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Record W3205576405 · doi:10.1186/s12935-021-02201-6

Huanglianjiedu Decoction as an effective treatment for oral squamous cell carcinoma based on network pharmacology and experimental validation

2021· article· en· W3205576405 on OpenAlexaff
Lejun Zhang, Zhaoting Ling, Zhengqiang Hu, Guanmin Meng, Xinqiang Zhu, Huifang Tang

Bibliographic record

VenueCancer Cell International · 2021
Typearticle
Languageen
FieldMedicine
TopicFlavonoids in Medical Research
Canadian institutionsUniversity of Alberta
FundersZhejiang UniversityNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsScutellaria baicalensisCoptis chinensisKEGGGardenia jasminoidesCell cycleMAPK/ERK pathwayCell growthPI3K/AKT/mTOR pathwayTraditional medicineCoptisApoptosisCancer researchBiologyPTENKinaseMedicineTraditional Chinese medicineTranscriptomeGeneCell biologyGene expressionGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Oral squamous cell carcinoma (OSCC) is one of malignant tumors in oral and maxillofacial region with high fatality. Huanglianjiedu Decoction (HLJDD) is a well-known traditional Chinese medicinal prescription, which consists of Coptis chinensis Franch, Scutellaria baicalensis Georgi, Phellodendron amurense Rupr and Gardenia jasminoides J.Ellis. Some clinical studies showed HLJDD had good effectiveness on OSCC, but the mechanism is unclear. METHODS: In this study, potential components of HLJDD and putative targets were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Combining with potential targets of OSCC searched from Therapeutic Target Database (TTD) and Online Mendelian Inheritance in Man (OMIM), we drew protein-protein interaction (PPI) network by Cytoscape v3.2.0 software. After topological analysis we got core targets and further did Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then we did the in vitro experiments to verify the major biological processes (cell cycle, apoptosis and proliferation) and signaling pathways (mitogen-activated protein kinase (MAPK), nuclear factor-kappa B (NF-κB), protein kinase B (AKT)) on OSCC cell lines, SCC-25 and CAL-27. RESULTS: The potential component targets number of Coptis chinensis Franch, Scutellaria baicalensis Georgi, Phellodendron amurense Rupr and Gardenia jasminoides J.Ellis were 39, 93, 81and 88, respectively. Then we got 52 core targets which enriched in cell cycle, apoptosis, proliferation, MAPK activation etc. and obtained TOP30 pathways. On SCC-25 and CAL-27, HLJDD suppressed cell proliferation, induced late apoptosis and inhibited cell invasion and migration which were consistent with the results from network pharmacology analysis. Additionally, in cell cycle, we confirmed HLJDD inhibited G1 phase and arrested in S phase to reduce cell proliferation on SCC-25. In signaling pathways, HLJDD inhibited the phosphorylation of extracellular regulatory protein kinase 1/2 (ERK1/2) and NF-κB p65 (S468) on SCC-25 and CAL-27. CONCLUSIONS: HLJDD played a potential therapeutic role on OSCC via inhibiting p-ERK1/2 and p-NF-κB p65 (S468).

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.413
Teacher spread0.380 · 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
Published2021
Admission routes1
Has abstractyes

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