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Record W3128286650 · doi:10.21203/rs.3.rs-59534/v1

The Possible Roles of Long Non-Coding RNAs FOXD2-AS1 and LINC00968 in Breast Cancer: Case-Control Study and in Silico Analysis

2020· preprint· en· W3128286650 on OpenAlexfundno aff
Maedeh Arabpour, Sepideh Mehrpour Layeghi, Keivan Majidzadeh‐A, Javad Tavakkoly‐Bazzaz, Mohammad Naghizadeh, Abbas Shakoori

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsnot available
FundersBeef Cattle Research CouncilAcademic Center for Education, Culture and ResearchTehran University of Medical Sciences and Health Services
KeywordsIn silicoBreast cancerCoding (social sciences)Computational biologyBiologyCancerOncologyBioinformaticsGeneticsMedicineStatisticsMathematicsGene

Abstract

fetched live from OpenAlex

Abstract Background: Breast Cancer (BC) is the most common cancer in women worldwide. Long non-coding RNAs (lncRNAs) are regulatory non-coding transcripts and longer than 200 nucleotides. lncRNAs can affect many biological and pathological processes and dysregulation of them is related and studied in many human diseases like, cancers. We performed this study to evaluate the probable functions of lncRNAs FOXD2-AS1 and LINC00968 in breast cancer. Methods: The tumor tissue and adjacent non-tumor tissue specimens of luminal A and B breast cancer (the most frequent subtypes of breast cancer) were used to analyze the expression of these two lncRNAs, using the qRT-PCR technique. Furthermore, two luminal A breast cancer cell lines, MCF7 and T47D, were used to evaluate the expression of FOXD2-AS1 and LINC00968 compared with control breast cell line, MCF10A. Because of the luminal subtypes are the most frequent subtypes of breast cancer and also the most common subtypes among breast cancer patients, the present study was generally focused on the luminal and breast cancer. Also, some in silico analyses were done according to some databases and software for better understanding about the potential functions of mentioned lncRNAs in breast cancer. Results: Our data indicated the significant upregulation and downregulation of FOXD2-AS1 and LINC00968, respectively, in tumor tissues and breast cancer cell lines (MCF7, T47D). The bioinformatic analyses confirmed the experimental results. FOXD2-AS1 expression was positively associated with p53 protein and LINC00968 expression was negatively associated with tumor stage and lymph node metastasis. According to our findings, LINC00968 might be function as a tumor suppressor gene in breast cancer and this lncRNA might function in some cellular signaling pathways, like PI3K/Akt and Ras signaling pathways based on the co-expressed genes, but more investigations are required. Conclusions: The two mentioned lncRNAs might play roles in breast cancer pathogenesis but more experimental studies are needed to explore the mechanisms of the functions of these two lncRNAs in breast cancer.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.021
GPT teacher head0.365
Teacher spread0.344 · 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 designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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