Evidence map›Paper›PMID 29103025›Full record

SynthesisCancer biomarkers : section A of Disease markers2018

Integrated analysis reveals down-regulation of SPARCL1 is correlated with cervical cancer development and progression.

Dong-Ming Wu, Jiao Shi, Teng Liu, Shi-Hua Deng, Rong Han, Ying Xu

Abstract readMeta-Analysis
In one paragraph

Synthesis 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 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

13 citing papers in PubMed, 27 citations in OpenAlex.

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  7. The Clinical Significance and Biological Function ofCancer management and research · 2021
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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

6 authors.

Dong-Ming Wu
Jiao Shi
Teng Liu
Shi-Hua Deng
Rong Han
Ying Xu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cervical cancer is the fourth most common malignancy among women worldwide, and continued research to discover biomarkers or therapeutic targets will aid early diagnosis and treatment of this cancer. Here, we investigated novel cervical cancer biomarkers using integrated analysis of high-throughput sequencing data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We have identified nine genes of interest that appear to be involved in cervical cancer development: SPARCL1, SYCP2, KIF4A, PRC1, TOP2A, LAMP3, KIF20A, MCM2, and APOBEC3B. Furthermore, gene ontology (GO) and co-expression analysis of these differentially expressed genes indicated that SPARCL1 may play a core role in cervical cancer development. Further, we analyzed the expression of these nine genes during the progression of cervical cancer, and found that SPARCL1 is also related to precancerous lesions and migration processes during cervical cancer pathogenesis. Finally, we validated these observations by investigating SPARCL1 expression in cervical cancer tissue and serum samples. The diagnostic specificity of serum SPARCL1 in cervical cancer occurrence was also compared with other high incidence diseases. All of these data indicate that SPARCL1 may be a novel cancer predictive marker and a potential therapeutic target for tumor development and progression in cervical cancer.

Indexed as

Biomarkers, TumorCalcium-Binding ProteinsDisease ProgressionDown-RegulationExtracellular Matrix ProteinsFemaleHumansUterine Cervical NeoplasmsBiomarkers, TumorCalcium-Binding ProteinsExtracellular Matrix ProteinsSPARCL1 protein, humanCervical cancermarkerSPARCL1

Identifiers

PMID29103025
PMCPMC13078267
OpenAlexW2767003118

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.