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.
ObjectiveTo explore the effectiveness of Ovol2 gene for epithelial-mesenchymal transition (EMT) to offer some theory evidences for the targeted therapy in lung adenocarcinoma.
MethodsA549 cells were treated with control and Ovol2 overexpressioned by lentivirus infection. Real-time PCR were performed to test the mRNA level of genes correlated to EMT. Western Blot was performed for protein level of the following makers:E-cadherin, N-cadherin, vimentin, ect. Moreover, we tested the migration and invasion ability of A549 cells by transwell and wound healing experiment.
ResultsAfter treated with Ovol2 overexpressed, the expression level of E-cadherin raised, while the expression level of N-cadherin, vimentin and Twist1 declined in both mRNA and protein expression level. The results of wound healing and transwell experiment indicated that the migration and invasion ability of A549 cells weakened.
ConclusionOverexpression of Ovol2 gene can suppress the distant metastasis ability and invasion ability of A549 cells by inhibiting the EMT.
ObjectiveTo investigate the value of serum soluble CD146 (sCD146) in determining acquired epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) resistance in lung adenocarcinoma.MethodsA total of 144 lung adenocarcinoma EGFR sensitive patients in People’s Hospital of Zhengzhou University diagnosed from January 2016 to December 2016 were recruited in the study. According to the different time of taking drugs, the patients were divided into a non-medication group (31 cases), a 1 to 3 month treatment group (25 cases), a 4 to 6 month treatment group (19 cases), a 7 to 12 month treatment group (25 cases), a drug-resistant group (24 cases), and a nonresistant group up to 1 year of treatment (20 cases). The serum levels of sCD146, carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) were measured by ELISA and chemiluminescence and compared between different period of medication. The relationship of serum sCD146 with tumor markers (CEA, NSE) and tumor related clinical parameters (age, gender, tumor stage, metastasis, tumor diameter, number of the lesions) were analyzed.ResultsThe serum sCD146 level was minimum in the non-medication group that did not receive pioglitazone treatment, highest in the 1 to 3 month treatment group (early treatment period), and declined with duration of medication until resistance occurred without significant difference (P>0.05). The level of sCD146 of the drug-resistant group was significantly lower than that of all nonresistant groups, with significant difference (allP<0.05), but still higher than that of the non-medication group (P<0.05). The serum sCD146 levels in the nonresistant patients with medication over 1 year and within 1 year were similar (P>0.05), and significantly higher than the non-medication group and drug-resistance group (allP<0.05). The serum CEA levels did not differ significantly between 6 groups (P>0.05). The serum NSE level of the 4 to 6 month treatment group was lower than that of the 7 to 12 month treatment group (P<0.05), but both in the normal reference range. The NSE levels did not differ in any other groups (P>0.05). Serum sCD146 was associated with metastasis (P<0.05), but not associated with serum CEA or NSE, nor with sex, age, tumor staging, tumor diameter or lesion number (allP>0.05).ConclusionsCD146 may be involved in the mechanism of TKI killing tumor cells and the mechanism of TKI resistance, and may be a serological marker for monitoring the efficacy of TKI and judging the resistance of TKI.
ObjectiveTo investigate the correlation between histological subtypes of invasive lung adenocarcinoma and epithelial growth factor receptor (EGFR) gene mutation, and to provide a reference for clinical prediction of EGFR gene mutation status.MethodsFrom October 2017 to May 2019, 102 patients with invasive lung adenocarcinoma were collected, including 58 males and 44 females aged 62 (31-84) years. Invasive lung adenocarcinoma was classified into different histological subtypes. Scorpion probe amplification block mutation system (ARMS) real-time PCR was used to detect the mutation of EGFR gene in adenocarcinoma specimens, and the relationship between invasive lung adenocarcinoma subtypes and EGFR mutation status was analyzed.ResultsIn 102 patients with invasive lung adenocarcinoma, EGFR gene mutations were detected in 68 patients, and the mutation rate was 66.7% (68/102). The mutation sites were mainly concentrated in the exons 19 and 21; the mutation rate was higher in female patients (34/44, 77.3%) and non-smokers (34/58, 58.6%). EGFR mutation was mostly caused by acinar-like invasive lung adenocarcinoma, and was rare in solid-type lung adenocarcinoma. The EGFR gene mutation rates in different subtypes of adenocarcinoma were statistically different (P<0.05).ConclusionThe EGFR mutation status is related to gender, smoking status and histological subtype of invasive lung adenocarcinoma. EGFR mutation rates are higher in female, non-smoking and acinar-like invasive lung adenocarcinoma patients, and are lower in patients with solid type lung adenocarcinoma.
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 select relatively specific biomarkers in serum from lung adenocarcinoma patients using surface-enhanced laser desorption ionization time of flight mass spectrometry （SELDI-TOF-MS） Protein Chip technology, and study the follow-up results of postoperative serum proteomic patterns. Methods Serum samples from 71 lung adenocarcinoma patients. 71 healthy volunteers with matched gender, age and history of smoking were analyzed by using weak cation exchange 2（WCX2） Protein Chip to select potentially biomarkers. Seventy-one patients were followed-up till 9 months after surgery. Compare the serum proteomic patterns 3,6 and 9 months after surgery. Results Five highly expressed potential biomarkers were identified with the relative molecular weights of 4 047.79, 4 203. 99, 4 959. 81, 5 329. 30 and 7 760. 12 Da. The postoperative serum proteomic patterns changed among individuals, and correlated with patients＇ clinical stage. Conclusions SELDI-TOF-MS Protein Chip technology is a quick, easy, convenient, and high-throughout analyzing method capable of selecting relatively specific, potential biomarkers from the serum of lung adenocarcinoma patients and may have attractive clinical value.
ObjectiveTo compare the recent efficiency and toxicity reactions of pemetrexed plus cisplatin and paclitaxel plus cisplatin for advanced lung adenocarcinoma.
MethodsOne hundred and twenty-four patients with advanced lung adenocarcinoma treated in our hospital between January 2009 and December 2012 were divided into pemetrexed plus cisplatin group (group PP, n=63) and paclitaxel plus cisplatin group (group TP, n=61). The effect was evaluated after two courses of treatment, and the toxicity reactions were evaluated every course.
ResultsThe objective response rate, disease control rate and progression-free survival in group PP and TP were respectively 58.7% vs 37.7%, 74.6% vs 52.5%, and 6.1 months vs 4.5 months, with significant differences (P<0.05). The incidence of nausea and vomiting, and white blood cell decrease (neutropenia) in group PP were significantly lower than that in group TP (χ2=16.164, P<0.001; χ2=9.469, P=0.002). There were no significant differences in incidence of thrombocytopenia, anemia and hepatic function damage (χ2=0.098, P=0.755; χ2=0.267, P=0.606; χ2=0.006, P=0.973).
ConclusionPemetrexed plus cisplatin shows obviously superior effects and fewer side effects on advanced lung adenocarcinoma compared with paclitaxel plus cisplatin regime.
Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.
Objective To explore the role of cyclin B1 (CCNB1), cyclin B2 (CCNB2) and cyclin dependent kinase 1 (CDK1) in lung adenocarcinoma (LUAD) using bioinformatic data. Methods First, RNA expression data were downloaded from two datasets in Gene Expression Omnibus (GEO), and DESeq2 software was used to identify deferentially expressed genes (DEGs). Subsequent analyses were conducted based on the results of these DEGs: protein-protein interaction (PPI) network was constructed with STRING database; the modules in PPI network were analyzed by Molecular Complex Detection software, and the most significant modules were selected, the genes included in these modules were the hub genes; high-throughput RNA sequencing data from other databases were used to verify the expression of these hub genes to confirm whether they were DEGs; survival curve analyses of the confirmed DEGs were conducted to select genes that had significant influence on the survival of LUAD; the expression of these hub genes in different stages of LUAD were also analyzed. Then, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for these selected hub genes using KOBAS database. MuTarget tool was used to analyze the correlations between the expression of these selected hub genes and gene mutation status in LUAD. The potential value of these hub genes in the treatment of LUAD was explored based on the drug information in GDSC database. Finally, immunohistochemical data from Human Protein Atlas (HPA) database were used to verify the expression of these hub genes in LUAD again. Results According to the expression data in GEO, 594 up-regulated genes and 651 down-regulated genes were identified (P<0.05), among which 30 hub genes were selected for subsequent analyses. The RNA high-throughput sequencing data of other databases verified that 18 genes were DEGs, among which 8 hub genes had significant impact on disease-free survival in LUAD (P<0.05). Moreover, the 8 genes were differentially expressed in different stages of LUAD, which were higher in the middle and late stage of LUAD. Among the 8 genes. CCNB1, CCNB2 and CDK1 were significantly enriched in the cell cycle pathway. The expression of CCNB1, CCNB2 and CDK1 in LUAD was closely related to the TP53 mutation status. In addition, CDK1 was associated with four drugs, revealing the potential value of CDK1 in the treatment of LUAD. Finally, immunohistochemical data from HPA database verified that CCNB1, CCNB2 and CDK1 were highly expressed in LUAD in the protein level. Conclusion Overexpression of CCNB1, CCNB2 and CDK1 are associated with poor prognosis of LUAD, indicating that the three genes may be prognostic biomarkers of LUAD and CDK1 is a potential therapeutic target for LUAD.
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.
