Evidence map›Paper›PMID 35814833›Full record

ReviewOncology letters2022

Tumor metabolic reprogramming in lung cancer progression.

Xin Li, Minghui Liu, Hongyu Liu, Jun Chen

Open access · diamondAbstract readReview
In one paragraph

Review in Oncology letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
4.1field-weighted citation impact, top 4% 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

28 citing papers in PubMed, 49 citations in OpenAlex.

  1. Review
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  3. Single-cell and bulk transcriptomic profiling of metabolism-related lncRNAs reveals tumor heterogeneity and prognostic markers in non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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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

4 authors at 1 institution in 1 country.

Xin LiDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China.
Minghui LiuDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China.
Hongyu LiuTianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China.
Jun ChenDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China.
Tianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic reprogramming is an important characteristic of tumor cells. Tumor cells reprogram their metabolic pathways to meet the material, energy and redox force needs for rapid proliferation. Metabolic reprogramming changes the level or type of specific metabolites inside and outside cells, and promotes tumor growth by affecting gene expression, cell state and the tumor microenvironment. Glucose metabolism, glutamine metabolism and lipid metabolism are significant metabolic pathways in tumors. Targeting metabolic reprogramming can significantly inhibit tumor growth and induce apoptosis. Metabolic reprogramming also plays an important role in maintaining the growth advantage of tumor cells and enhancing the chemotherapy tolerance of lung cancer. This review summarizes abnormal changes in the metabolism of glucose, fat and amino acids in lung cancer, and the underlying molecular mechanism, with the aim of providing novel ideas for the prevention, early diagnosis and treatment of lung cancer.

Indexed as

lung cancermetabolic reprogramming

Identifiers

PMID35814833
PMCPMC9260716
OpenAlexW4283700577

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.