Dataset Information

Identification and subsequent validation of transcriptomic signature associated with metabolic status in endometrial cancer.

ABSTRACT: Aberrant metabolism has been identified as a main driver of cancer. Profiling of metabolism-related pathways in cancer furthers the understanding of tumor plasticity and identification of potential metabolic vulnerabilities. In this prospective controlled study, we established transcriptomic profiles of metabolism-related pathways in endometrial cancer (EC) using a novel method, NanoString nCounter Technology. Fifty-seven ECs and 30 normal endometrial specimens were studied using the NanoString Metabolic Panel, further validated by qRT-PCR with a very high similarity. Statistical analyses were by GraphPad PRISM and Weka software. The analysis identified 11 deregulated genes (FDR ≤ 0.05; |FC|≥ 1.5) in EC: SLC7A11; SLC7A5; RUNX1; LAMA4; COL6A3; PDK1; CCNA1; ENO1; PKM; NR2F1; and NAALAD2. Gene ontology showed direct association of these genes with 'central carbon metabolism (CCM) in cancer'. Thus, 'CCM in cancer' appears to create one of the main metabolic axes in EC. Further, transcriptomic data were functionally validated with drug repurposing on three EC cell lines, with several drug candidates suggested. These results lay the foundation for personalized therapeutic strategies in this cancer. Metabolic plasticity represents a promising diagnostic and therapeutic option in EC.

SUBMITTER: Sidorkiewicz I

PROVIDER: S-EPMC10447446 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Identification and subsequent validation of transcriptomic signature associated with metabolic status in endometrial cancer.

Sidorkiewicz Iwona I Jóźwik Maciej M Buczyńska Angelika A Erol Anna A Jóźwik Marcin M Moniuszko Marcin M Jarząbek Katarzyna K Niemira Magdalena M Krętowski Adam A

Scientific reports 20230823 1

Aberrant metabolism has been identified as a main driver of cancer. Profiling of metabolism-related pathways in cancer furthers the understanding of tumor plasticity and identification of potential metabolic vulnerabilities. In this prospective controlled study, we established transcriptomic profiles of metabolism-related pathways in endometrial cancer (EC) using a novel method, NanoString nCounter Technology. Fifty-seven ECs and 30 normal endometrial specimens were studied using the NanoString ...[more]

PMID: 37612452

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Dataset Information

Identification and subsequent validation of transcriptomic signature associated with metabolic status in endometrial cancer.

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Identification and subsequent validation of transcriptomic signature associated with metabolic status in endometrial cancer.

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