Survival prediction of stage I lung adenocarcinomas by expression of 10 genes.
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ABSTRACT:
SUBMITTER: Bianchi F
PROVIDER: S-EPMC2030461 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Comparing Stage 1 and stage 2 lung adenocarcinomas against a standard human reference RNA sample. Two colour design, stage 1 or stage 2 adenocarcinoma vs human reference RNA (Clontech), 5 stage 1 comparisons and 5 stage 2 comparisons
| S-ECPF-GEOD-28956 | biostudies-other
Project description:ObjectivesThyroid transcription factor 1 (TTF-1) is routinely tested in the diagnostic evaluation of suspected lung cancers, is commonly expressed by lung adenocarcinomas, and may modulate lung cancer biology. We examined the role of TTF-1 as a predictive and prognostic marker in patients with advanced lung adenocarcinomas.Materials and methodsWe analyzed clinical, pathologic, and molecular features, treatments received, and overall survival obtained from the medical records of 479 consecutive patients at a single site with stage IV lung adenocarcinomas and evaluable TTF-1 expression. TTF-1 expression was determined by immunohistochemistry using antibody 8G7G3/1.Results and conclusionTTF-1 expression was evaluable in 479 (75%) of all patients reviewed, and was positive in 383 (80%, 95% CI 76-83%). Clinicopathologic features were similar between TTF-1 positive and TTF-1 negative tumors, except EGFR mutations were more common in TTF-1 positive cases (24% vs 6%, p<0.001). In univariate analysis, overall survival was significantly longer in patients with TTF-1 positive versus TTF-1 negative tumors (18 months vs 9 months, p<0.0001). In multivariate analysis, TTF-1 positivity remained associated with better overall survival (HR=0.38, p<0.0001), exceeding the prognostic impact of Karnofsky performance status >/=80% (HR 0.62, p=0.0003) and receipt of first-line combination chemotherapy or targeted therapy (HR relative to first-line single agent chemotherapy 0.59, p=0.05 and 0.51, p=0.05 respectively). Both patients with TTF-1 positive and TTF-1 negative cancers had longer durations of initial therapy when treated with pemetrexed-based chemotherapy. In patients with advanced lung adenocarcinomas, TTF-1 expression is associated with better survival but is not predictive of distinct benefit from pemetrexed-based chemotherapy.
| S-EPMC6423973 | biostudies-literature
Project description:Lung cancer is a leading cause of cancer deaths worldwide and new biomarkers are of utmost importance. Studies have indicated that the anti-plasminogen activators SerpinB2 and Neuroserpin, and the adhesion molecule L1CAM, have a coordinated impact on development of metastasis. Here, we examined whether expression of these markers was associated with clinico-pathologic characteristics and prognosis in resected non-small cell lung cancer (NSCLC). Surgical specimens from 438 NSCLC patients treated at Haukeland University Hospital, Bergen, Norway (1993-2010) were included (median age 68 years; 213 adenocarcinomas, 135 squamous cell carcinomas, 90 others). Representative tumor sections were stained for SerpinB2, Neuroserpin, and L1CAM. Low expression of SerpinB2 was associated with reduced lung cancer specific survival (LCSS) in adenocarcinomas (p = 0.017), also in stage I (p = 0.031). In contrast, high SerpinB2 was associated with reduced LCSS in stage I squamous cell carcinomas (p = 0.022). Although Neuroserpin and L1CAM showed some associations with clinico-pathologic phenotype, there were no associations with survival. In multivariate survival analysis of adenocarcinomas, low SerpinB2 demonstrated independent prognostic value (HR 1.8, p = 0.008). In summary, low expression of SerpinB2 in lung adenocarcinomas was an independent prognostic factor. In contrast to findings by others, we found no impact of L1CAM on survival.
| S-EPMC5710879 | biostudies-literature
Project description:BACKGROUND:Discovery of the progression-associated genes and pathways in lung adenocarcinoma (LAD) has important implications in understanding the molecular mechanism of tumor development. However, few studies had been performed to focus on the changes of pathways in lung adenocarcinoma development using microarray expression profile. RESULT:We performed a meta-analysis of 4 LAD microarray datasets encompassing 353 patients to reveal differentially expressed genes (DEGs) between normal lung tissues and LAD of different stages. Overall, 1 838 genes were found to be dys-regulated, and the adipogenesis, circadian rhythm, and Id pathways were significantly changed. Interestingly, most of the genes from the same gene family (such as Interleukin receptor, Matrix metallopeptidase, Histone cluster and Minichromosome maintenance complex component families) were found to be up-regulated (or down-regulated). Real-time PCR (qRT-PCR) was applied to validate the expression of randomly selected 18 DEGs in LAD cell lines. In the pathway analysis among stages, Oxidative stress, Glycolysis/Gluconeogenesis and Integrin-mediated cell adhesion pathways, which were involved in cancer cell proliferation and metastasis, were showed to be significantly regulated in stages other than IA. CONCLUSION:Genes involved in adipogenesis and Id pathways might play important roles in development of LADs. The similar trend of expression of the gene family members suggested coordinate regulation in tumor progression. Three pathways (Oxidative stress, Glycolysis/Gluconeogenesis and Integrin-mediated cell adhesion pathways) significantly regulated in stages other than stage IA suggested that genes and pathways conferring invasive character might be activated in the preinvasive stage IB, while the Oxidative stress and the Glycolysis/Gluconeogenesis pathways might have strong connections to cisplatin-based chemotherapy. The insignificantly regulated three pathways in stage IA might be used in early-stage detection of LAD.
| S-EPMC3088877 | biostudies-other
Project description:BackgroundTo study the role of computed tomography (CT)-derived radiomics features and clinical characteristics on the prognosis of "driver gene-negative" lung adenocarcinoma (LUAD) and to explore the potential molecular biological which may be helpful for patients' individual postoperative care.MethodsA total of 180 patients with stage I-III "driver gene-negative" LUAD in the First Affiliated Hospital of Sun Yat-Sen University from September 2003 to June 2015 were retrospectively collected. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to screen radiomics features and calculated the Rad-score. The prediction performance of the nomogram model based on radiomics features and clinical characteristics was validated and then assessed with respect to calibration. Gene set enrichment analysis (GSEA) was used to explore the relevant biological pathways.ResultsThe radiomics and the clinicopathological characteristics were combined to construct a nomogram resulted in better performance for the estimation of OS (C-index: 0.815; 95% confidence interval [CI]: 0.756-0.874) than the clinicopathological nomogram (C-index: 0.765; 95% CI: 0.692-0.837). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinicopathological nomogram. The clinical prognostic risk score of each patient was calculated based on the radiomics nomogram and divided by X-tile into high-risk (> 65.28) and low-risk (≤ 65.28) groups. GSEA results showed that the low-risk score group was directly related to amino acid metabolism, and the high-risk score group was related to immune and metabolism pathways.ConclusionsThe radiomics nomogram was promising to predict the prognosis of patients with "driver gene-negative" LUAD. The metabolism and immune-related pathways may provide new treatment orientation for this genetically unique subset of patients, which may serve as a potential tool to guide individual postoperative care for those patients.
| S-EPMC10264479 | biostudies-literature
Project description:BackgroundWe aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD-L1) expression in advanced stage lung adenocarcinoma.MethodsThis retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD-L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c-statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability.ResultsAmong 153 patients, 53 patients were classified as PD-L1 positive and 100 patients as PD-L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad-score by radiomic analysis was higher in the PD-L1 positive group than in the PD-L1 negative group with a statistical significance (-0.378 ± 1.537 vs. -1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c-statistic = 0.646 vs. 0.550, P = 0.0299).ConclusionsQuantitative CT radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.Key pointsSignificant findings of the study Quantitative CT radiomic features can help predict PD-L1 expression, whereas none of the qualitative imaging findings is associated with PD-L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.
| S-EPMC7113038 | biostudies-literature
Project description:Comparing Stage 1 and stage 2 lung adenocarcinomas against a standard human reference RNA sample.
2011-05-04 | GSE28956 | GEO
Project description:Lymph node status is a strong predictor of outcome for lung cancer patients. Recently, several reports have hinted that gene expression profiles of primary tumor may be able to predict node status. The goals of this study were to determine if microarray data could be used to accurately classify patients with regard to pathologic lymph node status, and to determine if this analysis could identify patients at risk for occult disease and worse survival.Two previously published lung adenocarcinoma microarray data sets were reanalyzed. Patients were separated into two groups based on pathologic lymph node positive (pN+) or negative (pN0) status, and prediction analysis of microarray (PAM) was used for training and validation to classify nodal status. Overall survival analysis was performed based on PAM classifications.In the training phase, a 318-gene set gave classification accuracy of 88.4% when compared with pathology. Survival was significantly worse in PAM-positive compared with PAM-negative patients overall (P < 0.0001) and also when confined to pN0 patients only (P = 0.0037). In the validation set, classification accuracy was again 94.1% in the pN+ patients but only 21.2% in the pN0 patients. However, among the pN0 patients, recurrence rates and overall survival were significantly worse in the PAM-positive compared with PAM-negative patients (P = 0.0258 and 0.0507).Analysis of gene expression profiles from primary tumor may predict lymph node status but frequently misclassifies pN0 patients as node positive. Recurrence rates and overall survival are worse in these "misclassified" patients, implying that they may in fact have occult disease spread.
| S-EPMC2211271 | biostudies-literature
Project description:Comparing Stage 1 and stage 2 lung adenocarcinomas against a standard human reference RNA sample. Two colour design, stage 1 or stage 2 adenocarcinoma vs human reference RNA (Clontech), 5 stage 1 comparisons and 5 stage 2 comparisons
2011-05-04 | E-GEOD-28956 | biostudies-arrayexpress
Project description:Despite surgical treatment of stage I non-small cell lung cancer (NSCLC), one third of patients will eventually have a recurrence. Robust prognostic markers are required to better manage therapy options. MicroRNAs play important roles in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina). The expression differences between cancer subtypes were compared by t tests. The association of miRNA expression profile with recurrence free survival (RFS) was assessed using partial Cox regression models. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 32 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype; while the second containing 27 miRNAs was adenocarcinoma specific. Both of them were validated using FFPE and/or fresh frozen tissues in independent data set with 170 stage I patients. This has important prognostic or therapeutic implications for the management of patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina).
| S-ECPF-GEOD-29135 | biostudies-other