Abstract 243: Integration of transcriptome and metabolome provides unique insights to pathways associated with obese breast cancer patients
Bibliographic record
Abstract
Abstract Information regarding transcriptome and metabolome has significantly contributed to the identification of potential therapeutic targets for the management of a variety of cancers. Obesity has been shown to have profound effects on both cancer cell transcriptome and metabolome and to affect the outcome of cancer therapy. The information regarding the potential effects of obesity on breast cancer (BC) transcriptome, metabolome, and its integration to identify novel pathways related to disease progression are still elusive. We assessed the whole blood transcriptome and serum metabolome as circulating metabolites, of obese BC patients and compared them with non-obese BC patients. In these patients, 186 differentially expressed genes (DEGs) were observed, with 156 upregulated and 30 downregulated, significantly. The DEGs were enriched in different cellular pathways: cell cycle, one carbon pathway, homologous recombination cellular senescence, and notch signaling pathway. Our results confirmed the altered expression of several DEGs by quantitative real-time polymerase chain reaction (qRT-PCR). Furthermore, 96 deregulated metabolites were identified when untargeted metabolomics was performed in obese BC patients when compared to non-obese BC patients, these were enriched in 71 pathways, most of them involved in ATP generation and cell proliferation. Finally, to provide a more comprehensive understanding of the association between obesity and BC, integration analysis between transcriptome and metabolomics data at the pathway level, revealed seven enriched pathways in obese BC vs. non-obese BC patients, that includes glutathione metabolism, glycine and serine metabolism, valine, leucine, and isoleucine degradation, purine metabolism, pyrimidine metabolism, thyroid hormone synthesis, and vitamin B6 metabolism; which may provide resistance for BC cell to dodge the circulating immune cells in whole blood. In conclusion, this study provides information on the unique pathways alteration at transcriptome and metabolome levels in obese BC patients, which may become an important tool for researchers and contribute to a rise in the knowledge on the molecular interaction between obesity and BC. Further studies are needed to confirm this and to elucidate the exact underlying mechanism for the effects of obesity on the BC initiation or/and progression. Citation Format: Mohammed Abdullah Hassan, Kaltoom Al-Sakkaf, Mohammed Razeeth Shait Mohammed, Ashraf Dallol, Jaudah Al-Maghrabi, Alia Aldahlawi, Sawsan Ashoor, Mabrouka Maamra, Jiannis Ragoussis, Wei Wu, Mohammad Imran Khan, Abdulrahman Al-Malki, Hani Choudhry. Integration of transcriptome and metabolome provides unique insights to pathways associated with obese breast cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 243.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".