ground-glass nodules (GGNs). MethodsWe retrospectively analyzed the clinical data of GGNs patients whose solid component less than 3 cm in the Department of Thoracic Surgery of Shanghai Pulmonary Hospital from March 2021 to July 2021 and the First Hospital of Lanzhou University from January 2019 to May 2022. The lesions were divided into pre-invasiveness and invasiveness according to postoperative pathological results, and the patients were randomly divided into a training set and a test set in a ratio of 7∶3. Radiomic features (1 317) were extracted from CT images of each patient, the max-relevance and min-redundancy (mRMR) was used to screen the top 100 features with the most relevant categories, least absolute shrinkage and selection operator (LASSO) was used to select radiomic features, and the support vector machine (SVM) classifier was used to establish the prediction model. We calculated the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, positive predictive value to evaluate the performance of the model, drawing calibration and decision curves of the prediction model to evaluate the accuracy and clinical benefit of the model, analyzed the performance in the training set and subgroups with different nodule diameters, and compared the prediction performance of this model with Mayo and Brock models. Two primary thoracic surgeons were required to evaluate the invasiveness of GGNs to investigate the clinical utility of the model. ResultsA total of 400 patients were divided into the training set (n=280) and the test set (n=120) according to the admission criteria. There were 267 females and 133 males with an average age of 52.4±12.7 years. Finally, 8 radiomic features were screened out from the training set data to build SVM model. The AUC, sensitivity and specificity of the model in the training and test sets were 0.91, 0.89, 0.75 and 0.86, 0.92, 0.60, respectively. The model showed good prediction performance in the training set 0-10 mm, 10-20 mm and the test set 0-10 mm, 10-20 mm subgroups, with AUC values of 0.82, 0.88, 0.84, 0.72, respectively. The AUC of SVM model was significantly better than that of Mayo model (0.73) and Brock model (0.73). With the help of this model, the AUC value, sensitivity, specificity and accuracy of thoracic surgeons A and B in distinguishing invasive or non-invasive adenocarcinoma were significantly improved. ConclusionThe SVM model based on radiomics is helpful to distinguish non-invasive lesions from invasive lesions, and has stable predictive performance for GGNs of different sizes and has better prediction performance than Mayo and Brock models. It can help clinicians to more accurately judge the invasiveness of GGNs, to make more appropriate diagnosis and treatment decisions, and achieve accurate treatment.
ObjectiveTo compare the short-term clinical effects of Da Vinci robot-assisted thoracic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) in the treatment of posterior mediastinal tumors, and to explore the advantages of RATS posterior mediastinal tumor resection. MethodsThe clinical data of patients who underwent posterior mediastinal tumors resection through the lateral chest approach admitted to the same medical group in the Department of Thoracic Surgery of the First Hospital of Lanzhou University between January 2019 to January 2023 were retrospectively analyzed. According to the different surgical methods, the patients were divided into a RATS group and a VATS group. The clinical data were compared between the two groups. ResultsA total of 85 patients were included in this study. There were 39 patients in the RATS group, including 25 females and 14 males, with an average age of 47.6±13.0 years, and 46 patients in the VATS group, including 14 males and 32 females, with an average age of 45.3±14.7 years. All patients completed the operation successfully. The hospitalization cost in the RATS group was significantly higher than that in the VATS group (P<0.001), and the white blood cell count and neutrophilic granulocyte percentage on the first day after operation in the RATS group were lower than those in the VATS group, and the differences were statistically significant (P<0.05). The operative time, intraoperative bleeding, postoperative hospital stay, white blood cell count and neutrophil percentage on the third postoperative day, visual analogue scale score on the first and third postoperative days, duration of analgesic pump use, postoperative 12 h oxygen saturation (no oxygen inhalation), postoperative down bed time, total thoracic drainage volume, duration of drainage tube retention, and postoperative complication rates were not statistically different between the two groups (P>0.05). There was no perioperative death, conversion to thoracotomy or serious perioperative complications in both groups. ConclusionRATS resection of posterior mediastinal tumor via lateral thoracic approach is safe and feasible, and its short-term effect is similar to that of VATS via lateral thoracic single-hole approach. It is worth further comparative study to explore its benefit and cost performance.