Evidence map›Paper›PMID 38664521›Full record

ArticleScientific reports2024

Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma.

Xiaoge Gao, Xin Ren, Feitong Wang, Xinxin Ren, Mengchen Liu, Guozhen Cui, Xiangye Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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
3.8field-weighted citation impact, top 6% 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, 9 citations in OpenAlex.

  1. Food and waterborne parasitology · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. 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

7 authors at 3 institutions in 1 country.

Xiaoge Gao *Cancer Institute, Xuzhou Medical University, Xuzhou, 221002, Jiangsu Province, People's Republic of China.
Xin Ren *Cancer Institute, Xuzhou Medical University, Xuzhou, 221002, Jiangsu Province, People's Republic of China.
Feitong WangDepartment of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu Province, People's Republic of China.
Xinxin RenSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, People's Republic of China.
Mengchen LiuSchool of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519040, Guangdong Province, People's Republic of China.
Guozhen CuiSchool of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519040, Guangdong Province, People's Republic of China.
Xiangye LiuJiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Xuzhou, 221004, Jiangsu Province, People's Republic of China. liuxy83@xzhmu.edu.cn.
Xuzhou Medical College · CNZunyi Medical University · CNAnhui Agricultural University · CN

Funding

Integration and innovation project of Xuzhou Medical University XYRHCX2022004Key Research Project of Xuzhou City KC22114National Natural Science Foundation of China 81802389National Natural Science Foundation of China 81972242Research Foundation of Xuzhou Medical University D2015006
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is one of the most significant causes of cancer-related deaths in the worldwide. Currently, predicting the survival of patients with HCC and developing treatment drugs still remain a significant challenge. In this study, we employed prognosis-related genes to develop and externally validate a predictive risk model. Furthermore, the correlation between signaling pathways, immune cell infiltration, immunotherapy response, drug sensitivity, and risk score was investigated using different algorithm platforms in HCC. Our results showed that 11 differentially expressed genes including UBE2C, PTTG1, TOP2A, SPP1, FCN3, SLC22A1, ADH4, CYP2C8, SLC10A1, F9, and FBP1 were identified as being related to prognosis, which were integrated to construct a prediction model. Our model could accurately predict patients' overall survival using both internal and external datasets. Moreover, a strong correlation was revealed between the signaling pathway, immune cell infiltration, immunotherapy response, and risk score. Importantly, a novel potential drug candidate for HCC treatment was discovered based on the risk score and also validated through ex vivo experiments. Our finds offer a novel perspective on prognosis prediction and drug exploration for cancer patients.

Indexed as

Carcinoma, HepatocellularImmunotherapyLiver NeoplasmsBiomarkers, TumorDrug Resistance, NeoplasmGene Expression Regulation, NeoplasticHumansPrognosisSignal TransductionBiomarkers, TumorDrug candidateHepatocellular carcinomaImmunotherapy responsePrognosis prediction

Identifiers

PMID38664521
PMCPMC11045740
OpenAlexW4395464379

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.