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Record W4379185249 · doi:10.3390/ijms24119649

The Curcumin Analog PAC Is a Potential Solution for the Treatment of Triple-Negative Breast Cancer by Modulating the Gene Expression of DNA Repair Pathways

2023· article· en· W4379185249 on OpenAlexaff
Esraa Almalki, Abdullah Alamri, Reem Alrashed, Mohammed Al‐Zharani, Abdelhabib Semlali

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsUniversité Laval
FundersAl-Imam Muhammad Ibn Saud Islamic UniversityImam Mohammed Ibn Saud Islamic University
KeywordsCurcuminCancerCancer researchBreast cancerApoptosisDNA repairCancer cellMetastasisCell growthMCF-7MTT assayChemotherapyBiologyProgrammed cell deathPharmacologyGeneBiochemistryGenetics

Abstract

fetched live from OpenAlex

Breast Cancer (BC) is one of the most common and challenging cancers among females worldwide. Conventional treatments for oral cancer rely on the use of radiology and surgery accompanied by chemotherapy. Chemotherapy presents many side effects, and the cells often develop resistance to this chemotherapy. It will be urgent to adopt alternative or complementary treatment strategies that are new and more effective without these negative effects to improve the well-being of patients. A substantial number of epidemiological and experimental studies reported that many compounds are derived from natural products such as curcumin and their analogs, which have a great deal of beneficial anti-BC activity by inducing apoptosis, inhibiting cell proliferation, migration, and metastasis, modulating cancer-related pathways, and sensitizing cells to radiotherapy and chemotherapy. In the present study, we investigated the effect of the curcumin-analog PAC on DNA repair pathways in MCF-7 and MDA-MB-231 human breast-cancer cell lines. These pathways are crucial for genome maintenance and cancer prevention. MCF-7 and MDA-MB-231 cells were exposed to PAC at 10 µM. MTT and LDH assays were conducted to evaluate the effects of PAC on cell proliferation and cytotoxicity. Apoptosis was assessed in breast cancer cell lines using flow cytometry with annexin/Pi assay. The expression of proapoptotic and antiapoptotic genes was determined by RT-PCR to see if PAC is active in programming cell death. Additionally, DNA repair signaling pathways were analyzed by PCR arrays focusing on genes being related and confirmed by quantitative PCR. PAC significantly inhibited breast-cancer cell proliferation in a time-dependent manner, more on MDA-MB-231 triple-negative breast cancer cells. The flow cytometry results showed an increase in apoptotic activity. These data have been established by the gene expression and indicate that PAC-induced apoptosis by an increased Bax and decreased Bcl-2 expression. Moreover, PAC affected multiple genes involved in the DNA repair pathways occurring in both cell lines (MCF-7 and MDA-MB231). In addition, our results suggest that PAC upregulated more than twice 16 genes (ERCC1, ERCC2, PNKP, POLL, MPG, NEIL2, NTHL1, SMUG1, RAD51D, RAD54L, RFC1, TOP3A, XRCC3, XRCC6BP1, FEN1, and TREX1) in MDA-MB-231, 6 genes (ERCC1, LIG1, PNKP, UNG, MPG, and RAD54L) in MCF-7, and 4 genes (ERCC1, PNKP, MPG, and RAD54L) in the two cell lines. In silico analysis of gene-gene interaction shows that there are common genes between MCF-7 and MDA-MB-321 having direct and indirect effects, among them via coexpression, genetic interactions, pathways, predicted and physical interactions, and shared protein domains with predicted associated genes indicating they are more likely to be functionally related. Our data show that PAC increases involvement of multiple genes in a DNA repair pathway, this certainly can open a new perspective in breast-cancer treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.016
GPT teacher head0.285
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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