Plasma Based Protein Signatures Associated with Small Cell Lung Cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Fahrmann JF
PROVIDER: S-EPMC8391533 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Alternative splicing (AS) is fundamental to transcriptome and proteome richness, and data from recent studies suggested a critical association between AS and oncogenic processes. To date, no systematic analysis has been conducted on AS from the perspective of different sexes and subtypes in non-small-cell lung cancer (NSCLC). Thus, we integrated the information of NSCLC patients from The Cancer Genome Atlas (TCGA) and evaluated AS profiles from the perspectives of sex and subtype. Eventually, a total of 813 and 1020 AS events were found to be significantly related to the overall survival (OS) of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. Four prognostic prediction models performed well at 1, 3, and 5 years, with an area under the receiver operating characteristic (ROC) curve (AUC) greater than 0.75. Notably, we explored the upstream splicing factors (SFs) and downstream regulatory mechanisms of the OS-associated AS events and verified four differentially expressed alternative splicing (DEAS) events via qPCR. These findings can provide important guidance for subsequent studies. In addition, we also constructed nomograms to facilitate early screening by clinicians and to determine patient outcomes in NSCLC.
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Project description:IntroductionSmall cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based classifier to assist in SCLC diagnoses.MethodsWe profiled deregulated circulating cell-free miRNAs in the plasma of SCLC patients. We tested selected miRNAs on a training cohort and created a classifier by integrating miRNA expression and patients' clinical data. Finally, we applied the classifier on a validation dataset.ResultsWe determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group.DiscussionThis study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.
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Project description:Resectable non-small-cell lung cancer (NSCLC) patients have poor prognosis, with 30-50% relapsing within 5 years. Current staging criteria do not fully capture the complexity of this disease. Survival could be improved by identification of those early-stage patients who are most likely to benefit from adjuvant therapy. Molecular classification by using mRNA expression profiles has led to multiple, poorly overlapping signatures. We hypothesized that differing statistical methodologies contribute to this lack of overlap. To test this hypothesis, we analyzed our previously published quantitative RT-PCR dataset with a semisupervised method. A 6-gene signature was identified and validated in 4 independent public microarray datasets that represent a range of tumor histologies and stages. This result demonstrated that at least 2 prognostic signatures can be derived from this single dataset. We next estimated the total number of prognostic signatures in this dataset with a 10-million-signature permutation study. Our 6-gene signature was among the top 0.02% of signatures with maximum verifiability, reaffirming its efficacy. Importantly, this analysis identified 1,789 unique signatures, implying that our dataset contains >500,000 verifiable prognostic signatures for NSCLC. This result appears to rationalize the observed lack of overlap among reported NSCLC prognostic signatures.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2023-10-31 | GSE240759 | GEO
Project description:Due to high mortality rates of lung cancer, there is a need for identification of new, clinically useful markers, which improve detection of this tumor in early stage of disease. In the current study, serum peptide profiling was evaluated as a diagnostic tool for non-small cell lung cancer patients. The combination of the ZipTip technology with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the analysis of peptide pattern of cancer patients (n = 153) and control subjects (n = 63) was presented for the first time. Based on the observed significant differences between cancer patients and control subjects, the classification model was created, which allowed for accurate group discrimination. The model turned out to be robust enough to discriminate a new validation set of samples with satisfactory sensitivity and specificity. Two peptides from the diagnostic pattern for non-small cell lung cancer (NSCLC) were identified as fragments of C3 and fibrinogen ? chain. Since ELISA test did not confirm significant differences in the expression of complement component C3, further study will involve a quantitative approach to prove clinical utility of the other proteins from the proposed multi-peptide cancer signature.
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Project description:BackgroundNon-small-cell lung cancer (NSCLC) is the leading cause of cancer death. Early detection of NSCLC will improve its outcome. The current techniques for NSCLC early detection are either invasive or have low accuracy. Molecular analyses of clinical specimens present promising diagnostic approaches. Non-coding RNAs (ncRNAs) play an important role in tumorigenesis and could be developed as biomarkers for cancer. Here we aimed to develop small nucleolar RNAs (snoRNAs), a common class of ncRNAs, as biomarkers for NSCLC early detection. The study comprised three phases: (1) profiling snoRNA signatures in 22 NSCLC tissues and matched noncancerous lung tissues by GeneChip Array, (2) validating expressions of the signatures by RT-qPCR in the tissues, and (3) evaluating plasma expressions of the snoRNAs in 37 NSCLC patients, 26 patients with chronic obstructive pulmonary disease (COPD), and 22 healthy subjects.ResultsIn the surgical tissues, six snoRNAs were identified, which were overexpressed in all tumour tissues compared with their normal counterparts. The overexpressions of the genes in tumors were confirmed by RT-qPCR. The snoRNAs were stably present and reliably detectable in plasma. Of the six genes, three (SNORD33, SNORD66 and SNORD76) displayed higher plasma expressions in NSCLC patients compared with the cancer-free individuals (All < 0.01). The use of the three genes produced 81.1% sensitivity and 95.8% specificity in distinguishing NSCLC patients from both normal and COPD subjects. The plasma snoRNA expressions were not associated with stages and histological types of NSCLC (All > 0.05).ConclusionsThe identified snoRNAs provide potential markers for NSCLC early detection.
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Project description:Tobacco smoking is responsible for 85% of all lung cancers. To further our understanding of the molecular pathogenesis of lung cancer, we determined whether smoking history leads to the emergence of specific genomic alterations found in non-small cell lung cancer (NSCLC).To identify gene copy number alterations in NSCLCs associated with smoking history or DNA repair capacity.Seventy-five NSCLCs were selected for this study from patients with current, none, or past smoking history, including pack year information. Tissue sections were microdissected, and DNA was extracted, purified, and labeled by random priming before hybridization onto bacterial artificial chromosome (BAC) arrays. Normalized ratios were correlated with smoking history and DNA repair capacity was measured by an in vitro lymphocyte assay in the same patients.We identified smoking-related genomic signatures in NSCLCs that could be predicted with an overall 74% accuracy. Lung tumors arising from current-smokers had the greatest number of copy number alterations. The genomic regions most significantly associated with smoking were located within 60 regions and were functionally associated with genes controlling the M phase of the cell cycle, the segregation of chromosomes, and the methylation of DNA. Verification of the data is provided from data in the public domain and by quantitative real-time polymerase chain reaction. The associations between genomic abnormalities and DNA repair capacity did not reach statistical significance.These findings indicate that smoking history leaves a specific genomic signature in the DNA of lung tumors and suggest that these alterations may reflect new molecular pathways to cancer development.
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Project description:Objectives: Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based biomarker classifier to assist in SCLC diagnoses. Materials and Methods: We profiled deregulated circulating cell-free miRNA in the plasma of SCLC patients. We tested selected miRs on a training cohort and created a classifier by integrating miRNA expression and patient clinical data. Finally, we applied the classifier on a validation dataset. Results: We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group. Conclusion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.
2023-10-31 | GSE240758 | GEO