An individualized gene expression signature for prediction of lung adenocarcinoma metastases

Qi, Lishuang; Li, Tianhao; Shi, Gengen; Wang, Jiasheng; Li, Xin; Zhang, Sainan; Chen, Libin; Qin, Yuan; Gu, Yunyan; Zhao, Wenyuan; Guo, Zheng

doi:10.1002/1878-0261.12137

Cited by 27 publications

(22 citation statements)

References 65 publications

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“…A four‐gene prognostic signature, including one lncRNA gene, has been previously reported for LUAD patients, which performs well in stage I patients as well as EGFR‐mutant and wild‐type cohorts . Similarly, other studies have focused on characterizing gene signatures for prognosis of lung cancers . Until now, only a few lncRNA signatures have been characterized and proposed as biomarkers for lung cancer prognosis .…”

Section: Discussionmentioning

confidence: 99%

“…9 Similarly, other studies have focused on characterizing gene signatures for prognosis of lung cancers. [22][23][24] Until now, only a few lncRNA signatures have been characterized and proposed as biomarkers for lung cancer prognosis. [25][26][27] In the current study, 735 lncRNAs were found differentially expressed between LUAD patients with and without metastasis, among which 43 lncRNAs were identified to be significantly associated with RFS of patients.…”

Section: Discussionmentioning

confidence: 99%

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Unique metastasis‐associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma

Zhang

Han

et al. 2020

Thoracic Cancer

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Background Local relapses and metastases are primary causes of death in lung cancer patients. In the present study, we aimed to develop a prognostic signature based on metastasis‐associated lncRNAs in patients with lung adenocarcinoma (LUAD). Methods Firstly, the potential metastasis‐associated lncRNAs were identified by analyzing high‐throughput data from The Cancer Genome Atlas (TCGA), and based on which, an lncRNA signature was constructed for prediction of relapse in LUAD patients using Cox proportional hazards regression analysis. Moreover, the prognostic performance of the lncRNA signature was evaluated using Kaplan‐Meier survival analysis, time‐dependent receiver operating characteristic (ROC) curve and Cox analysis, respectively. In addition, the potential metastasis‐associated function of these six lncRNAs was confirmed by lncRNA over‐expression or depletion and in vitro transwell assays in LUAD cells. Results An lncRNA signature consisting of six most important prognostic factors (LINC01819, ZNF649‐AS1, HNF4A‐AS1, FAM222A‐AS1, LINC02323 and LINC00672) was developed. The signature was an independent predictor for patients' relapse‐free survival (RFS), which could provide higher tumor relapse prediction capability compared with the TNM staging system at three years and five years, respectively (P = 0.0209 and P = 0.0468). Furthermore, the combination of this lncRNA signature and TNM stage had better prognostic value than TNM stage alone at three and five years, respectively (P = 0.0006 and P = 0.0096). Additionally, all the lncRNAs of the signature had a regulatory role in the LUAD cell mobility. Conclusions This novel six‐lncRNA signature had considerable prognostic value for prediction of relapse in LUAD patients. Key points Significant findings of the studyThe unique metastasis‐associated lncRNA signature was related to tumor metastasis and prognosis in LUAD patients. What this study addsThis signature had considerable prognostic value for prediction of relapse in LUAD patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Unique metastasis‐associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma

Zhang

Han

et al. 2020

Thoracic Cancer

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“…Consistent with our previous study, 34 the qualitative REOs‐based prognostic signature is highly robust against experimental batch effects and differences in probe designs of different platforms. Besides, the signature can be readily applied at an individualized level without data normalization, which is more reliable and practical than quantitative signatures for risk prediction 54 …”

Section: Discussionmentioning

confidence: 99%

“…Besides, the signature can be readily applied at an individualized level without data normalization, which is more reliable and practical than quantitative signatures for risk prediction. 54 At present, most of the clinical tissue samples are fixed in FFPE blocks, [55][56][57] and stored in hospitals and tissue banks, which is a huge and precious resource for clinical research. 58 Nevertheless, FFPE samples are generally considered unreliable for gene expression analysis because of RNA degradation during preparation and storage.…”

Section: Discussionmentioning

confidence: 99%

A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy

Huang

Zhang

et al. 2020

The Prostate

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Background The qualitative transcriptional characteristics, the within‐sample relative expression orderings (REOs) of genes, are highly robust against batch effects and sample quality variations. Hence, we develop a qualitative transcriptional signature based on REOs to predict the biochemical recurrence risk of prostate cancer (PCa) patients after radical prostatectomy. Methods Gene pairs with REOs significantly correlated with the biochemical recurrence‐free survival (BFS) were identified from 131 PCa samples in the training data set. From these gene pairs, we selected a qualitative transcriptional signature based on the within‐sample REOs of gene pairs which could predict the recurrence risk of PCa patients after radical prostatectomy. Results A signature consisting of 74 gene pairs, named 74‐GPS, was developed for predicting the recurrence risk of PCa patients after radical prostatectomy based on the majority voting rule that a sample was assigned as high risk when at least 37 gene pairs of the 74‐GPS voted for high risk; otherwise, low risk. The signature was validated in six independent datasets produced by different platforms. In each of the validation datasets, the Kaplan‐Meier survival analysis showed that the average BFS of the low‐risk group was significantly better than that of the high‐risk group. Analyses of multiomics data of PCa samples from TCGA suggested that both the epigenomic and genomic alternations could cause the reproducible transcriptional differences between the two different prognostic groups. Conclusions The proposed qualitative transcriptional signature can robustly stratify PCa patients after radical prostatectomy into two groups with different recurrence risk and distinct multiomics characteristics. Hence, 74‐GPS may serve as a helpful tool for guiding the management of PCa patients with radical prostatectomy at the individual level.

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“…Besides, the qualitative signatures can be applied at the individual level [33]. Based on the within-sample relative expression orderings (REOs) of gene pairs, we have developed prognostic signatures for many cancer types [28,[34][35][36] and demonstrated their robustness in both interlaboratories and across-platforms tests [29,37].…”

Section: Introductionmentioning

confidence: 99%

A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients

Jiang

Liang

et al. 2020

BMC Genomics

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Background: Histological grade (HG) is commonly adopted as a prognostic factor for ER-positive breast cancer patients. However, HG evaluation methods, such as the pathological Nottingham grading system, are highly subjective with only 50-85% inter-observer agreements. Specifically, the subjectivity in the pathological assignment of the intermediate grade (HG2) breast cancers, comprising of about half of breast cancer cases, results in uncertain disease outcomes prediction. Here, we developed a qualitative transcriptional signature, based on within-sample relative expression orderings (REOs) of gene pairs, to define HG1 and HG3 and reclassify pathologically-determined HG2 (denoted as pHG2) breast cancer patients. Results: From the gene pairs with significantly stable REOs in pathologically-determined HG1 (denoted as pHG1) samples and reversely stable REOs in pathologically-determined HG3 (denoted as pHG3) samples, concordantly identified from seven datasets, we extracted a signature which could determine the HG state of samples through evaluating whether the within-sample REOs match with the patterns of the pHG1 REOs or pHG3 REOs. A sample was classified into the HG3 group if at least a half of the REOs of the 10 gene pairs signature within this sample voted for HG3; otherwise, HG1. Using four datasets including samples of early stage (I-II) ER-positive breast cancer patients who accepted surgery only, we validated that this signature was able to reclassify pHG2 patients into HG1 and HG3 groups with significantly different survival time. For the original pHG1 and pHG3 patients, the signature could also more accurately and objectively stratify them into distinct prognostic groups. And the up-regulated and down down-regulated genes in HG1 compared with HG3 involved in cell proliferation and extracellular signal transduction pathways respectively. By comparing with existing signatures, 10-GPS was with prognostic significance and was more aligned with survival of patients especially for pHG2 samples. Conclusions: The transcriptional qualitative signature can provide an objective assessment of HG states of ER-positive breast cancer patients, especially for reclassifying patients with pHG2, to assist decision making on clinical therapy.

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An individualized gene expression signature for prediction of lung adenocarcinoma metastases

Cited by 27 publications

References 65 publications

Unique metastasis‐associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma

Unique metastasis‐associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma

A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy

A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients

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