Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis
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Bibliographic record
Abstract
The prognosis of acute myeloid leukemia (AML) remains a challenge. In this study, we applied the weighted gene coexpression network analysis (WGCNA) to find survival-specific genes in AML based on 42 adult CN-AML samples from The Cancer Genome Atlas (TCGA) database. Eighteen hub genes (ABCA13, ANXA3, ARG1, BTNL8, C11orf42, CEACAM1, CEACAM3, CHI3L1, CRISP2, CYP4F3, GPR84, HP, LTF, MMP8, OLR1, PADI2, RGL4, and RILPL1) were found to be related to AML patient survival time. We then compared the hub gene expression levels between AML peripheral blood (PB) samples ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>n</a:mi> <a:mo>=</a:mo> <a:mn>162</a:mn> </a:math> ) and control healthy whole blood samples ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>n</c:mi> <c:mo>=</c:mo> <c:mn>337</c:mn> </c:math> ). Seventeen of the hub genes showed lower expression levels in AML PB samples. The gene expression analysis was also done among AML BM (bone marrow) samples of different stages: diagnosis ( <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>n</e:mi> <e:mo>=</e:mo> <e:mn>142</e:mn> </e:math> ), posttreatment ( <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>n</g:mi> <g:mo>=</g:mo> <g:mn>42</g:mn> </g:math> ), and recurrent ( <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mi>n</i:mi> <i:mo>=</i:mo> <i:mn>12</i:mn> </i:math> ) stages. The results showed a significant increase of ANXA3, CEACM1, RGL4, RILPL1, and HP in posttreatment samples compared to diagnosis and/or recurrent samples. Transcription factor (TF) prediction of the hub genes suggested LTF as the top hit, overlapping 10 hub genes, while LTF itself is just one of the hub genes. Also, 3671 correlation links were shown between 128 mRNAs and 209 lncRNAs found in survival time-related modules. Generally, we identified candidate mRNA biomarkers based on CN-AML data which can be extensively used in AML prognosis. In addition, we mapped their potential regulatory mechanisms with correlated lncRNAs, providing new insights into potential targets for therapies in AML.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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 it