Evidence map›Paper›PMID 37434012›Full record

ArticleCellular oncology (Dordrecht, Netherlands)2023

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer.

Guangyu Chen, Yueze Liu, Dan Su, Jiangdong Qiu, Junyu Long, Fangyu Zhao, Jinxin Tao, Gang Yang, Hua Huang, Jianchun Xiao and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cellular oncology (Dordrecht, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
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

12 authors at 2 institutions in 1 country.

Guangyu Chen *General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Yueze Liu *General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Dan Su *General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Jiangdong QiuGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Junyu LongNational Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, People's Republic of China.
Fangyu ZhaoGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Jinxin TaoGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Gang YangGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Hua HuangGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Jianchun XiaoGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Taiping ZhangGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. tpingzhang@yahoo.com.
Yupei ZhaoGeneral Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. zhao8028@263.net.
Peking Union Medical College Hospital · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

CAMS Innovation Fund for Medical Sciences (CIFMS) (2021-1-I2M-002)China Postdoctoral Science Foundation 2021T140071 and 2021M690462National High Level Hospital Clinical Research Funding 2022-PUMCH-A-245National High Level Hospital Clinical Research Funding 2022-PUMCH-D-001National Key R&D Program of China 2018YFE0118600National Natural Science Foundation of China No.81972258, No.81974376, No. 82103016, No. 82172836, No.82272917Natural Science Foundation of Beijing No. 7192157Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences 2018PT32014Youth Research Fund of Peking Union Medical College Hospital pumch201911710, pumch201910819
6 · The paper itself

Abstract

purposePatients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer

methodsUnsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC

resultsHigh-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids

conclusionThe risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

Indexed as

GenomicsPancreatic NeoplasmsHumansNeoplasm ProteinsOncogenesPrognosisTumor MicroenvironmentFAM83A protein, humanNeoplasm ProteinsImmuneMetabolismPancreatic cancerPrognosisSubtypeTumor microenvironment

Identifiers

PMID37434012
PMCPMC12974763
OpenAlexW4383903911

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.