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Gene expression profiling of normal lung tissue adjacent to lung adenocarcinoma (ADCA)

ABSTRACT: Individual variations in the transcriptional profile of normal lung tissue may reflect the lung ADCA patients’ predisposition to a specific clinical stage Associations between clinical outcome of cancer patients and the gene expression signature in primary tumors at time of diagnosis have been reported. To test whether gene expression patterns in the normal tissue surrounding the cancerous tissue might correlate with clinical stage, we compared transcriptome of normal lung samples of adenocarcinoma (ADCA) smoker patients of clinical stage I versus stage >I, and identified 55 differentially expressed genes. Eight out of 10 genes validated by quantitative real-time PCR confirmed statistical association with clinical stage, with 6 genes downregulated in high clinical stage patients, including the TMEM100 gene showing the best statistical association. Five of these 6 genes were also downregulated in lung ADCA tissue as compared to normal tissue. Functional studies in vitro indicated that four of them (SLC14A1, SMAD6, TMEM100, and TXNIP) inhibited colony formation when over-expressed by transfection in lung cancer cell lines, suggesting their potential tumor-suppressor activity. Our findings indicated that individual variations in the transcriptional profile of normal lung tissue may reflect the lung ADCA patients’ predisposition to a specific clinical stage. The 120 RNA samples from normal lung were combined in 24 small pools, with 12 pools constituted by stage I patients and 12 pools by stage >I patients (5 samples per pool)

ORGANISM(S): Homo sapiens

SUBMITTER: Loris De Cecco

PROVIDER: E-GEOD-31267 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Gene expression profiling of normal lung tissue adjacent to lung adenocarcinoma (ADCA)

Project description:Individual variations in the transcriptional profile of normal lung tissue may reflect the lung ADCA patients’ predisposition to a specific clinical stage Associations between clinical outcome of cancer patients and the gene expression signature in primary tumors at time of diagnosis have been reported. To test whether gene expression patterns in the normal tissue surrounding the cancerous tissue might correlate with clinical stage, we compared transcriptome of normal lung samples of adenocarcinoma (ADCA) smoker patients of clinical stage I versus stage >I, and identified 55 differentially expressed genes. Eight out of 10 genes validated by quantitative real-time PCR confirmed statistical association with clinical stage, with 6 genes downregulated in high clinical stage patients, including the TMEM100 gene showing the best statistical association. Five of these 6 genes were also downregulated in lung ADCA tissue as compared to normal tissue. Functional studies in vitro indicated that four of them (SLC14A1, SMAD6, TMEM100, and TXNIP) inhibited colony formation when over-expressed by transfection in lung cancer cell lines, suggesting their potential tumor-suppressor activity. Our findings indicated that individual variations in the transcriptional profile of normal lung tissue may reflect the lung ADCA patients’ predisposition to a specific clinical stage.

2013-01-01 | GSE31267 | GEO

Characterizing the molecular spatial and temporal field of injury in early stage smoker non-small cell lung cancer patients after definitive surgery by expression profiling

Project description:Gene expression alterations in response to cigarette smoke have been characterized in normal-appearing bronchial epithelium of healthy smokers and it has been suggested that adjacent histologically normal tissue display tumor-associated molecular abnormalities. We sought to delineate the spatial and temporal molecular lung field of injury in smoker early stage non-small cell lung cancer (NSCLC) patients (n=19) who were accrued into a surveillance clinical trial for annual follow-up and bronchoscopies within one year after definitive surgery.

2012-12-01 | GSE40407 | GEO

Liu2023 - Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data

Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC

2023-07-11 | BIOMD0000001074 | BioModels

Gene expression comparison of colorectal cancer samples from patients diagnosed at a young and elderly age

Project description:Colorectal cancer (CRC) incidence increases with age and early onset of the disease is an indication of genetic predisposition, estimated to cause up to 30 % of all CRC cases. To identify genes associated with an increased risk for CRC, genome-wide gene expression levels of CRCs from patients diagnosed at an early age and CRCs from patients diagnosed at higher age were investigated. Patients with known hereditary predisposition syndromes were excluded from the study. Twenty four patients were diagnosed at a young age (mean, 43 years; range, 28-53 years), referred to as the early onset group. They were excluded from the HNPCC and FAP syndromes by clinical criteria and no other cancer syndromes were recorded for these patients. The second group consisted of 17 patients with primary diagnosis at old age (mean, 79 years; range, 69-87 years), referred to as the late onset group. The samples from the late onset group were selected to reflect the composition of the early onset group with respect to gender, tumor localization and tumor stage according to The International Union Against Cancer (UICC)/American Joint Committee on Cancer (AJCC). Microsatellite instability (MSI) status was previously analyzed in all samples to eliminate the potential risk of including patients with inherited DNA repair deficiencies.

2011-09-06 | E-GEOD-25071 | biostudies-arrayexpress

Proteomic Analysis Enables Distinction of Early- Versus Advanced-Stage Lung Adenocarcinomas

Project description:Background: A gel-free proteomic approach was utilized to perform in-depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long-term goal of this study is to generate a large-scale, label-free proteomic data set from histologically well-classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers. Methods and Results: Cases of early-stage (I-II) and advanced-stage (III-IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung adenocarcinomas were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). From the analysis of ADC and normal specimens, 5,933 protein groups were identified. To examine the protein expression profile of ADC, a peak area-based quantitation method was used. In early- and advanced-stage ADC, 33 and 39 proteins were differentially-expressed respectively between normal and tumor tissue (adjusted p-value < 0.01, fold change ≥ 4). For early- and advanced stage ADC tumors compared to normal patient-matched tissue, 11 and 22 proteins and 23 and 16 proteins were identified as down- and up-regulated, respectively. In silico functional analysis of the up-regulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most over-represented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes. Conclusions: Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage-specific drug targets for rational intervention.

2021-09-09 | PXD019259 | Pride

Comparative Tissue Proteomics for the Diagnosis of Indeterminate Pulmonary Nodules

Project description:We identified candidate protein biomarkers to distinguish lung adenocarcinomas from benign nodules. We employed shotgun proteomics using a multidimensional peptide separation approach coupled to tandem mass spectrometry (LC-MS/MS) to characterize proteomes of a collection of 34 benign lung nodules. The benign lung nodule inventories were compared to inventories from early stage lung adenocarcinomas, from 24 patients with non-adjuvant treatment following the resection, and to inventories from normal tissue collected from 10 patients as bronchial and alveolar epithelium to identify biomarkers.We developed PRM assays to quantify a subset of 43 candidate biomarker proteins via 170 tryptic peptides in a single LC-PRM-MS run using a validation cohort of 20 benign nodules, 20 adenocarcinoma, and 20 normal lung tissues.

2016-10-09 | MSV000080236 | MassIVE

Transcriptome of non-involved lung parenchyma from smoker lung adenocarcinoma patients from Milan, Italy.

Project description:Scientific evidence indicates that genetic factors may contribute to differences in lung cancer risk for individuals with similar levels of tobacco exposure, which is the main environmental risk factor of lung cancer. Moreover, lung cancer patients show large differences in clinical staging and survival; these differences seem to be attributable, at least partially, to the genetic background. The analysis of the molecular properties (e.g., germline variations and genome-wide expression levels) of non-involved tissue from lung cancer patients may contribute in the identification of genetic factors involved in the development and progression of this pathology. To this aim, we analyzed two series (discovery series, n = 204, and validation series, n = 78) of non-tumor lung tissue samples from smokers that underwent surgical lobectomy for adenocarcinoma in Milan, Italy. First, we examined the whole transcriptome of these two series to define the candidate genes and pathways associated with either lung cancer risk or prognosis in this cohort. Moreover, as sex and age are known to strongly influence the pathophysiology of human lungs, we used transcriptome data from the same samples to identify sex- and age-related transcriptional differences in lung. Samples of non-involved (apparently normal) lung parenchyma excised from patients who underwent lobectomy for lung adenocarcinoma in the area around Milan, Italy. The samples were analyzed in two sets: a discovery series (n = 204) and a validation series (n = 78).

2016-01-20 | E-GEOD-71181 | biostudies-arrayexpress

Intratumor heterogeneity of gene expression profiles in early stage non-small cell lung cancer (NSCLC)

Project description:Gene expression profiling of NSCLC tissues let to the establishment of several prognostic, predictive gene signatures with little overlap. To study factors introducing variability into gene expression microarray studies we compared interpatient (patient-to-patient) and intrapatient (tumor sub-sample) variations in gene expression profiles in stage I and II NSCLC tissues. We identified 128 probe sets which showed variable intratumoral expression Four different sites (A,B,C,D) of individual primary tumors and matched distant normal lung tissue (N) from 20 patients were used to establish gene expression patterns captured by Affymetrix HG-U133 Plus 2.0 arrays (n = 100).

2014-09-23 | E-GEOD-33532 | biostudies-arrayexpress

CD8 TIL subsets isolation from early-stage, untreated NSCLC

Project description:The overall objective of this study was to characterize the diversity and ontogeny of CD8 T cells in untreated lung tumors. This was accomplished by performing bulk RNAseq and flow cytometry analysis on CD8+ T cell subsets isolated from tumor tissue, normal adjacent to tumor (juxta0 tissue samples from patients undergoing surgical resection for early-stage, untreated non-small-cell lung cancer (NSCLC).

2023-10-09 | GSE232899 | GEO

Transcriptome analysis to decipher molecular mechanism governing radiation induced lung fibrosis in mouse models

Project description:Normal lung tissue tolerance constitutes a limiting factor in delivering the required dose of radiotherapy to cure thoracic and chest wall malignancies. Patient genetic predisposition, the volume of irradiated lung and combination regimens consisting of concurrent chemotherapy are correlated with increased risk of radiation induced toxicity in lung. The main purpose of this study is to investigate dose-response regulations of fractionated (5-fractions) of mouse lung irradiation based on a comprehensive dose-escalation program, for a better understanding of molecular mechanism governing radiation induced lung fibrosis.

2022-08-02 | E-MTAB-5922 | biostudies-arrayexpress

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