CENPA regulates tumor stemness in lung adenocarcinoma

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.