Evidence map›Paper›PMID 38093163›Full record

ArticleDiscover oncology2023

Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma.

Shiming Yi, Chunlei Zhang, Ming Li, Tianyi Qu, Jiafeng Wang

Open access · goldAbstract read
In one paragraph

Article in Discover oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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.

Shiming YiDepartment of Hepatobiliary Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China.
Chunlei ZhangDepartment of Colorectal and Anus Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China.
Ming LiDepartment of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China.
Tianyi QuEmergency Department, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China.
Jiafeng WangDepartment of Hepatobiliary Surgery, the Affiliated Taian City Central Hospital of Qingdao University, Taian, China. doctorwangjf@126.com.
Binzhou University · CNBinzhou Medical University · CNTaian City Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning techniques have been widely used in predicting disease prognosis, including cancer prognosis. One of the major challenges in cancer prognosis is to accurately classify cancer types and stages to optimize early screening and detection, and machine learning techniques have proven to be very useful in this regard. In this study, we aimed at identifying critical genes for diagnosis and outcomes of hepatocellular carcinoma (HCC) patients using machine learning. The HCC expression dataset was downloaded from GSE65372 datasets and TCGA datasets. Differentially expressed genes (DEGs) were identified between 39 HCC and 15 normal samples. For the purpose of locating potential biomarkers, the LASSO and the SVM-RFE assays were performed. The ssGSEA method was used to analyze the TCGA to determine whether there was an association between SPINK1 and tumor immune infiltrates. RT-PCR was applied to examine the expression of SPINK1 in HCC specimens and cells. A series of functional assays were applied to examine the function of SPINK1 knockdown on the proliferation of HCC cells. In this study, 103 DEGs were obtained. Based on LASSO and SVM-RFE analysis, we identified nine critical diagnostic genes, including C10orf113, SPINK1, CNTLN, NRG3, HIST1H2AI, GPRIN3, SCTR, C2orf40 and PITX1. Importantly, we confirmed SPINK1 as a prognostic gene in HCC. Multivariate analysis confirmed that SPINK1 was an independent prognostic factor for overall survivals of HCC patients. We also found that SPINK1 level was positively associated with Macrophages, B cells, TFH, T cells, Th2 cells, iDC, NK CD56bright cells, Th1 cells, aDC, while negatively associated with Tcm and Eosinophils. Finally, we demonstrated that SPINK1 expression was distinctly increased in HCC specimens and cells. Functionally, silence of SPINK1 distinctly suppressed the proliferation of HCC cells via regulating Wnt/β-catenin pathway. The evidence provided suggested that SPINK1 may possess oncogenic properties by inducing dysregulated immune infiltration in HCC. Additionally, SPINK1 was identified as a novel biomarker and therapeutic target for HCC.

Indexed as

BiomarkerDiagnosisHepatocellular carcinomaImmune infiltrationMachine learningSPINK1Wnt/β-catenin pathway

Identifiers

PMID38093163
PMCPMC10719188
OpenAlexW4389740621

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.