CENPA regulates tumor stemness in lung adenocarcinoma

Identification of potential diagnostic biomarkers and therapeutic targets in patients with hypoxia pulmonary hypertension

BACKGROUND

Pulmonary hypertension is a serious disease. Emerging studies have shown that M2 macrophages play an essential role in pulmonary hypertension; however, their mechanism of action is uncertain.

METHODS

Four GEO datasets were downloaded. The differentially expressed genes (DEGs) were obtained using the limma package. Simultaneously, the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and weighted gene co-expression network analysis (WGCNA) were used to get the information about M2 macrophage-related modules. Potential key genes were obtained by intersecting DEGs with M2 macrophage-related module genes (M2MRGs), and finally the area under the curve (AUC) was calculated. Rats were exposed to hypoxia condition (10 % O2) for 4 weeks to induce PH. Subsequently, potential key genes with AUC>0.7 were analyzed by quantitative real-time polymerase chain reaction and Western blot using normoxia and hypoxia rat lungs. We knocked down EPHA3 in Raw264.7 cells and detected the protein expression of M2 macrophage markers including arginase 1 (ARG1) and interleukin 10 (IL-10), phospho-protein kinase B (P-Akt), and protein kinase B (Akt) to explore the downstream pathways of EPHA3.

RESULTS

Seven potential hub genes were detected by intersecting M2MRGs and DEGs. Six genes with AUC values above 0.7 were used for further exploration. The expression of EPHA3 mRNA and protein was significantly more upregulated in rats with hypoxia than in rats with normoxia. The expression levels of IL10, ARG1, and P-Akt/Akt decreased after knocking down EPHA3.

CONCLUSIONS

This study suggested that the activation of the P-Akt/Akt signaling pathway promoted by EPHA3 played an essential role in the progression of pulmonary hypertension.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

OBJECTIVES

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis.

MATERIALS AND METHODS

We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost.

RESULTS

The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes.

CONCLUSION

In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Roles of Histone H2B, H3 and H4 Variants in Cancer Development and Prognosis

Histone variants are the paralogs of core histones (H2A, H2B, H3 and H4). They are stably expressed throughout the cell cycle in a replication-independent fashion and are capable of replacing canonical counterparts under different fundamental biological processes. Variants have been shown to take part in multiple processes, including DNA damage repair, transcriptional regulation and X chromosome inactivation, with some of them even specializing in lineage-specific roles like spermatogenesis. Several reports have recently identified some unprecedented variants from different histone families and exploited their prognostic value in distinct types of cancer. Among the four classes of canonical histones, the H2A family has the greatest number of variants known to date, followed by H2B, H3 and H4. In our prior review, we focused on summarizing all 19 mammalian histone H2A variants. Here in this review, we aim to complete the full summary of the roles of mammalian histone variants from the remaining histone H2B, H3, and H4 families, along with an overview of their roles in cancer biology and their prognostic value in a clinical context.

CENPA facilitates glioma stem cell stemness and suppress ferroptosis to accelerate glioblastoma multiforme progression by promoting GBP2 transcription

The function of glioma stem cells (GSCs) is closely related to the progression of glioblastoma multiforme (GBM). Centromere protein A (CENPA) has been confirmed to be related to the poor prognosis of GBM patients. However, whether CENPA regulates GSCs function to mediate GBM progression is still unclear. GSCs were isolated from GBM cells. The expression of CENPA and guanylate-binding protein 2 (GBP2) was examined by quantitative real-time PCR and western blot. GSCs proliferation and stemness were assessed using EdU assay and sphere formation assay. Cell ferroptosis was evaluated by detecting related factors. The interaction between CENPA and GBP2 was analyzed by ChIP assay and dual-luciferase reporter assay. Animal experiments were conducted to measure the effect of CENPA knockdown on the tumorigenicity of GSCs in vivo. CENPA was upregulated in GBM tissues and GSCs. CENPA knockdown inhibited GSCs proliferation, stemnness, and promoted ferroptosis. GBP2 was overexpressed in GBM tissues and GSCs, and CENPA enhanced GBP2 transcription by binding to its promoter region. CENPA overexpression accelerated GSCs proliferation and stemnness and suppressed ferroptosis, while GBP2 knockdown reversed these effects. Downregulation of CENPA reduced the tumorigenicity of GSCs by decreasing GBP2 expression in vivo. In conclusion, CENPA enhanced GBP2 transcription to increase its expression, thus accelerating GSCs proliferation and stemnness and repressing ferroptosis. Our findings promote a new idea for GBM treatment.

Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer

Gastric cancer presents a formidable challenge, marked by its debilitating nature and often dire prognosis. Emerging evidence underscores the pivotal role of tumor stem cells in exacerbating treatment resistance and fueling disease recurrence in gastric cancer. Thus, the identification of genes contributing to tumor stemness assumes paramount importance. Employing a comprehensive approach encompassing ssGSEA, WGCNA, and various machine learning algorithms, this study endeavors to delineate tumor stemness key genes (TSKGs). Subsequently, these genes were harnessed to construct a prognostic model, termed the Tumor Stemness Risk Genes Prognostic Model (TSRGPM). Through PCA, Cox regression analysis and ROC curve analysis, the efficacy of Tumor Stemness Risk Scores (TSRS) in stratifying patient risk profiles was underscored, affirming its ability as an independent prognostic indicator. Notably, the TSRS exhibited a significant correlation with lymph node metastasis in gastric cancer. Furthermore, leveraging algorithms such as CIBERSORT to dissect immune infiltration patterns revealed a notable association between TSRS and monocytes and other cell. Subsequent scrutiny of tumor stemness risk genes (TSRGs) culminated in the identification of CDC25A for detailed investigation. Bioinformatics analyses unveil CDC25A's implication in driving the malignant phenotype of tumors, with a discernible impact on cell proliferation and DNA replication in gastric cancer. Noteworthy validation through in vitro experiments corroborated the bioinformatics findings, elucidating the pivotal role of CDC25A expression in modulating tumor stemness in gastric cancer. In summation, the established and validated TSRGPM holds promise in prognostication and delineation of potential therapeutic targets, thus heralding a pivotal stride towards personalized management of this malignancy.

RACGAP1 promotes the progression and poor prognosis of lung adenocarcinoma through its effects on the cell cycle and tumor stemness

OBJECTION

Investigating the key genes and mechanisms that influence stemness in lung adenocarcinoma.

METHODS

First, consistent clustering analysis was performed on lung adenocarcinoma patients using stemness scoring to classify them. Subsequently, WGCNA was utilized to identify key modules and hub genes. Then, machine learning methods were employed to screen and identify the key genes within these modules. Lastly, functional analysis of the key genes was conducted through cell scratch assays, colony formation assays, transwell migration assays, flow cytometry cell cycle analysis, and xenograft tumor models.

RESULTS

First, two groups of patients with different stemness scores were obtained, where the high stemness score group exhibited poor prognosis and immunotherapy efficacy. Next, LASSO regression analysis and random forest regression were employed to identify genes (PBK, RACGAP1) associated with high stemness scores. RACGAP1 was significantly upregulated in the high stemness score group of lung adenocarcinoma and closely correlated with clinical pathological features, poor overall survival (OS), recurrence-free survival (RFS), and unfavorable prognosis in lung adenocarcinoma patients. Knockdown of RACGAP1 suppressed the migration, proliferation, and tumor growth of cancer cells.

CONCLUSION

RACGAP1 not only indicates poor prognosis and limited immunotherapy benefits but also serves as a potential targeted biomarker influencing tumor stemness.

NCAPG stimulates lung adenocarcinoma cell stemness through aerobic glycolysis

BACKGROUND

Cancer stem cells are pivotal in cancer progression and therapy, including lung adenocarcinoma (LUAD). High NCAPG level is implicated in malignant tumorigenesis, but investigations on NCAPG and LUAD stem cells are warranted. Hence, projecting the impact of NCAPG on cell stemness and the targeted therapy for LUAD is of the essence.

METHODS

Bioinformatics analyzed NCAPG expression in LUAD tissues. qRT-PCR assayed NCAPG expression in LUAD cells. CCK-8 assessed cell viability and cell sphere-forming assay measured sphere-forming ability. Western blot assessed expression of stem cell-related markers (CD133, CD44, Oct-4) and specific genes (HK2, PKM2, LDHA) related to glycolysis metabolism pathway. Cellular glycolytic capacity was assayed by glycolytic metabolites pyruvic acid, lactate, citrate, and malate assay kits, and extracellular acidification rate and oxygen consumption rate analyzers.

RESULTS

NCAPG was upregulated in LUAD and enriched in the aerobic glycolysis pathway, and its expression was positively correlated with that of glycolytic marker genes. Cell function assays revealed that NCAPG stimulated proliferation, stemness, and glycolytic activity of LUAD cells. Rescue experiments unveiled that 2-DG (glycolysis inhibitor) was able to reverse the stimulative impact of NCAPG overexpression on proliferation, stemness, and glycolytic activity of LUAD cells.

CONCLUSION

NCAPG stimulated LUAD cell stemness through activation of glycolysis pathway. NCAPG may be possible biomarker for diagnosis and target for treatment of LUAD.

NRP1 regulates autophagy and proliferation of gastric cancer through Wnt/β-catenin signaling pathway

Gastric cancer possesses high lethality rate, and its complex molecular mechanisms of pathogenesis lead to irrational treatment outcomes. Autophagy plays a dual role in cancer by both promoting and suppressing the cancer. However, the role of autophagy in gastric cancer is still vague. Therefore, in this study, we first obtained autophagy-related genes from the Human Autophagy Database, and then applied consensus clustering analysis to analyse the molecular subtypes of gastric cancer samples in the TCGA database. The genes obtained after subtyping were then applied to construct risk prognostic model. Following this, PCA and tSNE assessed risk scores with good discriminatory ability for gastric cancer samples. The results of Cox regression analysis and time-dependent ROC curve analysis indicated that the model had good risk prediction ability. Finally, NRP1 was selected as the final study subject in the context of expression pairwise analysis, Kaplan-Meier curves and external validation of the GEO dataset. In vitro experiments showed that NRP1 has the ability to regulate the proliferation and autophagy of gastric cancer cells by affecting the Wnt/β-catenin signalling pathway. Similarly, in vivo experiments have shown that NRP1 can affect tumour growth in vivo. We therefore propose that NRP1 can be used as both a prognostic factor and a therapeutic target through the regulation of autophagy in gastric cancer.

MAGE-A3 regulates tumor stemness in gastric cancer through the PI3K/AKT pathway

Gastric cancer remains a malignant disease of the digestive tract with high mortality and morbidity worldwide. However, due to its complex pathological mechanisms and lack of effective clinical therapies, the survival rate of patients after receiving treatment is not satisfactory. A increasing number of studies have focused on cancer stem cells and their regulatory properties. In this study, we first constructed a co-expression network based on the WGCNA algorithm to identify modules with different degrees of association with tumor stemness indices. After selecting the most positively correlated modules of the stemness index, we performed a consensus clustering analysis on gastric cancer samples and constructed the co-expression network again. We then selected the modules of interest and applied univariate COX regression analysis to the genes in this module for preliminary screening. The results of the screening were then used in LASSO regression analysis to construct a risk prognostic model and subsequently a sixteen-gene model was obtained. Finally, after verifying the accuracy of the module and screening for risk genes, we identified MAGE-A3 as the final study subject. We then performed in vivo and in vitro experiments to verify its effect on tumor stemness and tumour proliferation. Our data supports that MAGE-A3 is a tumor stemness regulator and a potent prognostic biomarker which can help the prediction and treatment of gastric cancer patients.