Acute lymphoblastic leukemia (ALL) is a type of hematological malignancy and has a poor prognosis. In our study, we aimed to construct a prognostic model of ALL by identifying important genes closely related to ALL prognosis. We obtained transcriptome data (RNA-seq) of ALL samples from the GDC TARGET database and identified differentially expressed genes (DEGs) using the “DESeq” package of R software. We used univariate and multivariate cox regression analyses to screen out the prognostic genes of ALL. In our results, the risk score can be used as an independent prognostic factor to predict the prognosis of ALL patients [hazard ratio (HR) = 2.782, 95% CI = 1.903–4.068, p < 0.001]. Risk score in clinical parameters has high diagnostic sensitivity and specificity for predicting overall survival of ALL patients, and the area under curve (AUC) is 0.864 in the receiver operating characteristic (ROC) analysis results. Our study evaluated a potential prognostic signature with six genes and constructed a risk model significantly related to the prognosis of ALL patients. The results of this study can help clinicians to adjust the treatment plan and distinguish patients with good and poor prognosis for targeted treatment.

BACKGROUND: Acute myeloid leukemia (AML) has a poor prognosis, and the current 5-year survival rate is less than 30%. OBJECTIVE: The present study was designed to identify the significant genes closely related to AML prognosis and predict the prognostic value by constructing a risk model based on their expression. METHODS: Using bioinformatics (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, univariate and multivariate Cox regression analysis, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) analysis) to identify a prognostic gene signature for AML. Finally, The Cancer Genome Atlas (TCGA) database was used to validate this prognostic signature. RESULTS: Based on univariate and multivariate Cox regression analysis, eighteen prognostic genes were identified, and the gene signature and risk score model were constructed. Multivariate Cox analysis showed that the risk score was an independent prognostic factor [hazard ratio (HR) = 1.122, 95% confidence interval (CI) = 1.067–1.180, P< 0.001]. ROC analysis showed a high predictive value of the risk model with an area under the curve (AUC) of 0.705. CONCLUSIONS: This study evaluated a potential prognostic signature with eighteen genes and constructed a risk model significantly related to the prognosis of AML patients.

Therapy resistance remains a major cause of relapse and poor outcomes in acute lymphoblastic leukemia (ALL). Recent multi-omics studies in ALL have revealed that resistance arises from a combination of leukemia-specific genetic lesions, treatment-driven clonal evolution, and adaptive non-genetic programs. Genomic analyses have identified recurrent alterations associated with resistance to chemotherapy, tyrosine kinase inhibitors, and immunotherapies, while single-cell profiling has uncovered heterogeneous cell states that persist during treatment and contribute to minimal residual disease. Emerging epigenetic, proteomic, and metabolic data further indicate that reversible regulatory and signaling changes play a central role in leukemic persistence. Integrative analyses are beginning to define convergent resistance pathways and clinically relevant biomarkers, although longitudinal sampling and clinical translation remain limited. This review summarizes the current multi-omics landscape of therapy resistance in ALL and discusses opportunities to improve risk stratification and therapeutic strategies.

BACKGROUND: Although there are many treatments for Multiple myeloma (MM), patients with MM still unable to escape the recurrence and aggravation of the disease. OBJECTIVE: We constructed a risk model based on genes closely associated with MM prognosis to predict its prognostic value. METHODS: Gene function enrichment and signal pathway enrichment analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, univariate and multivariate Cox regression analysis, Kaplan-Meier (KM) survival analysis and Receiver Operating Characteristic (ROC) analysis were used to identify the prognostic gene signature for MM. Finally, the prognostic gene signature was validated using the Gene Expression Omnibus (GEO) database. RESULTS: Thirteen prognostic genes were screened by univariate Cox analysis and LASSO regression analysis. Multivariate Cox analysis revealed risk score to be an independent prognostic factor for patients with MM [Hazard Ratio (HR) = 2.564, 95% Confidence Interval (CI) = 2.223–2.958, P< 0.001]. The risk score had a high level of predictive value according to ROC analysis, with an area under the curve (AUC) of 0.744. CONCLUSIONS: The potential prognostic signature of thirteen genes were assessed and a risk model was constructed that significantly correlated with prognosis in MM patients.

Lymphoid malignancies comprise a diverse group of hematologic cancers characterized by extensive genetic, epigenetic, and microenvironmental heterogeneity. Despite substantial advances in targeted therapies and immunotherapeutic approaches, disease relapse and therapeutic resistance remain major clinical challenges. Increasing evidence suggests that malignant lymphoid cells are highly dependent on protein homeostasis (proteostasis) networks to cope with the elevated proteotoxic stress imposed by oncogenic signaling, rapid proliferation, immunoglobulin synthesis, and microenvironmental stressors. This dependence, often referred to as proteostasis addiction, represents a critical vulnerability that can be therapeutically exploited. Proteostasis is maintained through an integrated network that regulates protein synthesis, folding, quality control, and degradation. In lymphoid malignancies, dysregulation of these pathways drives adaptive responses involving molecular chaperones, the unfolded protein response (UPR), the ubiquitin–proteasome system (UPS), and autophagy–lysosome pathways. These mechanisms collectively enable tumor cells to survive conditions that would otherwise induce proteotoxic collapse and cell death. Notably, the clinical success of proteasome inhibitors in plasma cell neoplasms has provided proof of concept that targeting proteostasis can yield meaningful therapeutic benefit. In this review, we discuss the major sources of proteotoxic stress in lymphoid malignancies and summarize the molecular mechanisms that sustain proteostasis addiction. We further examine current and emerging therapeutic strategies aimed at disrupting proteostasis networks, including proteasome inhibitors, UPR-targeted agents, chaperone-directed therapies, and novel targeted protein degradation technologies. Finally, we highlight the contribution of proteostasis remodeling to therapeutic resistance and discuss future opportunities for biomarker development and precision medicine. A deeper understanding of proteostasis dependencies may facilitate the identification of novel therapeutic vulnerabilities and improve outcomes for patients with lymphoid malignancies.

The therapeutic landscape of acute myeloid leukemia (AML) has changed substantially over the past decade with the incorporation of molecularly targeted agents, lower-intensity venetoclax-based regimens, and increasingly refined genetic risk stratification. Among these advances, the combination of venetoclax with hypomethylating agents has become a pivotal treatment backbone for older or medically unfit patients with newly diagnosed AML, producing higher remission rates and longer survival than hypomethylating-agent monotherapy. However, venetoclax-based therapy is not curative for most patients. Primary resistance, incomplete eradication of leukemia stem and progenitor populations, lineage-associated drug tolerance, clonal evolution, and short survival after relapse have revealed major limitations of this approach. These challenges define the emerging post-venetoclax era of AML therapy. In this setting, treatment development is shifting from empiric intensification toward rational, biomarker-guided strategies that combine venetoclax-based backbones with targeted inhibitors of FLT3, IDH1/2, menin-dependent transcriptional programs, and other disease-specific vulnerabilities. At the same time, adverse-risk entities such as TP53-mutated AML continue to show limited benefit from venetoclax-based therapy, underscoring the need for alternative therapeutic frameworks. This review summarizes the clinical impact of venetoclax-based therapy, the biological and clinical mechanisms underlying treatment failure, and emerging strategies designed to improve outcomes after or beyond venetoclax exposure. We particularly highlight molecularly guided triplet regimens, menin inhibition, salvage approaches after hypomethylating-agent/venetoclax failure, measurable residual disease-guided treatment adaptation, and unresolved challenges in trial design, toxicity management, and patient selection. Understanding AML therapy through the lens of the post-venetoclax era may provide a practical framework for integrating targeted agents into precision treatment algorithms.