A novel prognostic signature based on four glycolysis-related genes predicts survival and clinical risk of hepatocellular carcinoma

The METTL14-YTHDF1-SAP30 Axis Promotes Glycolysis and Oxaliplatin Resistance in Colorectal Adenocarcinoma via m6A Modification

Colorectal cancer (CRC) is a prevalent cancer with a poor prognosis, especially in advanced metastatic stages. This study identifies SAP30 as a significantly upregulated gene in COAD, linking high SAP30 expression to reduced overall survival. Experimental validation revealed elevated SAP30 levels in CRC cell lines (SW480, RKO, HT29, and HCT15), with the highest expression in oxaliplatin-resistant sublines (HT29-OxR and HCT15-OxR). SAP30 knockdown in oxaliplatin-resistant cells reduced glycolytic activity, glucose consumption, and glycolytic enzyme expression (LDHA, HK1, HK2, GLUT1, and GLUT4), while SAP30 overexpression enhanced glycolysis, partially reversed by the GLUT1 inhibitor WZB117. SAP30 also promoted cell proliferation, inhibited apoptosis, and enhanced migration and invasion in resistant CRC cells. Mechanistically, METTL14, an m6A methyltransferase, upregulates SAP30 mRNA via m6A modification, stabilized by the m6A reader protein YTHDF1. This METTL14-YTHDF1-SAP30 axis sustains SAP30 expression, promoting glycolysis and oxaliplatin resistance. In vivo studies confirmed that SAP30 knockout impairs tumor growth and reduces proliferation and glycolysis markers. This study highlights the METTL14-YTHDF1-SAP30 axis in glycolysis and chemoresistance in CRC, suggesting SAP30 as a potential target to overcome oxaliplatin resistance and improve patient outcomes.

SAP30 promotes clear cell renal cell carcinoma proliferation and inhibits apoptosis through the MT1G axis

Sin3A-associated protein 30 (SAP30) is a crucial component of the SIN/HDAC histone deacetylase complex and acts as a scaffold that facilitates target gene binding. SAP30 is highly expressed in various tumours; however, its role in renal cell carcinoma (RCC) remains unclear. In our study, we observed the upregulation of SAP30 in clear cell renal cell carcinoma (ccRCC) tissues, and its elevated expression was correlated with a poor prognosis. Previous research has suggested that SAP30 may influence the growth, proliferation, and apoptosis of RCC cells. Gene Ontology (GO) analysis of the downstream regulatory targets of SAP30 revealed that SAP30 suppressed the expression of MT1G, a protein that binds to p53. Mechanistically, SAP30 inhibited MT1G transcription, thereby impairing the function of MT1G in delivering zinc ions to p53, which diminished p53 activity. Moreover, reduced MT1G levels attenuated the inhibitory effect of MT1G on MDM2, further destabilizing p53. Consequently, this cascade promoted RCC progression. In conclusion, our findings indicate that SAP30 inhibits the p53 pathway through MT1G suppression, suggesting that SAP30 and MT1G are potential prognostic markers and therapeutic targets for RCC.

CDCA8 and its multifaceted role in tumorigenesis

Human cell division cycle-associated 8 (CDCA8), also known as Borealin or Dasra-B, is a critical component of the vertebrate Chromosomal Passenger Complex (CPC). It plays a pivotal role in the segregation of sister chromatids during the cell cycle and is essential for preventing the formation of aneuploid chromosomes and ensuring successful cytokinesis. Numerous studies have demonstrated that CDCA8 is upregulated in various cancers, including hepatocellular carcinoma, lung cancer, glioma, and bladder cancer. By influencing key biological processes such as cell proliferation, apoptosis, invasion, and metastasis, CDCA8 drives tumor progression. Clinically, the expression of CDCA8 correlates closely with tumor staging and histological grading, providing significant prognostic value for patients with diverse cancers. Moreover, CDCA8 modulates tumor biology through multiple signaling pathways, including P53, PI3K/Akt, E2F/Rb, and mTOR. In summary, CDCA8 represents a promising diagnostic and therapeutic target across multiple cancer types and serves as a potential prognostic biomarker. This review highlights the critical roles of CDCA8 in cancer diagnosis, treatment, and prognosis, as well as the underlying mechanisms through which it exerts its effects. These insights offer a theoretical basis and research direction for early cancer diagnosis, targeted therapy, and prognostic evaluation.

Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. The prognostic relationship between CMLS and STAD was assessed using Kaplan-Meier curves, and the independent prognostic value of CMLS was determined by univariate and multivariate regression analyses. we also evaluated the immune characteristics, immunotherapy response, and drug sensitivity of different CMLS groups. The results of the multiomics analysis classified STAD into three subtypes, with CS1 resulting in the best survival outcome. In total, 10 hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, and DKK1) were screened and constructed the CMLS was significantly correlated with prognosis in patients with STAD and was an independent prognostic factor for patients with STAD. Using the CMLS risk score, all patients were divided into a high CMLS group and a low CMLS group. Patients in the low-CMLS group had better survival, more enriched immune cells, and higher tumor mutation load scores, suggesting better immunotherapy responsiveness and a possible "hot tumor" phenotype. Patients in the high-CMLS group had a significantly poorer prognosis and were less sensitive to immunotherapy but were likely to benefit more from chemotherapy and targeted therapy. In this study, 10 clustering methods and 10 machine learning methods were combined to analyze the multiomics of STAD, classify STAD into three subtypes, and constructed CMLS-related prognostic model features, which are important for accurate management and effective treatment of STAD.

Exploring the utility of zinc finger protein-related genes in predicting hepatocellular carcinoma prognosis, immune responses, and drug efficacy

BackgroundHepatocellular carcinoma (LIHC), a prevalent liver cancer with a grim prognosis due to high recurrence rates, is under scrutiny for its association with zinc finger proteins (ZNFs) in tumorigenesis. This study aims to create a prognostic model for LIHC incorporating ZNF-related genes.MethodsBy analyzing TCGA data, we identified differentially expressed genes (DEGs) between normal and LIHC samples, focusing on ZNF-related genes through univariate Cox and LASSO Cox regression. A multivariate Cox regression model was built, categorizing LIHC patients into high- and low-ZNFRS groups based on ZNF-related risk scores. Model performance was evaluated using ROC curves, with a nomogram integrating clinical data and ZNFRS. Immune microenvironment, enrichment analysis, mutations, and drug responses in LIHC were also explored.ResultsA prognostic model utilizing 10 ZNF-related genes accurately predicted LIHC survival. The low-risk group exhibited enhanced immune cell infiltration, contrasting with cell cycle and DNA replication enrichment in the high-risk group, which also displayed increased mutation rates. Promising drug candidates like SNS-314 and Decitabine warrant further investigation in LIHC treatment.ConclusionThis study introduces impactful prognostic markers for LIHC management, emphasizing the significance of ZNFs in predicting patient outcomes and guiding treatment strategies.

SNHG1: Redefining the Landscape of Hepatocellular Carcinoma through Long Noncoding RNAs

Hepatocellular carcinoma (HCC) represents a global health concern, ranking as the sixth most common malignancy worldwide and the third leading cause of cancer-related mortality. Despite advances in research, the diagnosis and prognosis of such malignancy remain challenging. Alpha-fetoprotein, the current serum biomarker used in the management of HCC, has limited sensitivity and specificity, making early detection and effective management more difficult. Thus, new management approaches in diagnosis and prognosis are needed to improve the outcome and survival of HCC patients. SNHG1 is a long noncoding RNA mainly expressed in the cell and cytoplasm of cells and is consistently upregulated in tissues and cell lines of HCC, where it acts as an important regulator of various processes: modulation of p53 activity, sponging of microRNAs with consequent upregulation of their target mRNAs, regulation of fatty acid, iron and glucose metabolism, and interaction with immune cells. The deregulation of these processes results in abnormal cell division, angiogenesis, and apoptosis, thus promoting various aspects of tumorigenesis, including proliferation, invasion, and migration of cells. Clinically, a higher expression of SNHG1 predicts poorer clinical outcomes by significantly correlating with bigger, less differentiated, and more aggressive tumors, more advanced disease stages, and lower overall survival in HCC patients. This article comprehensively summarizes the current understanding of the multifaceted roles of SNHG1 in the pathogenesis of HCC, while also highlighting its clinicopathological correlations, therefore concluding that it has potential as a biomarker in HCC diagnosis and prognosis.

Activation of the PI3K/AKT signaling pathway by ARNTL2 enhances cellular glycolysis and sensitizes pancreatic adenocarcinoma to erlotinib

BACKGROUND

Pancreatic adenocarcinoma (PC) is an aggressive malignancy with limited treatment options. The poor prognosis primarily stems from late-stage diagnosis and when the disease has become therapeutically challenging. There is an urgent need to identify specific biomarkers for cancer subtyping and early detection to enhance both morbidity and mortality outcomes. The addition of the EGFR tyrosine kinase inhibitor (TKI), erlotinib, to gemcitabine chemotherapy for the first-line treatment of patients with advanced pancreatic cancer slightly improved outcomes. However, restricted clinical benefits may be linked to the absence of well-characterized criteria for stratification and dependable biomarkers for the prediction of treatment effectiveness.

METHODS AND RESULTS

We examined the levels of various cancer hallmarks and identified glycolysis as the primary risk factor for overall survival in PC. Subsequently, we developed a glycolysis-related score (GRS) model to accurately distinguish PC patients with high GRS. Through in silico screening of 4398 compounds, we discovered that erlotinib had the strongest therapeutic benefits for high-GRS PC patients. Furthermore, we identified ARNTL2 as a novel prognostic biomarker and a predictive factor for erlotinib treatment responsiveness in patients with PC. Inhibition of ARNTL2 expression reduced the therapeutic efficacy, whereas increased expression of ARNTL2 improved PC cell sensitivity to erlotinib. Validation in vivo using patient-derived xenografts (PDX-PC) with varying ARNTL2 expression levels demonstrated that erlotinib monotherapy effectively halted tumor progression in PDX-PC models with high ARNTL2 expression. In contrast, PDX-PC models lacking ARNTL2 did not respond favorably to erlotinib treatment. Mechanistically, we demonstrated that the ARNTL2/E2F1 axis-mediated cellular glycolysis sensitizes PC cells to erlotinib treatment by activating the PI3K/AKT signaling pathway.

CONCLUSIONS

Our investigations have identified ARNTL2 as a novel prognostic biomarker and predictive indicator of sensitivity. These results will help to identify erlotinib-responsive cases of PC and improve treatment outcomes. These findings contribute to the advancement of precision oncology, enabling more accurate and targeted therapeutic interventions.

Unraveling the enigma of B cells in diffuse large B-cell lymphoma: unveiling cancer stem cell-like B cell subpopulation at single-cell resolution

Background

Diffuse large B-cell lymphoma (DLBCL) represents the most prevalent form of aggressive non-Hodgkin lymphoma. Despite receiving standard treatment, a subset of patients undergoes refractory or recurrent cases, wherein the involvement of cancer stem cells (CSCs) could be significant.

Methods

We comprehensively characterized B cell subpopulations using single-cell RNA sequencing data from three DLBCL samples and one normal lymph tissue. The CopyKat R package was employed to assess the malignancy of B cell subpopulations based on chromosomal copy number variations. CIBERSORTx software was utilized to estimate the proportions of B cell subpopulations in 230 DLBCL tissues. Furthermore, we employed the pySCENIC to identify key transcription factors that regulate the functionality of B cell subpopulations. By employing CellphoneDB, we elucidated the interplay among tumor microenvironment components within the B cell subpopulations. Finally, we validated our findings through immunofluorescence experiments.

Results

Our analysis revealed a specific cancer stem cell-like B cell subpopulation exhibiting self-renewal and multilineage differentiation capabilities based on the exploration of B cell subpopulations in DLBCL and normal lymph tissues at the single-cell level. Notably, a high infiltration of cancer stem cell-like B cells correlated with a poor prognosis, potentially due to immune evasion mediated by low expression of major histocompatibility complex molecules. Furthermore, we identified key transcription factor regulatory networks regulated by HMGB3, SAP30, and E2F8, which likely played crucial roles in the functional characterization of the cancer stem cell-like B cell subpopulation. The existence of cancer stem cell-like B cells in DLBCL was validated through immunofluorescent staining. Finally, cell communication between B cells and tumor-infiltrating T cell subgroups provided further insights into the functional characterization of the cancer stem cell-like B cell subpopulation.

Conclusions

Our research provides a systematic description of a specific cancer stem cell-like B cell subpopulation associated with a poor prognosis in DLBCL. This study enhances our understanding of CSCs and identifies potential therapeutic targets for refractory or recurrent DLBCL patients.

CDCA8 induced by NF-YA promotes hepatocellular carcinoma progression by regulating the MEK/ERK pathway

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors. Cell division cycle associated 8 (CDCA8) is an important multifactorial regulator in cancers. However, its up and downstream targets and effects in HCC are still unclear.

METHODS

A comprehensive bioinformatics analysis was performed using The Cancer Genome Atlas dataset (TCGA) to explore novel core oncogenes. We quantified CDCA8 levels in HCC tumors using qRT-PCR. HCC cell's proliferative, migratory, and invasive abilities were detected using a Cell Counting Kit-8 (CCK-8) assay, 5-ethynyl-2'-deoxyuridine (EdU) assay, clone formation, and a Transwell assay. An orthotopic tumor model and tail vein model were constructed to determine the effects of CDCA8 inhibition in vivo. The mechanism underlying CDCA8 was investigated using RNA sequencing. The prognostic value of CDCA8 was assessed with immunohistochemical staining of the tissue microarrays.

RESULTS

CDCA8 was identified as a novel oncogene during HCC development. The high expression of CDCA8 was an independent predictor for worse HCC outcomes both in publicly available datasets and in our cohort. We found that CDCA8 knockdown inhibited HCC cell proliferation, colony formation, and migration by suppressing the MEK/ERK pathway in vitro. Moreover, CDCA8 deficiency significantly inhibited tumorigenesis and metastasis. Next-generation sequencing and laboratory validation showed that CDCA8 silencing inhibited the expression of TPM3, NECAP2, and USP13. Furthermore, NA-YA overexpression upregulated the expression of CDCA8. CDCA8 knockdown could attenuate NF-YA-mediated cell invasion in vitro. The expression of NF-YA alone or in combined with CDCA8 were validated as significant independent risk factors for patient survival.

CONCLUSION

Our findings revealed that the expression of CDCA8 alone or in combined with NF-YA contributed to cancer progression, and could serve as novel potential therapeutic targets for HCC patients.

Identification of Hypoxia-Related Prognostic Signature and Competing Endogenous RNA Regulatory Axes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common type of liver cancer and one of the highly lethal diseases worldwide. Hypoxia plays an important role in the development and prognosis of HCC. This study aimed to construct a new hypoxia-related prognosis signature and investigate its potential ceRNA axes in HCC. RNA profiles and hypoxia genes were downloaded, respectively, from the Cancer Genome Atlas hepatocellular carcinoma database and Gene Set Enrichment Analysis website. Cox regression analyses were performed to select the prognostic genes and construct the risk model. The ENCORI database was applied to build the lncRNA-miRNA-mRNA prognosis-related network. The TIMER and CellMiner databases were employed to analyze the association of gene expression in ceRNA with immune infiltration and drug sensitivity, respectively. Finally, the co-expression analysis was carried out to construct the potential lncRNA/miRNA/mRNA regulatory axes. We obtained a prognostic signature including eight hypoxia genes (ENO2, KDELR3, PFKP, SLC2A1, PGF, PPFIA4, SAP30, and TKTL1) and further established a hypoxia-related prognostic ceRNA network including 17 lncRNAs, six miRNAs, and seven mRNAs for hepatocellular carcinoma. Then, the analysis of immune infiltration and drug sensitivity showed that gene expression in the ceRNA network was significantly correlated with the infiltration abundance of multiple immune cells, the expression level of immune checkpoints, and drug sensitivity. Finally, we identified three ceRNA regulatory axes (SNHG1/miR-101-3p/PPFIA4, SNHG1/miR-101-3p/SAP30, and SNHG1/miR-101-3p/TKTL1) associated with the progression of HCC under hypoxia. Here, we constructed a prognosis gene signature and a ceRNA network related to hypoxia for hepatocellular carcinoma. Among the ceRNA network, six highly expressed lncRNAs (AC005540.1, AC012146.1, AC073529.1, AC090772.3, AC138150.2, AL390728.6) and one highly expressed mRNA (PPFIA4) were the potential biomarkers of hepatocellular carcinoma which we firstly reported. The three predicted hypoxia-related regulatory axes may play a vital role in the progression of hepatocellular carcinoma.

Effects of PAMK on lncRNA, miRNA, and mRNA expression profiles of thymic epithelial cells

Polysaccharides from Atractylodes macrocephala Koidz (PAMK) can promote the proliferation of thymocytes and improve the body's immunity. However, the effect of PAMK on thymic epithelial cells has not been reported. Studies have shown that miRNAs and lncRNAs are key factors in regulating cell proliferation. In this study, we found that PAMK could promote the proliferation of mouse medullary thymic epithelial cell line 1 (MTEC1) cells through CCK-8 and EdU experiments. To further explore its mechanism, we detected the effect of PAMK on the expression profiles of lncRNAs, miRNAs, and mRNAs in MTEC1 cells. The results showed that PAMK significantly affected the expression of 225 lncRNAs, 29 miRNAs, and 800 mRNAs. Functional analysis showed that these differentially expressed genes were significantly enriched in cell cycle, cell division, NF-kappaB signaling, apoptotic process, and MAPK signaling pathway. Finally, we used Cytoscape to visualize lncRNA-miRNA-mRNA(14 lncRNAs, 17 miRNAs, 171 mRNAs) networks based on ceRNA theory. These results suggest that lncRNAs and miRNAs may be involved in the effect of PAMK on the proliferation of MTEC1 cells, providing a new research direction for exploring the molecular mechanism of PAMK promoting the proliferation of thymic epithelial cells.

Classification and Prognostic Characteristics of Hepatocellular Carcinoma Based on Glycolysis Cholesterol Synthesis Axis

Background

Liver hepatocellular carcinoma (LIHC) is among the most frequent causes of cancer-related death across the world with a considerably poor prognosis. The current study targeted providing a new type of LIHC from the perspective of the glycolysis/cholesterol synthesis axis, predicting its prognostic characteristics, and exploring the potential role and mechanism of the glycolysis/cholesterol synthesis axis in the occurrence and development of LIHC.

Methods

Based on the two expression profile data and clinical information of LIHC in The Cancer Genome Atlas (TCGA) database and hepatocellular carcinoma database (HCCDB), as well as glycolysis/cholesterol-related genes from the Molecular Signatures Database (MSigDB), unsupervised consistent clustering method was used to identify molecular subtypes. In addition, the differential genes were identified by limma package, and then the gene set was enriched, analyzed, and annotated by WebGestaltR package. At the same time, the immune infiltration analysis of tumor samples was carried out using the ESTIMATE to evaluate the tumor immune score of the samples. Finally, the differences in clinical characteristics among molecular subtypes were measured using univariate and multivariate Cox analyses.

Results

According to the median standardized expression levels of glycolysis/cholesterol production genes, samples were divided into four groups (molecular subtypes): Quiescent group, Glycolysis group, Cholesterol group, and Mixed group. Significant prognostic differences were observed among the four groups. In both TCGA and HCCDB18 datasets, the prognosis of subtype Mixed was the worst, while Quiescent had a good prognosis. Cell cycle and oncogenic pathways were significantly enriched in the Mixed group. In addition, glycolysis and cholesterol production gene expressions were related to the prognostic LIHC subtype classification genes' expression levels.

Conclusion

Metabolic classification regarding glycolysis and cholesterol production pathways provided new insights into the biological aspects of LIHC molecular subtypes and might help to develop personalized therapies for unique tumor metabolic profiles.