ArticleMolecular oncology2017
An individualized gene expression signature for prediction of lung adenocarcinoma metastases.
Article in Molecular oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- The multiple roles of interferon regulatory factor family in health and disease.Signal transduction and targeted therapy · 2024Review
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- Sec61γ is a vital protein in the endoplasmic reticulum membrane promoting tumor metastasis and invasion in lung adenocarcinoma.British journal of cancer · 2023Article
- Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma.Genome medicine · 2022Article
- A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma.Cancer cell international · 2022Article
- A novel epithelial-mesenchymal transition (EMT)-related gene signature of predictive value for the survival outcomes in lung adenocarcinoma.Frontiers in oncology · 2022Article
- Nonnegative matrix factorization-based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub-consensuses of lung adenocarcinoma.Cancer medicine · 2021Article
- IRF1 regulates the progression of colorectal cancer via interferon‑induced proteins.International journal of molecular medicine · 2021Article
- A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells.Frontiers in immunology · 2021Article
- Article
- A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients.BMC genomics · 2020Article
- A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy.The Prostate · 2020Article
- Unique metastasis-associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma.Thoracic cancer · 2020Article
- Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma.Journal of translational medicine · 2020Article
- A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas.BMC genomics · 2019Article
- Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma.Aging · 2019Article
- A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection.Frontiers in oncology · 2019Article
- Shared liver-like transcriptional characteristics in liver metastases and corresponding primary colorectal tumors.Journal of Cancer · 2018Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our laboratory previously reported an individual-level signature consisting of nine gene pairs, named 9-GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non-small-cell lung cancer after complete surgical resection. In this study, we first validated the cross-platform robustness of 9-GPS by demonstrating that 9-GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA-sequencing platform in The Cancer Genome Atlas (TCGA; log-rank P = 0.0318, C-index = 0.55). Applying 9-GPS to all the 423 stage I-IV LUAD samples in TCGA, the predicted high-risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9-GPS and found that the modified 9-GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9-GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9-GPS is a robust signature for identifying early-stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.
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