Evidence map›Paper›PMID 36803349›Full record

ArticleBMC gastroenterology2023

A comprehensive computational analysis to explore the importance of SIGLECs in HCC biology.

Senbang Yao, Wenjun Chen, Tingting Chen, He Zuo, Ziran Bi, Xiuqing Zhang, Lulian Pang, Yanyan Jing, Xiangxiang Yin, Huaidong Cheng

Open access · goldAbstract read
In one paragraph

Article in BMC gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 31% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. 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

10 authors at 3 institutions in 1 country.

Senbang YaoDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
Wenjun ChenDepartment of Oncology, Anhui Medical University, Hefei, Anhui, China.
Tingting ChenDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
He ZuoDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
Ziran BiDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
Xiuqing ZhangDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
Lulian PangDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
Yanyan JingDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
Xiangxiang YinDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
Huaidong ChengDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China. chd1975ay@126.com.
Second Affiliated Hospital of Anhui Medical University · CNAnhui Medical University · CNSouthern Medical University Shenzhen Hospital · CN

Funding

National Natural Science Foundation of China 81872504
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is an aggressive, malignant cancer with a complex pathogenesis. However, effective therapeutic targets and prognostic biomarkers are limited. Sorafenib provides delaying cancer progression and survival improvement in advanced HCC. But despite 10 years of research on the clinical application of sorafenib, predictive markers for its therapeutic effect are lacking.

methodsThe clinical significance and molecular functions of SIGLEC family members were assessed by a comprehensive bioinformatic analysis. The datasets included in this study (ICGC-LIRI-JP, GSE22058 and GSE14520) are mainly based on patients with HBV infections or HBV-related liver cirrhosis. The TCGA, GEO, and HCCDB databases were used to explore the expression of SIGLEC family genes in HCC. The Kaplan-Meier Plotter database was used to evaluate relationships between the expression levels of SIGLEC family genes and prognosis. Associations between differentially expressed genes in the SIGLEC family and tumour-associated immune cells were evaluated using TIMER.

resultsThe mRNA levels of most SIGLEC family genes were significantly lower in HCC than in normal tissues. Low protein and mRNA expression levels of SIGLECs were strongly correlated with tumour grade and clinical cancer stage in patients with HCC. Tumour-related SIGLEC family genes were associated with tumour immune infiltrating cells. High SIGLEC expression was significantly related to a better prognosis in patients with advanced HCC treated with sorafenib.

conclusionsSIGLEC family genes have potential prognostic value in HCC and may contribute to the regulation of cancer progression and immune cell infiltration. More importantly, our results revealed that SIGLEC family gene expression may be used as a prognostic marker for HCC patients treated with sorafenib.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsBiomarkers, TumorClinical RelevanceComputational BiologyHumansPrognosisSorafenibBiomarkers, TumorSorafenibHepatocellular carcinomaImmunotherapyPrognostic markersSorafenibTumour progression

Identifiers

PMID36803349
PMCPMC9938566
OpenAlexW4321328702

What Socratic 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 Socratic graph.