ArticleTranslational cancer research2026
Hypoxia-inducible factor-1α promotes the malignant progression of cervical cancer cells by regulating lactate dehydrogenase A-mediated glycolysis.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Enhanced glycolysis is a hallmark of metabolic reprogramming in cervical cancer and plays a key role in tumor progression. Hypoxia-inducible factor-1α (HIF-1α), a core regulator of glycolytic metabolism, remains incompletely characterized in cervical cancer. This study aimed to investigate the expression pattern and clinical significance of HIF-1α in cervical cancer, and to explore its association with malignant biological behavior and lactate dehydrogenase A (LDHA)-related glycolytic metabolism in cervical cancer cells. Methods: The expression level, clinicopathological features, immune infiltration correlation, and prognostic value of HIF-1α in cervical cancer were analyzed based on the The Cancer Genome Atlas (TCGA) database. HIF-1α overexpression and knockdown models were established in HeLa and Caski cells. Cell viability and invasive ability were assessed by Cell Counting Kit-8 (CCK-8) and Transwell assays, respectively. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot were used to detect changes in LDHA expression. Lactate production was measured using a lactate assay kit, and intracellular reactive oxygen species (ROS) levels were determined by flow cytometry. Results: Bioinformatics analysis showed that HIF-1α was highly expressed in cervical cancer and was closely associated with patient age, menopausal status, immune cell infiltration, and poor prognosis. Kaplan-Meier survival analysis demonstrated that patients with high HIF-1α expression had significantly worse overall survival (OS) than those with low expression. In vitro functional experiments further confirmed that HIF-1α overexpression significantly enhanced the viability and invasive ability of HeLa and Caski cells, whereas HIF-1α knockdown produced the opposite effects. HIF-1α overexpression was associated with increased messenger RNA (mRNA) and protein expression levels of LDHA, a key glycolytic molecule, along with increased lactate production and elevated intracellular ROS levels. Conclusions: HIF-1α is aberrantly highly expressed in cervical cancer and may enhance glycolytic activity by upregulating LDHA expression, thereby promoting the proliferation and invasion of cervical cancer cells. These findings suggest that HIF-1α could serve as a potential diagnostic, prognostic, and therapeutic target biomarker for cervical cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.