Sodium Overload-Related Molecular Subtypes and a Four-Gene Prognostic Signature Predict Survival, Immune Landscape, and Therapeutic Response in Acute Myeloid Leukemia.
Aug 2026· Molecular Carcinogenesis· Vol 65, pp. 1260-1280· 0 citations· 36 references
Medicine
TL;DR
A robust four-gene signature stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts, offering potential utility for personalized risk assessment and treatment optimization.
Abstract
Sodium overload has recently emerged as a critical metabolic stressor involved in cancer progression; however, its molecular characteristics and clinical relevance in acute myeloid leukemia (AML) remain unexplored. RNA-seq data sets, clinical annotations, and mutational profiles of AML patients were annotations from The Cancer Genome Atlas and integrated with Genotype-Tissue Expression normal samples. Sodium overload-related genes (SORGs) were obtained from GeneCards. Differentially expressed SORGs (DESORGs) screened by applying the limma statistical model, followed by univariate Cox proportional hazards regression, consensus clustering, functional enrichment, immune infiltration analysis, and pathway evaluation. A prognostic signature was developed through least absolute shrinkage and selection operator regression followed by multivariate Cox modeling. The model's performance was further verified in two external GEO data sets (GSE71014 and GSE37642). Nomogram construction, subgroup analysis, tumor mutational burden (TMB) assessment, drug sensitivity prediction, transcription factor (TF) analysis, and competing endogenous RNA (ceRNA) network analyses were also performed. A total of 57 DESORGs were identified, and 2 sodium overload-related molecular subtypes exhibited distinct survival, immune infiltration, and inflammatory pathway activation. A robust four-gene signature (DOCK1, GABRE, HTR7, ACSM1) stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts. High-risk patients displayed increased immune infiltration, higher TMB, reduced sensitivity to multiple chemotherapeutic drugs, and inferior predicted response to PD-L1 blockade. TF and ceRNA networks revealed multilayered transcriptional and post-transcriptional regulation of the signature genes. This study identifies sodium overload-related molecular heterogeneity in AML and establishes a validated four-gene prognostic signature that integrates genomic, immunologic, and therapeutic features, offering potential utility for personalized risk assessment and treatment optimization.
BACKGROUND
Acute myeloid leukemia (AML) features high biological heterogeneity and unfavorable prognoses, demanding reliable prognostic biomarkers. Dysregulated post-translational modifications (PTMs) drive AML progression by disrupting protein function and cellular signaling. This work constructed a PTM-based risk signature to predict AML survival and dissect the tumor immune microenvironment.
METHODS
RNA-seq and clinical data of TCGA-Acute Myeloid Leukemia (LAML) AML patients and normal genotype-tissue expression samples were analyzed. Limma identified differentially expressed genes (DEGs), and overlapping PTM-related DEGs were screened. Unsupervised consensus clustering stratified patients into molecular subgroups, whose overall survival (OS) was compared via Kaplan-Meier curves. A prognostic score model was built through univariate Cox screening, least absolute shrinkage and selection operator dimension reduction and multivariate Cox regression, validated by time-dependent receiver operating characteristic curves. Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and single-sample gene set enrichment analysis quantified immune infiltration, while tumor mutation burden (TMB) was calculated to characterize genomic features. The GSE71014 cohort served as external validation.
RESULTS
A four-gene signature (ITGAX, DOCK1, CPNE8, and GABRE) was established and validated. High-risk patients had markedly shorter OS, with 1-, 3-, and 5-year AUC values of 0.79, 0.79, and 0.90, consistent with external cohort results. The two risk groups displayed divergent immune landscapes; high-risk patients overexpressed multiple immune checkpoints. Though TMB was similar across groups, high-risk patients with low TMB had the worst survival. Low-risk patients showed greater cytarabine susceptibility, confirming the model's clinical value.
CONCLUSIONS
This PTM-associated signature accurately stratifies AML patients and reveals immune microenvironment disparities. It enables precise personalized prognosis and identifies PTM-related genes as promising therapeutic targets for AML immunotherapy.
D. Gan, Jin-fang Zeng, Jun Lin et al.· Japanese Journal of Clinical...· 0 citations
Acute myeloid leukemia (AML) exhibits heterogeneous outcomes and lacks reliable prognostic markers. As a critical regulator of cell fate, calcium signaling's prognostic value in AML requires investigation. This study aims to construct a calcium-related gene (CRG)-based prognostic model for AML. Differential analysis on RNA-seq data was conducted for AML from TCGA and GEO. Intersecting differentially expressed genes and CRGs yielded AML-associated differentially expressed CRGs (DECRGs). A prognostic model was developed through univariate/multivariate Cox regression and LASSO, and validated in a GEO dataset. Bioinformatics analyses explored the links between risk groups and immune characteristics, genomic mutations, and drug sensitivity. Key genes' effects on cell proliferation, apoptosis, and differentiation were verified in vitro using CCK-8 assay, colony formation assay, and flow cytometry. The 13-DECRG-based model distinguished high- and low-risk patients in both training and validation cohorts, with high-risk patients showing a worse prognosis. The risk score was an independent prognostic factor. Immune analysis revealed a unique immune microenvironment for the high-risk group. CAMK2A overexpression inhibited cell proliferation and colony-forming ability, promoted cell apoptosis, and induced an increased proportion of CD11b- and CD14-positive cells. In vitro experiments indicated CAMK2A-induced suppression of AML cells' malignant phenotype by activating the P53 signaling pathway. An AML CRG-based model with favorable risk stratification performance was constructed. In vitro experiments revealed CAMK2A-induced inhibition of the malignant phenotype via suppressing proliferation, promoting apoptosis, and facilitating myeloid differentiation in AML cells. This study provides novel evidence for understanding CRGs in AML as well as the potential functions of CAMK2A.
Jing Duan, Hu Zhang, Junjie Chu et al.· Experimental Hematology· 0 citations
The proposed glutathione metabolism-related signature demonstrated prognostic value in both training and validation cohorts and was associated with immune characteristics, pathway enrichment patterns, genomic alterations, and tumor mutation burden in LUAD.
Z. Sheng, Si-Yu Chen, Su Chen· Cancer Informatics· 0 citations
Colorectal cancer (CRC) exhibits marked molecular and microenvironmental heterogeneity, and the TNM staging system alone does not fully explain differences in prognosis or treatment response. Neutrophil extracellular traps (NETs) and oxidative stress participate in inflammatory remodeling, immune regulation, and tumor progression in CRC. However, transcriptomic studies integrating NET-related programs with oxidative stress-associated signaling for molecular stratification and prognostic modeling remain limited. Transcriptome and clinical data from TCGA-COADREAD (training set, 449 tumor and 39 normal samples) and GSE39582 (validation set, 566 tumor and 19 non-tumor samples) were analyzed. Candidate NET-related and oxidative stress-associated genes were collected from GeneCards, Gene Ontology, and published NETosis signatures, and were further restricted by differential expression analysis (|log2FC| > 0.585, adjusted P < .05) and univariate Cox analysis (P < .05). Consensus clustering, survival analysis, immune infiltration analysis, mutation analysis, and oncoPredict-based drug sensitivity modeling were performed. A multigene prognostic model was constructed using LASSO-Cox regression and evaluated in both cohorts. Two molecular subtypes were identified. C1 showed a better prognosis, higher tumor mutational burden, higher immune checkpoint and immunogenicity scores, and an immune-active phenotype. C2 showed poorer survival, enrichment of NET formation and inflammatory pathways, lower relative neutrophil infiltration by CIBERSORT, and predicted sensitivity to JAK, Wnt/β-catenin, proteasome, and cell-cycle checkpoint inhibitors. The prognostic model separated high- and low-risk patients in both the training and validation cohorts, with 1-, 3-, and 5-year area under the curves of 0.698, 0.711, and 0.645 in TCGA-COADREAD and 0.542, 0.578, and 0.569 in GSE39582. This study proposes NET-related CRC molecular subtypes with oxidative stress-associated biological context and develops a transcriptomic risk model with external validation. The findings suggest subtype-specific immune and therapeutic features, while prospective clinical and functional validation is required before clinical translation.
Yi Wei, Weijian Chu, Chunhui Rao et al.· Medicine· 0 citations
Public LUAD transcriptomes identified two inflammation-associated subtypes and a five-gene score comprising CHRDL1, FDCSP, CXCL13, CYP4B1, and S100P that separated survival groups and marked distinct proliferative and immune expression programs.
Xing-Chen Zhou, Zhen Le, Pengxia Song et al.· bioRxiv· 0 citations
BACKGROUND
Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking.
METHODS
Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines.
RESULTS
We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes.
CONCLUSIONS
We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.
Yu-Xin Liang, Xing Chen, Yuhao Su et al.· Gene· 0 citations
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