Objective The management of pulmonary nodules is a common clinical problem, and this study constructed a nomogram model based on FUT7 methylation combined with CT imaging features to predict the risk of adenocarcinoma in patients with pulmonary nodules. Methods The clinical data of 219 patients with pulmonary nodules diagnosed by histopathology at the First Affiliated Hospital of Zhengzhou University from 2021 to 2022 were retrospectively analyzed. The FUT7 methylation level in peripheral blood were detected, and the patients were randomly divided into training set (n=154) and validation set (n=65) according to proportion of 7:3. They were divided into a lung adenocarcinoma group and a benign nodule group according to pathological results. Single-factor analysis and multi-factor logistic regression analysis were used to construct a prediction model in the training set and verified in the validation set. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model, the calibration curve was used to evaluate the consistency of the model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The applicability of the model was further evaluated in the subgroup of high-risk CT signs (located in the upper lobe, vascular sign, and pleural sign). Results Multivariate logistic regression analysis showed that female, age, FUT7_CpG_4, FUT7_CpG_6, sub-solid nodules, lobular sign and burr sign were independent risk factors for lung adenocarcinoma (P<0.05). A column-line graph prediction model was constructed based on the results of the multifactorial analysis, and the area under the ROC curve was 0.925 (95%CI 0.877 - 0.972 ), and the maximum approximate entry index corresponded to a critical value of 0.562, at which time the sensitivity was 89.25%, the specificity was 86.89%, the positive predictive value was 91.21%, and the negative predictive value was 84.13%. The calibration plot predicted the risk of adenocarcinoma of pulmonary nodules was highly consistent with the risk of actual occurrence. The DCA curve showed a good clinical net benefit value when the threshold probability of the model was 0.02 - 0.80, which showed a good clinical net benefit value. In the upper lobe, vascular sign and pleural sign groups, the area under the ROC curve was 0.903 (95%CI 0.847 - 0.959), 0.897 (95%CI 0.848 - 0.945), and 0.894 (95%CI 0.831 - 0.956). Conclusions This study developed a nomogram model to predict the risk of lung adenocarcinoma in patients with pulmonary nodules. The nomogram has high predictive performance and clinical application value, and can provide a theoretical basis for the diagnosis and subsequent clinical management of pulmonary nodules.
ObjectiveTo evaluate the application value of three-dimensional (3D) reconstruction in preoperative surgical diagnosis of new classification criteria for lung adenocarcinoma, which is helpful to develop a deep learning model of artificial intelligence in the auxiliary diagnosis and treatment of lung cancer.MethodsThe clinical data of 173 patients with ground-glass lung nodules with a diameter of ≤2 cm, who were admitted from October 2018 to June 2020 in our hospital were retrospectively analyzed. Among them, 55 were males and 118 were females with a median age of 61 (28-82) years. Pulmonary nodules in different parts of the same patient were treated as independent events, and a total of 181 subjects were included. According to the new classification criteria of pathological types, they were divided into pre-invasive lesions (atypical adenomatous hyperplasia and and adenocarcinoma in situ), minimally invasive adenocarcinoma and invasive adenocarcinoma. The relationship between 3D reconstruction parameters and different pathological subtypes of lung adenocarcinoma, and their diagnostic values were analyzed by multiplanar reconstruction and volume reconstruction techniques.ResultsIn different pathological types of lung adenocarcinoma, the diameter of lung nodules (P<0.001), average CT value (P<0.001), consolidation/tumor ratio (CTR, P<0.001), type of nodules (P<0.001), nodular morphology (P<0.001), pleural indenlation sign (P<0.001), air bronchogram sign (P=0.010), vascular access inside the nodule (P=0.005), TNM staging (P<0.001) were significantly different, while nodule growth sites were not (P=0.054). At the same time, it was also found that with the increased invasiveness of different pathological subtypes of lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. Meanwhile, nodule diameter and the average CT value or CTR were independent risk factors for malignant degree of lung adenocarcinoma.ConclusionImaging signs of lung adenocarcinoma in 3D reconstruction, including nodule diameter, the average CT value, CTR, shape, type, vascular access conditions, air bronchogram sign, pleural indenlation sign, play an important role in the diagnosis of lung adenocarcinoma subtype and can provide guidance for personalized therapy to patients in clinics.
ObjectiveTo investigate the predictive value of thyroid transcription factor-1 (TTF-1) in the treatment of advanced lung adenocarcinoma with different chemotherapy regimens.MethodsA total of 126 patients with advanced lung cancer were divided into three groups according to the chemotherapy regimen, namely a pemetrexed+nedaplatin group (PEM+NDP group), a pemetrexed+cisplatin/carboplatin group (PEM+DDP/CBP group) and a third-generation (3G) chemotherapy+cisplatin/carboplatin group (3G agent+DDP/CBP group). The predictive value of TTF-1 in the above three treatment regimens was analyzed. The patients were followed up by telephone or outpatient visit until April 2017.ResultsThere were no significant differences in disease control rate or objective response rate between the three different chemotherapy regimens (all P>0.05). The survival rate of PEM+NDP group was significantly higher than that of PEM+DDP/CBP group and 3G agent+DDP/CBP group (9.68%vs. 5.56% and 6.80%, both P<0.05). ECOG score and brain metastasis were independent risk factors for the prognosis of chemotherapy regimens. TTF-1 was an independent risk factor for PEM+NDP therapy.ConclusionTTF-1 is an independent risk factor for PEM+NDP chemotherapy, but not for 3G agent + DDP/CBP or PEM+DDP/CBP regimens.
ObjectiveTo assess the specific clinicopathological characteristics as well as prognostic value of prognostic significance of spread through air spaces (STAS) in lung adenocarcinoma.MethodsWe systematically searched the databases of PubMed, EMbase and Web of Science databases from their date of inception to March 2019. The quality of the included literature was assessed by the Newcastle-Ottawa scale (NOS). The NOS of the study higher than 6 points was considered as high quality. Software of Stata 12.0 was used for meta-analysis.ResultsTwenty retrospective cohort studies involved with totally 6 225 patients were included. Quality of included studies was high with NOS score equal or higher than 6 points. STAS was associated with male sex, ever smoking history, abnormal carcino-embryonic antigen (CEA) level, air bronchogram negative, anaplasticlymphoma kinase (ALK) arrangement positive, epidermal growth factor receptor (EGFR) mutation positive, advanced pathological tumor stage and more invasive pathological adenocarcinoma subtypes. The presence of STAS indicated significantly poor recurrence free survival (RFS) (HR=1.960, 95%CI 1.718-2.237, P<0.001) as well as poor overall survival (OS) (HR=1.891, 95%CI 1.389-2.574, P<0.001). Further subgroup analyses showed that exhibiting tumor size including diameter less than 2 cm (HR=2.344, 95%CI 1.703-3.225, P<0.001) and diameter over 2 cm (HR=2.571, 95%CI 1.559-4.238, P<0.001), resection type including lobectomy (HR=1.636, 95%CI 1.258-2.127, P<0.001) and sublobar resection (HR=3.549, 95%CI 2.092-6.021, P<0.001) in stageⅠ adenocarcinoma suggested that STAS had a bad effect on RFS.ConclusionPresence of STAS is associated with more aggressive clinicopathological features and independently associated with worse RFS and OS in lung adenocarcinoma. STAS positive has a negative effect on RFS whatever the tumor size (including the diameter<2 cm or >2 cm) and resection types in stageⅠ adenocarcinoma.
Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. ConclusionThe model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.
Objective To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. MethodsThe patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.
Objective To investigate the molecular mechanisms by which the long non-coding RNA (lncRNA) MIR223HG affects the proliferation, migration and apoptosis of lung adenocarcinoma cells. MethodsDNA damaging agent Zeocin was used to treat human embryo lung cell (MRC-5) and lung cancer cell (A549 and H1299), and the expression of MIR223HG was tested by quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Moreover, the ataxia-telangiectasia mutated (ATM) protein and ATM pathway downstream factor Cell cycle checkpoint kinase 2 (Chk2), p53 tumor suppressor protein (p53) in the lung cancer cell (A549 and H1299) with Zeocin were also tested by qRT-PCR. Cell transfection and Transwell migration assay, colony formation assays, apoptosis assays were performed to verify the role of ATM in the expression of MIR223HG in lung adenocarcinoma. ResultsThe expression of MIR223HG was reduced markedly in the lung cancer cells (A549 and H1299) compared with human embryo lung cell (MRC-5) after treated with Zeocin. ATM protein and its downstream factors Chk2, p53 involved in the process, and ATM regulated the expression of MIR223HG in the lung cancer cells with Zeocin. Futhermore, ATM joined in the processes that MIR223HG regulated the lung cancer cells proliferation, migration and apoptosis. Conclusions The expression of MIR223HG is related to the DNA damage response in the lung cancer, and MIR223HG regulates lung cancer cells proliferation, migration and apoptosis by ATM/Chk2/p53 pathway. MIR223HG may be a potential therapeutic target for lung adenocarcinoma treatment.
Survivin-D53A (SVV-D53A) is a dominant-negative mutant survivin, which represents a potential promising target for cancer gene therapy. The present study was designed to determine whether SVV-D53A plasmid encapsuled by DOTAP: Chol liposome would have the anti-tumor activity against SPC-A1 lung adenocarcinoma, and to detect the possible mechanisms. In our experiment, SPC-A1 cells were transfected in vitro with SVV-D53A plasmid and examined for protein expression by Western blot, then flow cytometric analysis was used to detect apoptosis. SPC-A1 lung adenocarcinoma xenografts were established in vivo in the nude mice, which received the i.v. administrations of SVV-D53A plasmid/liposome complexes. After mice were sacrificed, the paraffin-embedded tumor tissue sections were used for proliferating cell nuclear antigen (PCNA) expression and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)assay. Compared with the control group, the mice treated with SVV-D53A plasmid had an obviously reduced tumor volume, with high level of apoptosis and decreased cell proliferation in tumor tissue. The research results proved that the administration of SVV-D53A plasmid resulted in significant inhibition of SPC-A1 cells both in vitro and in vivo. The functional mechanism is that the anti-tumor response causes and induces tumor cell apoptosis.
ObjectiveA competing endogenous RNA (ceRNA) regulatory network associated with long non-coding RNA (lncRNA) specific for lung adenocarcinoma (LUAD) was constructed based on bioinformatics methods, and the functional mechanism of actinfilament-associated protein 1-antisense RNA1 (AFAP1-AS1) in LUAD was analyzed, in order to provide a new direction for the study of LUAD therapeutic targets. MethodsThe gene chip of LUAD was downloaded from the Gene Expression Omnibus (GEO), and lncRNA and mRNA with differential expression between LUAD and normal tissues were screened using GEO2R online software, and their target genes were predicted by online databases to construct ceRNA networks and perform enrichment analysis. In cell experiments, AFAP1-AS1 was genetically knocked down and siRNA was constructed and transfected into LUAD cells A549 by cell transfection. CCK8, transwell, scratch assay and flow cytometry were used to detect the ability of cells to proliferate, invade, migrate and apoptosis. ResultsA total of 6 differentially expressed lncRNA and 494 differentially expressed mRNA were identified in the microarray of LUAD. The ceRNA network involved a total of 6 lncRNA, 22 miRNA, and 55 mRNA. Enrichment analysis revealed that mRNA was associated with cancer-related pathways. In cell assays, knockdown of AFAP1-AS1 inhibited cell proliferation, invasion, and migration, and AFAP1-AS1 promoted apoptosis. ConclusionIn this study, we construct a lncRNA-mediated ceRNA network, which may help to further investigate the mechanism of action of LUAD. In addition, through cellular experiments, AFAP1-AS1 is found to have potential as a therapeutic target for LUAD.
This research is to explore the perfusion time-intensity curve parameters of a lung adenocarcinoma xenograft into nude mouse model with contrast enhanced ultrasonography (CEUS); and to investigate the angiogenesis features of tumor at different growth time. Twenty one lung adenocarcinoma xenografted nude mice were divided into three groups and inculcated with human lung adenocarcinoa. Time window for examining CEUS were respectively in 7-day, 14-day and 28-day. The perfusion parameters including rise time (RT), peak intensity (PI), area under the curve (AUC) of lung tumor were obtained on CEUS images by using off-line software Q lab. Immunohistochemically staining for CD34 was used to observe the microvessel density (MVD).The 7-day group had the highest AUC and PI; AUC and PI of 14-day and 28-day group decreased gradually (P < 0.05). RT was increased as tumor growth. In tumor with necrosis, AUC and PI of non-necrosis part were also larger than necrosis part (P < 0.05). Immunohistochemically staining for CD34 of all tumors reflected that the density of microvessels in necrosis tumor was significantly higher than those without necrosis (7.50±3.44 vs.12.44±5.74, P=0.034). Pearson correlation indicated that PI was positively related with MVD (r=0.668, P=0.008). Lung adenocarcinoma perfusion characteristic can be accessed from time-intensity curve parameters by using noninvasively and non-radiative contrast enhanced ultrasonography. Time-intensity curve parameters including AUC, PI and RT may reflect tumor angiogenesis.