Dataset Information

The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma.

ABSTRACT: Background:Primary pulmonary papillary adenocarcinoma (PA) is a specific and rare subtype of invasive pulmonary adenocarcinoma (ADC). The knowledge concerning the clinicopathologic features and prognosis of patients with primary pulmonary PA has not been clarified because of its rarity. Methods:The clinical data of a total of 3391 patients with primary pulmonary PA were retrospectively analyzed to confirm their clinical characteristics and factors influencing prognosis and were in comparison with 3236 patients with non- PA pulmonary adenocarcinoma. All patients were histologically diagnosed between 1988 and 2015 in The Surveillance Epidemiology and End Results (SEER) database. A nomogram with satisfactory predictive performance was established to visually predict long-term survival of these patients. Results and conclusion:Collectively, primary pulmonary PA is a rare pathological cancer and its prognosis is analogous to that of non-PA pulmonary adenocarcinoma. Older age, larger lesions, distant metastases, lymph node invasion, and poor pathological differentiation are correlative with unacceptable prognosis. Surgical intervention is conducive to reaping favorable prognosis. Unfortunately, radiotherapy or chemotherapy results of no significant effects on patient survival. In our study, a nomogram with prognostic function is formulated to confer individual prediction of overall survival (OS).

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7070103 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma.

Zhang Yuqian Y Xie Hui H Zhang Ziying Z Zhang Pengfei P Chen Peng P Wang Xiang X

Open medicine (Warsaw, Poland) 20200220

<h4>Background</h4>Primary pulmonary papillary adenocarcinoma (PA) is a specific and rare subtype of invasive pulmonary adenocarcinoma (ADC). The knowledge concerning the clinicopathologic features and prognosis of patients with primary pulmonary PA has not been clarified because of its rarity.<h4>Methods</h4>The clinical data of a total of 3391 patients with primary pulmonary PA were retrospectively analyzed to confirm their clinical characteristics and factors influencing prognosis and were in ...[more]

PMID: 32195357

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Project description:To study a complex network of gene expression changes underlying c-Myc-triggered carcinogenesis in lung we performed oligonucleotide array analysis of solid lung adenocarcinomas which developed in transgenic mice due to constitutive expression of c- Myc in alveolar type II epithelial cells and compared them with the non-transgenic lung as a control. The microarray analysis yielded 162 up regulated and 301 down regulated genes. The genes were further analyzed with regard to their biological functions, to known responsiveness to c-Myc and to the presence of potential c-Myc -binding sites in their promoters. We identified inappropriate expression of a variety of genes involved in the several pathways critical for the development of lung tumor: cell cycle, cell growth, cell death, adhesion, cytoskeleton organization, invasive and angiogenic capacity. Some genes identified in this study were known to be similarly changed in human malignancies and specifically in lung tumors which makes the SPC/c-Myc-transgenic mouse model useful for investigating human lung cancer. Many other genes differently expressed in lung tumors are novel targets for mechanism based therapies and serve as useful tumor markers. New c- Myc-responsive genes and putative c-Myc -targets identified in this study contributed to the collective examination of distinct c-Myc transcriptomes that are partly cell type- and species- specific The molecular rules defining c-Myc’s activity in lung cancer remain elusive. We therefore investigated the c-Myc gene networks in a transgenic mouse model of lung cancer. Genome wide gene expression profiling applied to histological well-defined tumors revealed 162 up and 301 repressed genes and identified novel c-Myc targeted cell cycle and apoptosis genes. To better understand transcriptional responses the co-occupancy of different transcription factors (TF) were analyzed. This defined composite modules with 64, 68, 96 and 92, 88 and 96% of tumor associated down-regulated genes having TF-binding sites for either c-Myc, Klf7, Gata3 or c-Myc, Sox18 and P53, respectively. The composite modules fitted 48% and 80% of regulated genes. Likewise, 75, 96, 89 and 44, 59 and 54% of up-regulated genes in tumor or non-tumor transgenic tissue were enriched for Myc, Elf5,Cebp and c-Myc, Hbp1 and Hif1 TF-binding sites, respectively to suggest diverse and distinct TF-clusters at different stages of disease. Importantly, the genes coding for TFs were regulated as well and electrophoretic mobility shift assay (EMSA) confirmed c-Myc DNA-binding activity at targeted promoters of regulated genes. Furthermore, computational biology defined Gata3, mortalin and moesin to function in master regulatory gene networks and experimental validation of the constructed networks was achieved by EMSA, RT-PCR, Western blotting and gene reporter assay. Lastly, evidence for c-Myc DNA binding activity at targeted gene promoters was also derived from published Chip-sequence data of 7 human cell lines and the clinical significance of findings can be demonstrated by their regulation in human lung cancer. In conclusion, novel c-Myc targeted cell cycle and apoptosis genes were identified to function in gene regulatory networks. The results of the present study improve an understanding of c-Myc transforming capacity and provide a molecular rationale for the development of molecularly targeted therapies.

Project description:BackgroundDNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.MethodThe DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.ResultWe identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.ConclusionsThe present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

Project description:Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004-2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common type of Non-small-cell lung cancer (NSCLC). Distant metastasis of lung adenocarcinoma reduces the survival rate. we aim to develop a nomogram in order to predict the survival of patients with metastatic lung adenocarcinoma.MethodsWe retrospectively collected patients who were initially diagnosed as metastatic LUAD from 2010 to 2015 from SEER database. Based on the multivariate and univariate Cox regression analysis of the training cohorts, independent prognostic factors were assessed. The nomogram prediction model was then constructed based on these prognostic factors to predict the overall survival at 12, 24 and 36 months after surgery. Nomogram were identified and calibrated by c-index, time-dependent receiver operating characteristic curve (time-dependent AUC) and calibration curve. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities, and to better compare with the TNM staging system, we calculated the c-index of this nomogram as well as the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI).ResultA total of 1102 patients with metastatic LUAD who met the requirements were included for analysis. They were randomly divided into 774 in the training cohorts and 328 in the validation cohorts. As can be seen from the calibration plots, the predicted nomogram and the actual observations in both of the training and validation cohorts were generally consistent. The time dependent AUC values of 12 months, 24 months and 36 months were 0.707, 0.674 and 0.686 in the training cohorts and 0.690, 0.680 and 0.688 in the verification cohorts, respectively. C-indexes for the training and validation cohorts were 0.653 (95%CI 0.626-0.68)and 0.663 (95%CI 0.626-1), respectively. NRI and IDI show that the model is more clinical applicable than the existing staging system. In addition, our risk scoring system based on Kaplan Meier (K-M) survival curve can accurately divide patients into three hierarchy risk groups.ConclusionThis has led to the development and validation of a prognostic nomogram to assist clinicians in determining the prognosis of patients with metastatic lung adenocarcinoma after primary site surgery.

Dataset Information

The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma.

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The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma.

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