Evidence map›Paper›PMID 29278873›Full record

ArticleCancer biomarkers : section A of Disease markers2018

Increased high-mobility group A2 correlates with lymph node metastasis and prognosis of non-small cell lung cancer.

Xiangjun Guo, Jiaxin Shi, Yan Wen, Mengmeng Li, Qin Li, Xiaomei Li, Jiashu Li

Abstract read
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Article in Cancer biomarkers : section A of Disease markers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

7 authors.

Xiangjun Guo
Jiaxin Shi
Yan Wen
Mengmeng Li
Qin Li
Xiaomei Li
Jiashu Li

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh-mobility group A2 (HMGA2) has been investigated to be associated with tumorigenesis; however, the expression pattern and clinical significance of HMGA2 in non-small cell lung cancer (NSCLC) remains poorly understood. The purpose of this study is to examine the expression of HMGA2 and to analyze its relationships with respect to clinico-pathological features and patient survival in NSCLC.

methodsThe expression level of HMGA2 was examined by Western blot and immunohistochemistry in NSCLC cells and tissues. The relationship between HMGA2 expression and survival of NSCLC patients was calculated by a Kaplan-Meier method and the evaluation of risk factor was determined by the multiple regression analysis.

resultsNSCLC tissues exhibited a higher expression level of HMGA2 compared to normal tissues (p< 0.05) and the expression level of HMGA2 was significantly associated with poor differentiation of NSCLC (p< 0.05), lymph node metastasis (p< 0.05) and advanced clinical stage (p< 0.05). Besides, HMGA2 was also confirmed to be elevated in NSCLC cells by Western blot. Moreover, increased expression of HMGA2 correlated with decreased survival of NSCLC patients (p< 0.05).

conclusionsHMGA2 was highly expressed in NSCLC tissues and cells and its overexpression was correlated with low-grade differentiation, lymph node metastasis, advanced clinical stage and poor survival time of NSCLC, which suggested that it could serve as a potential molecular marker and prognostic index for NSCLC.

Indexed as

Biomarkers, TumorAdultAgedCarcinoma, Non-Small-Cell LungCell Line, TumorFemaleHMGA2 ProteinHumansImmunohistochemistryKaplan-Meier EstimateLung NeoplasmsMaleMiddle AgedNeoplasm GradingNeoplasm MetastasisNeoplasm StagingBiomarkers, TumorHMGA2 Proteinexpressionhigh-mobility group A2HMGA2Non-small cell lung cancerNSCLCprognosis

Identifiers

PMID29278873
PMCPMC13078311
OpenAlexW2780924146

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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.