Objective
To investigate the clinical and pathological characteristics, prognosis and treatment strategies of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA).
Methods
We retrospectively analyzed the clinical data of 489 patients with AIS and MIA in our hospital from January 2007 to August 2015. There were 122 males and 367 females with an average age of 26–78 (51±9) years. According to the pathological types, they were divided into the AIS group (246 patients) and the MIA group (243 patients). In the AIS group, there were 60 males and 186 females with an average age of 50±7 years. In the MIA group, there were 62 males and 181 females with an average age of 54±5 years. The clinicopathological features, surgical methods and prognosis of the two groups were compared.
Results
There were significant differences in age, value of carcino-embryonic antigen (CEA), nodule shape and nodule size between the AIS and MIA groups (P<0.05). AIS patients were mostly under the age of 60 years with the value of CEA in the normal range which often appeared as pure ground-glass opacity lung nodules <1 cm in diameter on the CT scan. MIA often appeared as mixed ground-glass nodules <1.5 cm in diameter, accompanied by bronchiectasis and pleural indentation. The 5-year disease-free survival rate of the AIS and MIA groups reached 100%, and there was no statistical difference in the prognosis between the two groups after subtotal lobectomy (pulmonary resection and wedge resection) and lobectomy, systematic lymph node dissection and mediastinal lymph node sampling.
Conclusion
The analysis of preoperative clinical and imaging features can predict the AIS and MIA and provide individualized surgery and postoperative treatment program.
Objective To analyze the expression of H2A histone family, member X (H2AFX) gene in lung adenocarcinoma and its influence on prognosis. Methods We analyzed the expression level of H2AFX gene in the tumor tissues (497 cases) and normal adjacent tissues (54 cases) of lung adenocarcinoma patients via The Cancer Genome Atlas. The patients were divided into high expression group and low expression group according to the expression level of H2AFX gene in lung adenocarcinoma samples. The relationship between H2AFX and clinicopathological features of patients was analyzed through logistic regression. Kaplan-Meier survival curve and log-rank test were used to study the correlation between H2AFX expression and the prognosis of lung adenocarcinoma patients. Univariate and multiple Cox regression analyses were performed to determine the prognostic significance of H2AFX expression in lung adenocarcinoma patients. The research also covered H2AFX-related pathways of genes in the development of lung adenocarcinoma with gene set enrichment analysis (GSEA). Results The H2AFX expression was higher in lung adenocarcinoma tissues than that in normal adjacent tissues (P<0.001). Besides, it was significantly correlated with age (P<0.001), T staging (P=0.007), and N staging (P=0.010), but had little to do with M staging or gender (P>0.05). Kaplan-Meier survival curve and log-rank test showed that the survival rate of patients with high H2AFX expression was vastly lower than that of patients with low H2AFX expression (P<0.001). Multiple Cox regression analysis demonstrated that H2AFX could be an independent prognostic factor for lung adenocarcinoma [hazard ratio=1.41, 95% confidence interval (1.11, 1.78), P=0.004]. The results of GSEA displayed that H2AFX was involved in cell cycle, homologous recombination, DNA replication, base excision and repair, spliceosome, mismatch repair, p53 signaling pathway, nucleotide excision and repair, RNA degradation, RNA polymerase, and other pathways. Conclusions The expression of H2AFX gene is high in lung adenocarcinoma, and closely connected to the prognosis, occurrence, and evolution of lung adenocarcinoma. This gene can be one of the new molecular markers and therapeutic targets for lung adenocarcinoma.
ObjectiveTo investigate the CT signs and clinicopathological features of peripheral cavitary lung adenocarcinoma with the largest diameter less than or equal to 3 cm.Methods From January 2015 to December 2017, the CT signs and clinicopathological fertures of 51 patients with ≤3 cm peripheral cavitary lung adenocarcinoma diagnosed by chest CT and surgical pathology were retrospectively analyzed. Furthermore, CT signs and clinicopathological features of thick-walled cavitary lung adenocarcinoma and thin-walled cavitary lung adenocarcinoma were compared. There were 29 males and 22 females at age of 62 (56, 67) years.ResultsThere were 27 thick-walled cavitary lung adenocarcinoma and 24 thin-walled cavitary lung adenocarcinoma. Thick-walled cavitary adenocarcinoma had greater SUVmax [6.5 (3.7, 9.7) vs. 2.2 (1.4, 3.8), P=0.019], larger cavity wall thickness (11.8±4.6 mm vs. 7.6±3.7 mm, P=0.001), larger tumor tissue size [2.1 (1.7, 2.8) cm vs. 1.6 (1.2, 2.0) cm, P=0.006], and more solid nodules (17 patients vs. 8 patients, P=0.035). Thin-walled cavitary adenocarcinoma had more smoking history (12 patients vs. 6 patients, P=0.038), larger cavity size [12.3 (9.2, 16.6) mm vs. 4.4 (2.8, 7.1) mm, P=0.000], and larger proportion of cavities [0.30 (0.19, 0.37) vs. 0.03 (0.01, 0.09), P=0.000]. On CT signs, there were more features of irregular inner wall (19 patients vs. 6 patients, P=0.000), intra-cystic separation (16 patients vs. 6 patients, P=0.001) and vessels through the cystic cavity (10 patients vs. 1 patient, P=0.001) in thin-walled caviraty lung adenocarcinoma.ConclusionPeripheral cavitary lung adenocarcinoma of ≤3 cm on chest CT has characteristic manifestations in clinical, imaging and pathology, and there is a statistical difference between thick-walled cavitary lung adenocarcinoma and thin-walled cavitary lung adenocarcinoma.
Objective To evaluate the correlation between cyclin B1 (CCNB1) gene expression and the prognosis of lung adenocarcinoma. Methods Oncomine, STRING, Human Protein Atlas, The Cancer Genome Atlas and other databases as well as Kaplan-Meier method, Cox regression, receiver operating characteristic (ROC) curve and Spearman correlation analysis were used to verify the effect of CCNB1 on patients with lung adenocarcinoma. Results CCNB1 was highly expressed in lung adenocarcinoma, and the high expression was correlated with T stage (P=0.001), N stage (P<0.001), pathological stage (P<0.001) and gender (P=0.008). Univariate Cox regression analysis showed that the expression of CCNB1, T stage, N stage, M stage and pathological stage were the factors affecting the overall survival rate of patients with lung adenocarcinoma (P<0.05); multivariate Cox regression analysis showed that the expression of CCNB1 and T stage were independent risk factors for overall survival of patients with lung adenocarcinoma (P<0.05). Kaplan-Meier analysis showed that high expression of CCNB1 was associated with shorter overall survival [hazard ratio (HR)=1.60, 95% confidence interval (CI) (1.20, 2.14), P=0.002], disease-specific survival [HR=1.68, 95%CI (1.16, 2.44), P=0.006] and progression-free interval [HR=1.42, 95%CI (1.09, 1.85), P=0.009]. The ROC curve showed that CCNB1 might be a potential diagnostic molecule for lung adenocarcinoma [area under the curve=0.980, 95%CI (0.967, 0.993)]. Spearman correlation analysis showed that CCNB1 expression was positively correlated with the infiltration of T helper cells 2 (rs=0.805, P<0.001) and T helper cells (rs=0.103, P=0.017), and negatively correlated with the infiltration of natural killer cells (rs=?0.195, P<0.001), macrophages (rs=?0.134, P=0.002), and T cells (rs=?0.092, P=0.033). Conclusion CCNB1 is highly expressed in lung adenocarcinoma compared with normal tissues, which is related to poor prognosis and may provide a potential therapeutic target for patients with lung adenocarcinoma.
ObjectiveTo explore the accuracy of machine learning algorithms based on SHOX2 and RASSF1A methylation levels in predicting early-stage lung adenocarcinoma pathological types. MethodsA retrospective analysis was conducted on formalin-fixed paraffin-embedded (FFPE) specimens from patients who underwent lung tumor resection surgery at Affiliated Hospital of Nantong University from January 2021 to January 2023. Based on the pathological classification of the tumors, patients were divided into three groups: a benign tumor/adenocarcinoma in situ (BT/AIS) group, a minimally invasive adenocarcinoma (MIA) group, and an invasive adenocarcinoma (IA) group. The methylation levels of SHOX2 and RASSF1A in FFPE specimens were measured using the LungMe kit through methylation-specific PCR (MS-PCR). Using the methylation levels of SHOX2 and RASSF1A as predictive variables, various machine learning algorithms (including logistic regression, XGBoost, random forest, and naive Bayes) were employed to predict different lung adenocarcinoma pathological types. ResultsA total of 272 patients were included. The average ages of patients in the BT/AIS, MIA, and IA groups were 57.97, 61.31, and 63.84 years, respectively. The proportions of female patients were 55.38%, 61.11%, and 61.36%, respectively. In the early-stage lung adenocarcinoma prediction model established based on SHOX2 and RASSF1A methylation levels, the random forest and XGBoost models performed well in predicting each pathological type. The C-statistics of the random forest model for the BT/AIS, MIA, and IA groups were 0.71, 0.72, and 0.78, respectively. The C-statistics of the XGBoost model for the BT/AIS, MIA, and IA groups were 0.70, 0.75, and 0.77, respectively. The naive Bayes model only showed robust performance in the IA group, with a C-statistic of 0.73, indicating some predictive ability. The logistic regression model performed the worst among all groups, showing no predictive ability for any group. Through decision curve analysis, the random forest model demonstrated higher net benefit in predicting BT/AIS and MIA pathological types, indicating its potential value in clinical application. ConclusionMachine learning algorithms based on SHOX2 and RASSF1A methylation levels have high accuracy in predicting early-stage lung adenocarcinoma pathological types.
Objective To explore the molecular mechanism of LINC00626 regulating malignant progression of lung adenocarcinoma metastasis through JAK1/STAT3/KHSRP axis. Methods Quantitative real-time polymerase chain polymerase chain reaction was used to detect the expression of LINC00626 and KHSRP mRNA in human non-small-cell lung carcinoma cell lines (A549, H1299, H1975, H1437), human normal bronchial epithelial cell line (16HBE) and 144 lung adenocarcinoma tissues. The knockdown LINC00626 lentivirus and the control lentivirus were transferred into H1299 and H1437 cells, and named as sh-LINC00626 group (silencing of LINC00626 by transfecting short hairpin RNA lentiviral vector and sh-NC Group negative control by transfecting short hairpin RNA lentiviral). The overexpressed LINC00626 lentivirus and the control lentivirus were transferred into A549 and H1975 cells and named as LINC00626 group and Vector group. KHSRP vector on the basis of silencing LINC00626 and blank vector on the basis of silencing LINC00626 were added in H1437 cells. Cell counting kit-8 assay and Transwell migration/invasion assay were used to detect cell proliferation, migration and invasion. The expression levels of JAK/STAT and KHSRP in stably transfected cells were detected by Western blot. The effect of LINC00626 in vivo was studied in nude mice. Nuclear-cytoplasmic separation and RNA fluorescence in situ hybridization assay are used to predict the subcellular localization of LINC00626 and KHSRP. RNA pull down and mass spectrometry analysis were used to identify LINC00626 binding proteins. Results The expression levels of LINC00626 and KHSRP in non-small-cell lung carcinoma cell lines were significantly higher than those in normal human bronchial epithelial cells. LINC00626 and KHSRP were highly expressed in lung adenocarcinoma. Compared with the control group, the cell proliferation rate, colony formation, cell migration and invasion of H1437 cells were significantly decreased in knockdown group, while the reverse was true for over-expression. LINC00626 and KHSRP were located in the nucleus. LINC00626 directly binded to the KHSRP protein. Compared with the control group, H1437 cells transfected with knockdown LINC00626 and KHSRP significantly increased cell proliferation rate, cell migration, number of invasions. Compared with the control group, knockdown group showed a significant decrease in tumor volume and weight, cell proliferation rate and proliferation index, and the number of lung metastases. While the overexpression group showed an opposite effect, there were significant differences among the groups (P<0.01). The expression of JAK1 and STAT3 mRNA and protein in sh-LINC00626 group was lower than that in sh-NC Group (P<0.05), and the expression of JAK1 and STAT3 mRNA and protein in sh-LINC00626 group was higher than that in Vector group (P<0.05). Conclusion LINC00626 promotes malignant progression of lung adenocarcinoma metastasis through JAK1/STAT3/KHSRP signaling axis.
Lung adenocarcinoma has become the most common type of lung cancer. According to the 2015 World Health Organization histological classification of lung cancer, invasive lung adenocarcinoma can be divided into 5 subtypes: lepidic, acinar, papillary, solid, and micropapillary. Relevant studies have shown that the local lobectomy or sublobectomy is sufficient for early lepidic predominant adenocarcinoma, while lobectomy should be recommended for tumors containing micropapillary and solid ingredients (≥5%). Currently, the percentage of micropapillary and solid components diagnosed by frozen pathological examination is 65.7%, and the accuracy of diagnosis is limited. Therefore, to improve the accuracy of diagnosis, it is necessary to seek new methods and techniques. This paper summarized the characteristics and rapid diagnosis tools of early lung adenocarcinoma subtypes.
Lung cancer is one of the malignant tumors with the greatest threat to human health, and studies have shown that some genes play an important regulatory role in the occurrence and development of lung cancer. In this paper, a LightGBM ensemble learning method is proposed to construct a prognostic model based on immune relate gene (IRG) profile data and clinical data to predict the prognostic survival rate of lung adenocarcinoma patients. First, this method used the Limma package for differential gene expression, used CoxPH regression analysis to screen the IRG to prognosis, and then used XGBoost algorithm to score the importance of the IRG features. Finally, the LASSO regression analysis was used to select IRG that could be used to construct a prognostic model, and a total of 17 IRG features were obtained that could be used to construct model. LightGBM was trained according to the IRG screened. The K-means algorithm was used to divide the patients into three groups, and the area under curve (AUC) of receiver operating characteristic (ROC) of the model output showed that the accuracy of the model in predicting the survival rates of the three groups of patients was 96%, 98% and 96%, respectively. The experimental results show that the model proposed in this paper can divide patients with lung adenocarcinoma into three groups [5-year survival rate higher than 65% (group 1), lower than 65% but higher than 30% (group 2) and lower than 30% (group 3)] and can accurately predict the 5-year survival rate of lung adenocarcinoma patients.
Objective To explore the correlation between lymph node metastasis and clinicopathological features of lung adenocarcinoma with diameter≤3 cm. Methods The clinicopathologic data of the patients with lung adenocarcinoma≤3 cm in diameter were retrospectively analyzed. The relationship between lymph node metastasis and age, gender, smoking history, pathological subtype, tumor location, tumor diameter, pleural invasion, vascular invasion and other factors was analyzed. The risk factors of lymph node metastasis were analyzed by univariate and multivariate logistic regression. Results Finally 1 718 patients were collected, including 697 males and 1 021 females with an average age of 58.89±9.85 years. The total lymph node metastasis rate was 12.9%, among whom 452 patients of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) did not have lymph node metastasis, and the lymph node metastasis rate of invasive lung adenocarcinoma was 17.5%. Multivariate analysis showed that tumor diameter, micropapillary subtype, solid subtype, micropapillary component, solid component, vascular invasion and pleural invasion were independent risk factors for lymph node metastasis of invasive lung adenocarcinoma with diameter≤3 cm (P<0.05). While age, lepidic subtype and lepidic component were independent protective factors for lymph node metastasis (P<0.05). Conclusion Clinicopathological features can help predict lymph node metastasis of lung adenocarcinoma with diameter≤3 cm.
ObjectiveTo investigate the risk factors for lymph node metastasis in resectable lung adenocarcinoma by combining spatial location, clinical, and imaging features, and to construct a lymph node metastasis prediction model. MethodsA retrospective study on patients who underwent chest computed tomography (CT) at the First Affiliated Hospital of Nanjing Medical University from June 2016 to June 2020 and were surgically confirmed to have invasive lung adenocarcinoma with or without lymph node metastasis was conducted. Patients were divided into a positive group and a negative group based on the presence or absence of lymph node metastasis. Clinical and imaging data of the patients were collected, and the independent risk factors for lymph node metastasis in resectable lung adenocarcinoma were analyzed using univariate and multivariate logistic regression. A combined spatial location-clinical-imaging feature prediction model for lymph node metastasis was established and compared with the traditional lymph node metastasis prediction model that does not include spatial location features. ResultsA total of 611 patients were included, with 333 in the positive group, including 172 males and 161 females, with an average age of (58.9±9.7) years; and 278 in the negative group, including 127 males and 151 females, with an average age of (60.1±11.4) years. Univariate and multivariate logistic regression analyses showed that the spatial relationship of the lesion to the lung hilum, nodule type, pleural changes, and serum carcinoembryonic antigen (CEA) levels were independent risk factors for lymph node metastasis. Based on this, the combined spatial location-clinical-imaging feature prediction model had a sensitivity of 91.67%, specificity of 74.05%, accuracy of 87.88%, and area under the curve (AUC) of 0.885. The traditional lymph node metastasis prediction model, which did not include spatial location features, had a sensitivity of 76.40%, specificity of 72.10%, accuracy of 53.86%, and AUC of 0.827. The difference in AUC between the two prediction methods was statistically significant (P=0.026). Compared with the traditional prediction model, the predictive performance of the combined spatial location-clinical-imaging feature prediction model was significantly improved. ConclusionIn patients with resectable lung adenocarcinoma, those with basal spatial location, solid density, pleural changes with wide base depression, and elevated serum CEA levels have a higher risk of lymph node metastasis.
