Evidence map›Paper›PMID 28036281›Full record

SynthesisOncotarget2017

The prognostic value of long noncoding RNA HOTTIP on clinical outcomes in breast cancer.

Yinlong Yang, Jinxian Qian, Youqun Xiang, Yizuo Chen, Jinmiao Qu

Open access · diamondAbstract readMeta-AnalysisReview
In one paragraph

Synthesis in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 pooled it
2.9field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. The Role of EMT-Related lncRNAs in Ovarian Cancer.International journal of molecular sciences · 2023
    Review
  10. Article
  11. Review
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  14. Review
  15. Long noncoding RNACell cycle (Georgetown, Tex.) · 2020
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Upregulation of the long non-coding RNA SPRY4-IT1 predicts poor prognosis in breast cancer.International journal of clinical and experimental pathology · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Yinlong YangDepartment of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200000, People's Republic of China.
Jinxian QianDepartment of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200000, People's Republic of China.
Youqun XiangDepartment of Surgical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, People's Republic of China.
Yizuo ChenDepartment of Surgical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, People's Republic of China.
Jinmiao QuDepartment of Surgical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, People's Republic of China.
First Affiliated Hospital of Wenzhou Medical University · CNFudan University Shanghai Cancer Center · CNWenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although a few studies have assessed the prognostic value of long noncoding RNA HOTTIP in patients with malignant tumors, the relationship between HOTTIP and clinical outcome of breast cancer remains elusive. The aim of this study is to explore the prognostic significance of HOTTIP in breast cancer patients. A meta-analysis was performed to involve the eligible studies to investigate the association of HOTTIP expression level with outcome in cancer patients. Pooled hazard ratios (HRs) and 95% confidence interval (CI) of HOTTIP for cancer survival were calculated. Five relevant articles involving 460 patients with various solid carcinomas were included in this meta-analysis. For overall survival, high HOTTIP expression could significantly predict worse outcome with the pooled HR of 2.29 (95 % CI 1.72-3.03, P < 0.00001). Furthermore, Gene Expression Omnibus was performed to evaluate the association of HOTTIP expression with the prognosis in breast cancer patients. It was also found an indication that high HOTTIP expression was associated with worse survival in breast cancer patients by microarray analysis (GSE20711, GSE16446 and GSE9195). Finally, association between HOTTIP levels and clinicopathological factors and prognosis was also analyzed in an independent validation cohort including 100 breast cancer cases. HOTTIP expression was correlated with tumor size (P=0.025), lymph node status (P=0.009) and TNM stage (P=0.0001) in the breast cancer validation cohort. The Kaplan-Meier survival curves indicated that breast cancer patients with high HOTTIP expression had worse overall survival (P=0.0139) and disease-free survival (P=0.0003). Multivariate survival analysis based on the Cox proportional hazards model showed that HOTTP is considered as an independent prognostic factor in breast cancer patients. Together, our combined results suggest that high HOTTIP expression may be serving as an unfavorable prognosis predictor for breast cancer patients.

Indexed as

Biomarkers, TumorBreast NeoplasmsChi-Square DistributionDisease-Free SurvivalDisease ProgressionFemaleGene Expression ProfilingHumansKaplan-Meier EstimateMultivariate AnalysisOdds RatioOligonucleotide Array Sequence AnalysisProportional Hazards ModelsRisk AssessmentRisk FactorsRNA, Long NoncodingBiomarkers, Tumorlong noncoding RNA HOTTIP, humanRNA, Long Noncodingbreast cancerHOTTIPlong non-coding RNAprognosis

Identifiers

PMID28036281
PMCPMC5351673
OpenAlexW2563849173

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.