MétaCan
Menu
Back to cohort
Record W4392714538 · doi:10.1186/s40001-024-01745-3

An increase in SNHG5 expression is associated with poor cancer prognosis, according to a meta-analysis

2024· article· en· W4392714538 on OpenAlexaboutno aff
Qiang Huang, Yi-gui Xia, Yongjian Huang, Haifeng Qin, Qun-xian Zhang, Chun-feng Wei, Wu-ru Tang, Yuan Liao

Bibliographic record

VenueEuropean journal of medical research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHazard ratioOncologyOdds ratioMeta-analysisInternal medicineConfidence intervalCancer

Abstract

fetched live from OpenAlex

BACKGROUND: He long noncoding RNA small nucleolar host RNA 5 (SNHG5) is highly expressed in many cancers, and there is a notable correlation between the elevated expression of SNHG5 and survival outcome in cancer patients. The objective of this study was to conduct a meta-analysis to evaluate the correlation between SNHG5 expression and the clinical outcome of cancer patients. METHODS: Six relevant electronic databases were exhaustively searched, and, depending on the inclusion and exclusion criteria, appropriate literature was obtained. The Newcastle-Ottawa Scale (NOS) score was utilized to evaluate the quality of the research for every article included, and pertinent data from each study were carefully extracted. Hazard ratios (HRs), odds ratios (ORs) and 95% confidence intervals (CIs) were combined to explore the association of SNHG5 expression levels with cancer prognosis, and sensitivity analyses and assessments of publication bias were also conducted to investigate any possibility in the publication of the studies. RESULTS: Eleven studies encompassing 721 patients were ultimately collected. When combined, the hazard ratios (HRs) revealed a substantial direct correlation between elevated SNHG5 expression and an unfavourable prognosis for cancer patients (HR = 1.90, 95% CI 0.87-4.15); however, the correlation did not reach statistical significance. Furthermore, high SNHG5 expression was predictive of advanced TNM stage (OR: 1.988, 95% CI 1.205-3.278) and larger tumour size (OR: 1.571, 95% CI 1.090-2.264); moreover, there were nonsignificant relationships between SNHG5 expression and DM (OR: 0.449, 95% CI 0.077-2.630), lymph node metastasis (OR: 1.443, 95% CI 0.709-2.939), histological grade (OR: 2.098, 95% CI 0.910-4.838), depth of invasion (OR: 1.106, 95% CI 0.376-3.248), age (OR: 0.946, 95% CI 0.718-1.247) and sex (OR: 0.762, 95% CI 0.521-1.115). CONCLUSION: SNHG5 expression is typically increased in the majority of tumour tissues. Elevated SNHG5 expression may indicate poor prognosis in cancer patients. Therefore, SNHG5 is a promising potential therapeutic target for tumours and a reliable prognostic biomarker.

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)0.013
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.039
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.091
GPT teacher head0.431
Teacher spread0.341 · 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 designMeta-analysis
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEuropean journal of medical researchSame topicCancer-related molecular mechanisms researchFrench-language works237,207