Background: Acute myeloid leukemia (AML) is a devastating malignancy with great heterogeneity, novel prognostic biomarkers and therapy targets are needed to improve the precise management of AML patients. Increasing evidence has shown the role of RPL34, a ribosomal protein, in tumorigenesis and progression. However, the detailed expression status and clinical significance of RPL34 in AML are largely unknown. Methods: The expression level of RPL34 was detected in bone marrow samples from both AML patients and AML cell lines. Then using recombinant shRNA-lentiviral vector, we analyzed the impact of RPL34 knockdown on cell proliferation, apoptosis and cell cycle distribution. Lastly, by analyzing public gene expression datasets (GSE12417 and GSE2191), we determined the prognostic role of RPL34 in AML. Results: The mRNA level of RPL34 was significantly elevated in AML bone marrow samples and cell lines. Patients with high level of RPL34 had inferior survival outcomes than their counterparts, and upregulation of RPL34 may mediate chemoresistance in AML. Through knockdown of RPL34 in HL-60 cell line, we found cell proliferation was inhibited, cell apoptosis was triggered, and cell cycle was arrested in S phase. Conclusions: The present study demonstrated that downregulation of RPL34 could inhibit cell proliferation, promote cell apoptosis, and induce cell cycle arrest in AML cell line HL-60. Also, its expression and clinical significance in AML patients was confirmed. All these findings suggest that RPL34 may be a potential novel therapy target in AML.
Background. Diffuse large B cell lymphoma (DLBCL) is a life-threatening malignant tumor characterized by heterogeneous clinical, phenotypic, and molecular manifestations. Given the association between immunity and tumors, identifying a suitable immune biomarker could improve DLBCL diagnosis. Methods. We systematically searched for DLBCL gene expression microarray datasets from the GEO database. Immune-related genes (IRGs) were obtained from the ImmPort database, and 318 transcription factor (TF) targets in cancer were retrieved from the Cistrome Cancer database. An immune-related classifier for DLBCL prognosis was constructed using Cox regression and LASSO analysis. To assess differences in overall survival between the low- and high-risk groups, we analyzed the tumor microenvironment (TME) and immune infiltration in DLBCL using the ESTIMATE and CIBERSORT algorithms. WGCNA was applied to study the molecular mechanisms explaining the clinical significance of our immune-related classifier and TFs. Results. Eighteen IRGs were selected to construct the classifier. The multi-IRG classifier showed powerful predictive ability. Patients with a high-risk score had poor survival. Based on the AUC for three- and five-year survival, the classifier exhibited better predictive power than clinical data. Discrepancies in overall survival between the low- and high-risk score groups might be explained by differences in immune infiltration, TME, and transcriptional regulation. Conclusions. Our study describes a novel prognostic IRG classifier with strong predictive power in DLBCL. Our findings provide valuable guidance for further analysis of DLBCL pathogenesis and clinical treatment.
Ferroptosis is a unique way of regulating cell death (RCD), which is quite different from other programmed cell deaths such as autophagy. It presents iron overload, accumulation of reactive oxygen species (ROS), and lipid peroxidation. A ferroptotic cell usually has an intact cell structure as well as shrinking mitochondria with decreased or vanishing cristae, concentrated membrane density, and ruptured outer membrane. Recently, increasing investigations have discovered that tumor cells have a much greater iron demand than the normal ones, making them more sensitive to ferroptosis. In other words, ferroptosis may inhibit the progress of the tumor, which can be used in the therapy of tumor patients, especially for those with chemotherapy resistance. Therefore, ferroptosis has become one hot spot in the field of tumor research in recent years. Colorectal cancer (CRC) is one common type of gastrointestinal malignancy. The incidence of CRC appears to have an upward trend year by year since the enhancement of living standards. Although surgery and chemoradiotherapy have largely improved the prognosis of patients with CRC, some patients still appear to have severe adverse reactions and drug resistance. Moreover, much research has verified that ferroptosis has a necessary association with the occurrence and progression of gastrointestinal tumors. In this review, we provide a comprehensive evaluation of the main mechanisms of iron metabolism, lipid metabolism, and amino acid metabolism involved in the occurrence of ferroptosis, as well as the research progress of ferroptosis in CRC.
As the most common subtype of non-Hodgkin's lymphoma, diffuse large B-cell lymphoma (DLBCL) is characterized by a huge degree of clinical and prognostic heterogeneity. Currently, there is an urgent need for highly specific and sensitive biomarkers to predict the therapeutic response of DLBCL and assess which patients can benefit from systemic chemotherapy to help develop more precise therapeutic regimens for DLBCL. Systems biology (holistic study of diseases) is more comprehensive in quantifying and identifying biomarkers, helps addressing major biological problems, and possesses high accuracy and sensitivity. In this article, we provide an overview of research advances in DLBCL prognostic biomarkers made using the multi-omics approach of genomics, transcriptomics, epigenetics, proteomics, metabonomics, radiomics, and the currently developing single-cell technologies.
This article is aimed at exploring the relationship between the phosphatase 2A catalytic subunit Cα (PP2Acα, encoded by PPP2CA) and methyltransferase-like 3 (METTL3) in the malignant progression of gastric cancer (GC). Through analyzing the bioinformatics database and clinical tissue immunohistochemistry results, we found that abnormal PP2Acα and METTL3 levels were closely related to the malignant progression of GC. To explore the internal connection between PP2Acα and METTL3 in the progression of GC, we carried out cellular and molecular experiments and finally proved that PP2Acα inhibition can upregulate METTL3 levels by activating ATM activity, thereby promoting the malignant progression of GC.
Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis.Methods: CRC’s data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients’ overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan–Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes’ expression was examined in the GSE41258 dataset.Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan–Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset.Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.
Background: Stem and progenitor cell populations sequentially accumulate and lead to cancer through various genetic/epigenetic alterations. The stemness and its association with the tumor microenvironment (TME) have not been studied in diffuse large B-cell lymphoma (DLBCL). Methods: Molecular data from Gene Expression Omnibus of 702 DLBCL patients who received cyclophosphamide, doxorubicin, vincristine, and prednisone combined with rituximab (R-CHOP) were analyzed. A one-class logistic regression (OCLR) machine-learning algorithm was applied to obtain a stemness index (mRNAsi) for each patient and to build molecular stemness-associated genetic signature. A novel stemness molecular signature was established via artificial intelligence to evaluate therapeutic response and prognosis in DLBCL. The significance between the stemness signature and TME cell-infiltrating characteristics was dissected. Results: mRNAsi was biologically significant among DLBCL subtypes and was associated with overall survival (OS). Based on refined 12 stemness-related genes, the stemness molecular signature was able to classify DLBCL patients into high- and low-risk groups. The signature could accurately predict OS and provide a clinically-significant risk stratification in distinguishing DLBCL patients who benefitted from R-CHOP therapy from those with poor outcomes. TME analysis revealed negative correlation between DLBCL stemness origin and features of infiltrating immune cells. Two additional immunotherapy cohorts validated above discoveries in which patients with low-risk score showed significant clinical benefits from PD1/PD-L1 blockade. Conclusions: mRNAsi and stemness molecular signature as new prognostic algorithm was valuable to quantify DLBCL stemness and therapeutic resistance. Such molecular signature and stemness prediction model represent a solid foundation of biologic features for cancer stem cell therapy and immunotherapy in DLBCL. A. survival of patients with high and low mRNAsi. B. According to the stemness molecular signature, two cohorts were defined: high-risk and low-risk cohort. overall survival (OS) in 702 DLBCL patients in the training set. C. AUC for OS at 3 and 5 years in the training cohort to assess prognostic accuracy. D. Expression of 12 stemness signature genes between the germinal B cell and non-germinal B cell subtypes of the training cohort. E. Expression of 12 stemness signature genes between patients at early and advanced stages in the training cohort. F. Proportion of patients with different therapy responses in the high- and low-risk groups of the GSE31312 cohort. G. According to the stratification of the therapy responses, the survival analysis of DLBCL patients. H-I. Survival plots showing the stratification of risk model for each of the individual responses to R-CHOP. J. GSVA enrichment analysis showing the activation states of biological pathways in high- and low-risk groups. K. Abundance of TME-infiltrating cells between high- and low-risk groups. L. Component differences of immune cells between low-risk and high-risk samples analyzed by the CIBERSORT algorithm. M. Based on multivariate Cox regression analysis, riskScore, age, subtype, stage, ECOG, and extranodal sites were integrated to construct a nomogram for predicting patients' prognosis. N-O. Time-dependent receiver operating characteristic (ROC) curve to evaluate the accuracy of the OS nomogram. P-Q. Calibration curves for 3-year OS nomogram model in the training cohort (P), GSE31312 cohort (Q). R. Survival analyses for low- (172 cases) and high- (176 cases) riskScore patient groups in the anti-PD-L1 immunotherapy cohort (IMvigor210 cohort). S. Proportion of patients with different therapy responses in the high- and low-risk groups of the IMvigor210 cohort. T. Difference in riskScore among different clinical response groups in the anti-PD-L1 immunotherapy cohort. U. Kaplan-Meier survival curve according to stratification of the responses to anti-PD-L1 therapy. V. Correlation between each stemness molecular signature gene and each TME infiltration cell type using Pearson correlation analyses. W. Difference in ImmuneScore between high and low ODC1 expression groups. X. Differences in immune-activated pathways between the high and low ODC1 expression groups. Y. Survival analyses for patients with low or high ODC1 expression in the anti-PD-L1 immunotherapy cohort. Figure Disclosures No relevant conflicts of interest to declare.
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