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.
Lung ground glass opacity (GGO), which is associated with the pathology of the lung adenocarcinoma, is drawing more and more attention with the increased detection rate. However, it is still in the research stage for the imaging interpretation of GGO lesions. In this paper, we reviewed and analyzed the new classification of lung adenocarcinoma corresponding to the interpretation of GGO imaging feature, which emphasizes on how to determine the GGO lesions comprehensively and quantitative determination of the invasive extent of GGO.
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.
ObjectiveTo evaluate the clinical manifestation, radiological, pathological features and treatment of organizing pneumonia (OP) induced by aerosolized recombinant super compound interferon (rSIFN-co). MethodsClinical features and related laboratory examinations of a patient with OP developing after initiation of rSIFN-co for treatment of lung adenocarcinoma were analyzed, and the relevant literature was reviewed. ResultsA 48-year-old man developed cough, fevers, shortness of breath and weight loss, shortly half a month after initiation of therapy with rSIFN-co for lung adenocarcinoma. Chest high resolution computerized tomography (HRCT) showed multiple lung infection diseases. However, the anti-infection treatment was invalid. Lung tissue biopsy by bronchofibroscope was consistent with OP. After discontinuation of rSIFN-co and receiving pulse corticosteroid therapy followed by oral methylprednisolone, the pneumonic symptoms and chest manifestations markedly improved. After eight-month follow-up, the patient's condition was stable. The relative literature screening from Pubmed and Wanfangdata was implemented, but there was no report about OP caused by aerosolized rSIFN-co for lung adenocarcinoma. ConclusionThis report suggests that treatment with aerosolized rSIFN-co for lung adenocarcinoma may induce OP, a rare complication, and clinicians should have vigilance on it.
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.
Recently, an editorial in Cell Research illuminated the concept of the curative time window for lung adenocarcinoma. Within this window, patients could achieve 100.0% 5-year or 10-year post-operative recurrence-free survival. Adenocarcinoma in situ and minimally invasive adenocarcinoma in pathology, as well as pure ground-glass opacity in radiology could be regarded as the curative time window. However, not all the patients can catch this time window. A precise understanding of this concept helps elevate the curative rate for lung cancer patients and mitigate the risks of overdiagnosis and overtreatment. This article delves into the accurate comprehension of the surgical curative time window for lung adenocarcinoma, proposed a clinical strategy for early-stage lung adenocarcinoma, and introduced clinical procedures comprising of this time window. It offers fresh insights into the timing of surgical interventions for 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.
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.
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.
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.