ArticleBMC genomics2019
A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Beyond Histology: A Unified Transcriptomic Atlas Defines Lung Cancer Biologic States and Subtypes.bioRxiv : the preprint server for biology · 2026Article
- LDHA associated with telomerase-related genes promotes tumorigenesis and progression of lung adenocarcinoma.Scientific reports · 2025Article
- A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer.Cancer informatics · 2023Article
- Comprehensive Characterization of Human Lung Large Cell Carcinoma Identifies Transcriptomic Signatures with Potential Implications in Response to Immunotherapy.Journal of clinical medicine · 2022Article
- An 8-Gene Signature for Classifying Major Subtypes of Non-Small-Cell Lung Cancer.Cancer informatics · 2022Article
- lncRNA pair as candidate diagnostic signature for colorectal cancer based on the within-sample relative expression levels.Frontiers in oncology · 2022Article
- Hierarchical identification of a transcriptional panel for the histological diagnosis of lung neuroendocrine tumors.Frontiers in genetics · 2022Article
- Comprehensive molecular characterization of lung tumors implicates AKT and MYC signaling in adenocarcinoma to squamous cell transdifferentiation.Journal of hematology & oncology · 2021Article
- Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data.Scientific reports · 2021Article
- Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma.Oncology letters · 2021Article
- The value of AGR2 and KRT5 as an immunomarker combination in distinguishing lung squamous cell carcinoma from adenocarcinoma.American journal of translational research · 2021Article
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11 authors.
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Abstract
backgroundTargeted therapy for non-small cell lung cancer is histology dependent. However, histological classification by routine pathological assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. Additionally, the effectiveness of immunomarkers is limited by technical inconsistencies of immunostaining and lack of standardization for staining interpretation.
resultsUsing gene expression profiles of pathologically-determined lung adenocarcinomas and squamous cell carcinomas, denoted as pADC and pSCC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to distinguish ADC from SCC. The signature consists of two genes, KRT5 and AGR2, which has the stable REO pattern of KRT5 > AGR2 in pSCC and KRT5 < AGR2 in pADC. In the two test datasets with relative unambiguous NSCLC types, the apparent accuracy of the signature were 94.44 and 98.41%, respectively. In the other integrated dataset for frozen tissues, the signature reclassified 4.22% of the 805 pADC patients as SCC and 12% of the 125 pSCC patients as ADC. Similar results were observed in the clinical challenging cases, including FFPE specimens, mixed tumors, small biopsy specimens and poorly differentiated specimens. The survival analyses showed that the pADC patients reclassified as SCC had significantly shorter overall survival than the signature-confirmed pADC patients (log-rank p = 0.0123, HR = 1.89), consisting with the knowledge that SCC patients suffer poor prognoses than ADC patients. The proliferative activity, subtype-specific marker genes and consensus clustering analyses also supported the correctness of our signature.
conclusionsThe non-subjective qualitative REOs signature could effectively distinguish ADC from SCC, which would be an auxiliary test for the pathological assessment of the ambiguous cases.
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