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.
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.
Objective To access the diagnostic performance of CT texture analysis to differentiate atypical pancreatic solid pseudopapillary tumor (SPT) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 26 patients with pathologically proved atypical SPT and 52 patients with PDAC were analyzed. 3D regions of interest (ROIs) on arterial phase (AP) and portal venous phase (PVP) images were drawn by ITK-Snap software. A.K. software (GE company, USA) was used to extract texture features for the discrimination of atypical SPT and PDAC. After removing redundancy (by a correlation analysis through R software), texture features were selected by single-factor and multi-factor logistic regression, and logistic regression model was finally established. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of single texture feature and logistic model. Results A total of 792 texture features ［396 of AP, 396 of PVP］ from AP and PVP CT images were obtained by A.K. software. Of these, 61 texture features (35 of AP, 26 of PVP) were selected by R software (result of correlation analysis showed that correlation coefficient >0.7). Two texture features, including MinIntensity and Correlation_AllDirection_offset1_SD, were selected to establish logistic model. The sensitivity and specificity of these 2 texture features were 71.15% and 76.92%, 63.46% and 76.92% respectively, the area under curve ( AUC) were 0.740 and 0.754 respectively. The model’s sensitivity and specificity were 73.08% and 80.77% respectively, the AUC value was 0.796. There was no significance among the model, MinIntensity, and Correlation_AllDirection_offset1_SD (P>0.05). Conclusions CT texture analysis of 3D ROI is a quantitative method for differential diagnosis of atypical SPT from PDAC.
Hepatoid adenocarcinoma (HAC) of the pancreas is a rare, highly malignant tumor with poor prognosis. This article presents the CT and MR features of HAC of the pancreas, while also reviewing the relevant literatures to succinctly summarize the underlying pathophysiological mechanism, imaging diagnosis, and differential diagnosis. The objective is to enhance the understanding of HAC of the pancreas among clinicians and radiologists.
Objective To investigate the relationship between clinical features and lymph node metastasis in lung adenocarcinoma patients with T1 stage. Methods We retrospectively analyzed the clinical data of 253 T1-stage lung adenocarcinoma patients (92 males and 161 females at an average age of 59.45±9.36 years), who received lobectomy and systemic lymph node dissection in the Second Affiliated Hospital of Harbin Medical University from October 2013 to February 2016. Results Lymph node metastasis was negative in 182 patients (71.9%) and positive in 71 (28.1%). Poor differentiation (OR=6.988, P=0.001), moderate differentiation (OR=3.589, P=0.008), micropapillary type (OR=24.000, P<0.001), solid type (OR=5.080, P=0.048), pleural invasion (OR=2.347, P=0.024), age≤53.5 years (OR=2.594, P=0.020) were independent risk factors for lymph node metastasis. In addition, in the tumor with diameter≥1.55 cm (OR=0.615, P=0.183), although the cut-off value of 1.55 cm had no significant difference, it still suggested that tumor diameter was an important risk factor of lymph node metastasis. Conclusion In lung adenocarcinoma with T1 stage, the large tumor diameter, the low degree of differentiation, the high ratio of consolidation, and the micropapillary or solid pathological subtypes are more prone to have lymph node metastasis.
ObjectiveTo summarize the relationship between integrins, tumor metabolism, and tumor cells with pancreatic stellate cells in the tumor microenvironment, in order to provide targets and ideas for the treatment of pancreatic ductal adenocarcinoma.MethodTo review the literatures on pancreatic stellate cells, integrins, and amino acid metabolism as therapeutic targets for pancreatic ductal adenocarcinoma in the domestic and overseas.ResultsThe drug research for pancreatic ductal adenocarcinoma was currently under vigorous development, but remain in the animal and clinical test stage. As a new therapeutic protein, ProAgio could inhibit the expression of integrin αvβ3, activation and secretion of pancreatic stellate cells, and alanine metabolism in the microenvironment of pancreatic ductal adenocarcinoma, so as to achieve the dual effects of anti-fibrosis and anti-tumor.ConclusionsThe roles of activated pancreatic stellate cells, ProAgio, integrin αvβ3, and alanine metabolism in pancreatic ductal adenocarcinoma have been partially elucidated, but the specific mechanism still needs further investigation and may become a completely new therapeutic target someday.
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.
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.