PLCB4 upregulation is associated with unfavorable prognosis in pediatric acute myeloid leukemia

Decoding the Possible Molecular Mechanisms in Pediatric Wilms Tumor and Rhabdoid Tumor of the Kidney through Machine Learning Approaches

Objective: Wilms tumor (WT) and Rhabdoid tumor (RT) are pediatric renal tumors and their differentiation is based on histopathological and molecular analysis. The present study aimed to introduce the panels of mRNAs and microRNAs involved in the pathogenesis of these cancers using deep learning algorithms. Methods: Filter, graph, and association rule mining algorithms were applied to the mRNAs/microRNAs data. Results: Candidate miRNAs and mRNAs with high accuracy (AUC: 97%/93% and 94%/97%, respectively) could differentiate the WT and RT classes in training and test data. Let-7a-2 and C19orf24 were identified in the WT, while miR-199b and RP1-3E10.2 were detected in the RT by analysis of Association Rule Mining. Conclusion: The application of the machine learning methods could identify mRNA/miRNA patterns to discriminate WT from RT. The identified miRNAs/mRNAs panels could offer novel insights into the underlying molecular mechanisms that are responsible for the initiation and development of these cancers. They may provide further insight into the pathogenesis, prognosis, diagnosis, and molecular-targeted therapy in pediatric renal tumors.

Development and validation of prognostic models for colon adenocarcinoma based on combined immune-and metabolism-related genes

Background

The heterogeneity of tumor tissue is one of the reasons for the poor effect of tumor treatment, which is mainly affected by the tumor immune microenvironment and metabolic reprogramming. But more research is needed to find out how the tumor microenvironment (TME) and metabolic features of colon adenocarcinoma (COAD) are related.

Methods

We obtained the transcriptomic and clinical data information of COAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Consensus clustering analysis was used to identify different molecular subtypes, identify differentially expressed genes (DEGs) associated with immune-and metabolism-related genes (IMRGs) prognosis. Univariate and multivariable Cox regression analysis and Lasso regression analysis were applied to construct the prognostic models based on the IMRG risk score. The correlations between risk scores and TME, immune cell infiltration, and immune checkpoint genes were investigated. Lastly, potential appropriate drugs related to the risk score were screened by drug sensitivity analysis.

Results

By consensus clustering analysis, we identified two distinct molecular subtypes. It was also found that the multilayered IMRG subtypes were associated with the patient's clinicopathological characteristics, prognosis, and TME cell infiltration characteristics. Meanwhile, a prognostic model based on the risk score of IMRGs was constructed and its predictive power was verified internally and externally. Clinicopathological analysis and nomogram give it better clinical guidance. The IMRG risk score plays a key role in immune microenvironment infiltration. Patients in the high-risk groups of microsatellite instability (MSI) and tumor mutational burden (TMB) were found to, although with poor prognosis, actively respond to immunotherapy. Furthermore, IMRG risk scores were significantly associated with immune checkpoint gene expression. The potential drug sensitivity study helps come up with and choose a chemotherapy treatment plan.

Conclusion

Our comprehensive analysis of IMRG signatures revealed a broad range of regulatory mechanisms affecting the tumor immune microenvironment (TIME), immune landscape, clinicopathological features, and prognosis. And to explore the potential drugs for immunotherapy. It will help to better understand the molecular mechanisms of COAD and provide new directions for disease treatment.

Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

Background: The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. Latestresearch hasdisplayed that tumor immune cell infiltration (ICI) is associated with the clinical outcome of patients with osteosarcoma (OS). This work aimed to build a gene signature according to ICI in OS for predicting patient outcomes.

Methods: The TARGET-OS dataset was used for model training, while the GSE21257 dataset was taken forvalidation. Unsupervised clustering was performed on the training cohort based on the ICI profiles. The Kaplan–Meier estimator and univariate Cox proportional hazards models were used to identify the differentially expressed genes between clusters to preliminarily screen for potential prognostic genes. We incorporated these potential prognostic genes into a LASSO regression analysis and produced a gene signature, which was next assessed with the Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS in the training and validation cohorts. In addition, we compared our signature to previous models. GSEAswere deployed to further study the functional mechanism of the signature. We conducted an analysis of 22 TICsfor identifying the role of TICs in the gene signature’s prognosis ability.

Results: Data from the training cohort were used to generate a nine-gene signature. The Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS validated the signature’s capacity and independence in predicting the outcomes of OS patients in the validation cohort. A comparison with previous studies confirmed the superiority of our signature regarding its prognostic ability. Annotation analysis revealed the mechanism related to the gene signature specifically. The immune-infiltration analysis uncoveredkey roles for activated mast cells in the prognosis of OS.

Conclusion: We identified a robust nine-gene signature (ZFP90, UHRF2, SELPLG, PLD3, PLCB4, IFNGR1, DLEU2, ATP6V1E1, and ANXA5) that can predict OS outcome precisely and is strongly linked to activated mast cells.

Gene expression signature predicts relapse in adult patients with cytogenetically normal acute myeloid leukemia

Although ∼80% of adult patients with cytogenetically normal acute myeloid leukemia (CN-AML) achieve a complete remission (CR), more than half of them relapse. Better identification of patients who are likely to relapse can help to inform clinical decisions. We performed RNA sequencing on pretreatment samples from 268 adults with de novo CN-AML who were younger than 60 years of age and achieved a CR after induction treatment with standard "7+3" chemotherapy. After filtering for genes whose expressions were associated with gene mutations known to impact outcome (ie, CEBPA, NPM1, and FLT3-internal tandem duplication [FLT3-ITD]), we identified a 10-gene signature that was strongly predictive of patient relapse (area under the receiver operating characteristics curve [AUC], 0.81). The signature consisted of 7 coding genes (GAS6, PSD3, PLCB4, DEXI, JMY, NRP1, C10orf55) and 3 long noncoding RNAs. In multivariable analysis, the 10-gene signature was strongly associated with relapse (P < .001), after adjustment for the FLT3-ITD, CEBPA, and NPM1 mutational status. Validation of the expression signature in an independent patient set from The Cancer Genome Atlas showed the signature's strong predictive value, with AUC = 0.78. Implementation of the 10-gene signature into clinical prognostic stratification could be useful for identifying patients who are likely to relapse.