Prognostic significance of CDC25C in lung adenocarcinoma: An analysis of TCGA data

Impact of Protein Phosphatase Expressions on the Prognosis of Hepatocellular Carcinoma Patients

The study was conducted to unveil the significance of protein phosphatases in the prognosis of hepatocellular carcinoma (HCC) patients and its related molecular biological attributes as well as to discover novel potential biomarkers for therapeutic significance and diagnostic purposes that may benefit clinical practice. Analyzing a data set from 159 HCC patients using high-throughput phosphoproteomics, we examined the dysregulated expression of protein phosphatases. Employing bioinformatic and pathway analyses, we explored differentially expressed genes linked to protein phosphatases. A protein-protein interaction network was constructed using the search tool for the retrieval of interacting genes/proteins database. We quantified a total of 11,547 phosphorylation sites associated with 4043 phosphoproteins from HCC patients. Within this data set, we identified 105 identified phosphorylation sites associated with protein phosphatases; 28 genes were upregulated and 3 were downregulated in HCC. Enriched pathways using Gene Set Enrichment Analysis encompassed oocyte meiosis, proteoglycans in cancer, the oxytocin signaling pathway, the cGMP-PKG signaling pathway, the vascular smooth muscle, and the cAMP signaling pathway. The Kyoto encyclopedia of genes and genomes (KEGG) analysis highlighted pathways like mitogen-activated protein kinase, AMPK, and PI3K-Akt, indicating potential involvement in HCC progression. Notably, the PPI network identified hub genes, emphasizing their interconnections and potential roles in HCC. In our study, we found significantly upregulated levels of CDC25C, PPP1R13L, and PPP1CA, which emerge as promising avenues. This significant expression could serve as potent diagnostic and prognostic markers to enhance the effectiveness of HCC cancer treatment, offering efficiency and accuracy in patient assessment. The findings regarding protein phosphatases reveal their elevated expression in HCC, correlating with unfavorable prognosis. Moreover, the outcomes of gene ontology and KEGG pathway analyses suggest that protein phosphatases may influence liver cancer by engaging diverse targets and pathways, ultimately fostering the progression of HCC. These results underscore the substantial potential of protein phosphatases as key contributors to HCC's development and advancement. This insight holds promise for identifying therapeutic targets and charting research avenues to enhance the comprehension of the intricate molecular mechanisms underpinning HCC.

PUF60 promotes cell cycle and lung cancer progression by regulating alternative splicing of CDC25C

Alternative splicing (AS) has been implicated in cell cycle regulation and cancer, but the underlying mechanisms are poorly understood. The poly(U)-binding splicing factor 60 (PUF60) is essential for embryonic development and is overexpressed in multiple types of cancer. Here, we report that PUF60 promotes mitotic cell cycle and lung cancer progression by controlling AS of the cell division cycle 25C (CDC25C). Systematic analysis of splicing factors deregulated in lung adenocarcinoma (LUAD) identifies that elevated copy number and expression of PUF60 correlate with poor prognosis. PUF60 depletion inhibits LUAD cell-cycle G2/M transition, cell proliferation, and tumor development. Mechanistically, PUF60 knockdown leads to exon skipping enriched in mitotic cell cycle genes, including CDC25C. Exon 3 skipping in the full-length CDC25C results in nonsense-mediated mRNA decay and a decrease of CDC25C protein, thereby inhibiting cell proliferation. This study establishes PUF60 as a cell cycle regulator and an oncogenic splicing factor in lung cancer.

CDC25C Protein Expression Correlates with Tumor Differentiation and Clinical Outcomes in Lung Adenocarcinoma

Given that, even after multimodal therapy, early-stage lung cancer (LC) often recurs, novel prognostic markers to help guide therapy are highly desired. The mRNA levels of cell division cycle 25C (CDC25C), a phosphatase that regulates G2/M cell cycle transition in malignant cells, correlate with poor clinical outcomes in lung adenocarcinoma (LUAD). However, whether CDC25C protein detected by immunohistochemistry can serve as a prognostic marker in LUAD is yet unknown. We stained an LC tissue array and a cohort of 61 LUAD tissue sections for CDC25C and searched for correlations between CDC25C staining score and the pathological characteristics of the tumors and the patients' clinical outcomes. Clinical data were retrieved from our prospectively maintained departmental database. We found that high expression of CDC25C was predominant among poorly differentiated LUAD (p < 0.001) and in LUAD > 1cm (p < 0.05). Further, high expression of CDC25C was associated with reduced disease-free survival (p = 0.03, median follow-up of 39 months) and with a trend for reduced overall survival (p = 0.08). Therefore, high expression of CDC25C protein in LUAD is associated with aggressive histological features and with poor outcomes. Larger studies are required to further validate CDC25C as a prognostic marker in LUAD.

DriverMP enables improved identification of cancer driver genes

BACKGROUND

Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes.

FINDINGS

We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis.

CONCLUSIONS

The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.

Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.

Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4⁺ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.

Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

The Risk Model Based on the Three Oxidative Stress-Related Genes Evaluates the Prognosis of LAC Patients

Background

Oxidative stress plays a role in carcinogenesis. This study explores the roles of oxidative stress-related genes (OSRGs) in lung adenocarcinoma (LAC). Besides, we construct a risk score model of OSRGs that evaluates the prognosis of LAC patients.

Methods

OSRGs were downloaded from the Gene Set Enrichment Analysis (GSEA) website. The expression levels of OSRGs were confirmed in LAC tissues of the TCGA database. GO and KEGG analyses were used to evaluate the roles and mechanisms of oxidative stress-related differentially expressed genes (DEGs). Survival, ROC, Cox analysis, and AIC method were used to screen the prognostic DEGs in LAC patients. Subsequently, we constructed a risk score model of OSRGs and a nomogram. Further, this work investigated the values of the risk score model in LAC progression and the relationship between the risk score model and immune infiltration.

Results

We discovered 163 oxidative stress-related DEGs in LAC, involving cellular response to oxidative stress and reactive oxygen species. Besides, the areas under the curve of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 were 0.970, 0.984, 0.984, 0.945, 0.984, 0.771, and 0.959, respectively. This indicates that these OSRGs have diagnosis values of LAC and are significantly related to the overall survival of LAC patients. ERO1A, CDC25C, and ITGB4 overexpressions were independent risk factors for the poor prognosis of LAC patients and were associated with risk scores in the risk model. High-risk score levels affected the poor prognosis of LAC patients. Notably, a high-risk score may be implicated in LAC progression via cell cycle, DNA replication, mismatch repair, and other mechanisms. Further, ERO1A, CDC25C, and ITGB4 expression levels were related to the immune infiltrating cells of LAC, including mast cells, NK cells, and CD8 T cells.

Conclusion

In summary, ERO1A, CDC25C, and ITGB4 of OSRGs are associated with poor prognosis of LAC patients. We confirmed that the risk model based on the ERO1A, CDC25C, and ITGB4 is expected to assess the prognosis of LAC patients.

Construction of a Redox-Related Prognostic Model with Predictive Value in Survival and Therapeutic Response for Patients with Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) represents the most common histological subtype of lung cancer. Redox plays a significant role in oncogenesis and antitumor immunity. In this study, we aimed to investigate the prognostic redox-associated genes and construct a redox-based prognostic signature for LUAD.

Materials and Methods

A discovery cohort containing 479 LUAD samples from The Cancer Genome Atlas (TCGA) was analyzed. We identified prognostic redox-associated genes by weighted correlation network analysis (WGCNA) and univariate Cox regression analysis to construct a prognostic model via least absolute shrinkage and selection operator (LASSO)-multivariate Cox regression analyses. The performance of the redox-based model was validated in the TCGA cohort and an independent cohort of 456 samples by Cox regression analyses, log-rank test, and receiver operating characteristic (ROC) curves. Correlations of the model with clinicopathological variables and lymphocyte infiltration were assessed. Gene set enrichment analysis (GSEA) was used to clarify the underlying mechanism of the prognostic model. We constructed a nomogram based on the model and created calibration curves to show the accordance between actual survival and predicted survival of the nomogram.

Results

Stepwise analyses identified 6 prognostic redox-associated genes of LUAD and constructed a prognostic model that performed well in both the discovery and validation cohorts. The model was found to be associated with tumor stage, mutation of TP53 and EGFR, and lymphocyte infiltration. The model was mainly involved in the regulation of the cell cycle, DNA replication and repair, NADH metabolism, and the p53 signaling pathway. Calibration curves showed the high predictive accuracy of the nomogram.

Conclusions

This study explored the role of redox-associated genes in LUAD and constructed a prognostic model of LUAD. The signature was also associated with tumor progression and therapeutic response to immunotherapy. These findings contributed to uncovering the underlying mechanism and discovering novel prognostic predictor of LUAD.

Chordin-like 1 is a novel prognostic biomarker and correlative with immune cell infiltration in lung adenocarcinoma

Chordin-like 1 (CHRDL1), an inhibitor of bone morphogenetic proteins(BMPs), has been recently reported to participate in the progression of numerous tumors, however, its role in lung adenocarcinoma (LUAD) remains unclear. Our study aimed to demonstrate relationship between CHRDL1 and LUAD based on data from The Cancer Genome Atlas (TCGA). Among them, CHRDL1 expression revealed promising power for distinguishing LUAD tissues form normal sample. Low CHRDL1 was correlated with poor clinicopathologic features, including high T stage (OR=0.45, P<0.001), high N stage (OR=0.57, P<0.003), bad treatment effect (OR=0.64, P=0.047), positive tumor status (OR=0.63, P=0.018), and TP53 mutation (OR=0.49, P<0.001). The survival curve illustrated that low CHRDL1 was significantly correlative with a poor overall survival (HR=0.60, P<0.001). At multivariate Cox regression analysis, CHRDL1 remained independently correlative with overall survival. GSEA identified that the CHRDL1 expression was related to cell cycle and immunoregulation. Immune infiltration analysis suggested that CHRDL1 was significantly correlative with 7 kinds of immune cells. Immunohistochemical validation showed that CHRDL1 was abnormally elevated and negatively correlated with Th2 cells in LUAD tissues. In conclusion, CHRDL1 might become a novel prognostic biomarker and therapy target in LUAD. Moreover, CHRDL1 may improve the effectiveness of immunotherapy by regulating immune infiltration.

Seven autophagy-related lncRNAs are associated with the tumor immune microenvironment in predicting survival risk of nonsmall cell lung cancer

BACKGROUND

Nonsmall cell lung cancer (NSCLC) ranks first among global cancer-related deaths. Despite the emergence of various immunological and targeted therapies, immune tolerance remains a barrier to treatment.

METHODS

It has been found that this obstacle can be overcome by targeting autophagy-related genes (ATGs). ATGs were screened by coexpression analysis and the genes related to the prognosis of lung cancer were screened using Kaplan-Meier (K-M) survival analysis, univariate Cox regression and multivariate Cox regression. The prognostic risk model of ATGs was constructed and verified using K-M survival analysis and receiver operating characteristic (ROC) curve analysis.

RESULTS

The prognostic risk model of ATGs was constructed. Gene set enrichment analysis (GSEA) showed that the function and pathway of ATG enrichment were closely related to immune cell function. CIBERSORT, LM22 matrix and Pearson correlation analysis showed that risk signals were significantly correlated with immune cell infiltration and immune checkpoint genes.

CONCLUSIONS

We identified and independently verified the ATG (AL691432.2, MMP2-AS1, AC124067.2, CRNDE, ABALON, AL161431.1, NKILA) in NSCLC patients and found that immune regulation in the tumor microenvironment is closely related to this gene.

A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

BACKGROUND

DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.

METHOD

The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.

RESULT

We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.

CONCLUSIONS

The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

Construction of a prognostic model for non-small-cell lung cancer based on ferroptosis-related genes

We wished to construct a prognostic model based on ferroptosis-related genes and to simultaneously evaluate the performance of the prognostic model and analyze differences between high-risk and low-risk groups at all levels. The gene-expression profiles and relevant clinical data of patients with non-small-cell lung cancer (NSCLC) were downloaded from public databases. Differentially expressed genes (DEGs) were obtained by analyzing differences between cancer tissues and paracancerous tissues, and common genes between DEGs and ferroptosis-related genes were identified as candidate ferroptosis-related genes. Next, a risk-score model was constructed using univariate Cox analysis and least absolute shrinkage and selection operator (Lasso) analysis. According to the median risk score, samples were divided into high-risk and low-risk groups, and a series of bioinformatics analyses were conducted to verify the predictive ability of the model. Single-sample gene set enrichment analysis (ssGSEA) was used to investigate differences in immune status between high-risk and low-risk groups, and differences in gene mutations between the two groups were investigated. A risk-score model was constructed based on 21 ferroptosis-related genes. A Kaplan-Meier curve and receiver operating characteristic curve showed that the model had good prediction ability. Univariate and multivariate Cox analyses revealed that ferroptosis-related genes associated with the prognosis may be used as independent prognostic factors for the overall survival time of NSCLC patients. The pathways enriched with DEGs in low-risk and high-risk groups were analyzed, and the enriched pathways were correlated significantly with immunosuppressive status.

Multi-Omics Analysis of Anlotinib in Pancreatic Cancer and Development of an Anlotinib-Related Prognostic Signature

Aberrant regulation of angiogenesis involves in the growth and metastasis of tumors, but angiogenesis inhibitors fail to improve overall survival of pancreatic cancer patients in previous phase III clinical trials. A comprehensive knowledge of the mechanism of angiogenesis inhibitors against pancreatic cancer is helpful for clinical purpose and for the selection of patients who might benefit from the inhibitors. In this work, multi-omics analyses (transcriptomics, proteomics, and phosphoproteomics profiling) were carried out to delineate the mechanism of anlotinib, a novel angiogenesis inhibitor, against pancreatic cancer cells. The results showed that anlotinib exerted noteworthy cytotoxicity on pancreatic cancer cells. Multi-omics analyses revealed that anlotinib had a profound inhibitory effect on ribosome, and regulated cell cycle, RNA metabolism and lysosome. Based on the multi-omics results and available data deposited in public databases, an anlotinib-related gene signature was further constructed to identify a subgroup of pancreatic cancer patients who had a dismal prognosis and might be responsive to anlotinib.

Identification of CDC25C as a Potential Biomarker in Hepatocellular Carcinoma Using Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most aggressive type of gastrointestinal tumor, with a high rate of mortality. However, identifying biomarkers for the treatment of HCC remains to be developed. We aimed to determine whether cell division cycle 25C (CDC25C) could be used as a novel diagnostic and therapeutic biomarker in HCC. Expression of CDC25C in HCC was analyzed by using GEPIA (Gene Expression Profiling Interactive Analysis) and UALCAN databases. GEPIA and CBioPortal databases were applied to analyze patients’survival and CDC25C mutations, respectively. PPI (Protein-Protein Interaction) network was further built by STRING (Search Tool for the Retrieval of Interacting Genes) and Metascape Web portals. To the best of our knowledge, the novel observations identified in the present study reveal that the expression of CDC25C in HCC was significantly enhanced when compare to that in normal liver tissues (P < 0.001). A higher CDC25C expression resulted in a remarkably shorter disease free survival as well as overall survival. Moreover, the expression of CDC25C in HCC was related to HCC patients’grade and race, but not gender. The expression levels of CDC25C elevated gradually from stage 1 to 3 but decreased in stage 4. The specific gene mutations V41A, L87 H, N222 K and X309-splice of CDC25C occurred in HCC samples and these unique mutations were not detected in any other tumor tissues. Finally, PPI networks and GO enrichment analysis suggested that CDC25C might be associated with cell cycle and p53 signaling pathway. Taken together, bioinformatics analysis revealed that CDC25C might be a potential diagnostic predictor for HCC.

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Background

Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD).

Methods

RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method.

Results

A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance (P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis.

Conclusions

We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

A systematic review of genes affecting mitochondrial processes in cancer

Malignant conversion of cancer cells requires efficient mitochondria reprogramming orchestrated by hundreds of genes. The transformation includes increased energy demand, biosynthesis of precursors, and reactive oxygen species needed to accelerate cell growth, proliferation, and survival. Reprogramming involves complex gene alterations that have not been methodically curated. Therefore, we systematically analyzed the literature of cancer-related genes in mitochondria. Through the analysis of >2500 PubMed abstracts and >1600 human genes, we identified 228 genes showing clear roles in cancer. Each gene was classified according to their homeostatic function, together with the pathological transitions that contribute to specific cancer hallmarks. The potential clinical relevance of these hallmarks and genes is discussed by representative examples and validated by detecting differences in gene expression levels across 16 different types of cancer. A compendium, including the gene functions and alterations underpinning cancer progression, can be explored at http://bioinformatica.mty.itesm.mx/MitoCancer.

Systematic analysis of the ABC transporter family in hepatocellular carcinoma reveals the importance of ABCB6 in regulating ferroptosis

AIMS

ATP-binding cassette (ABC) transporters constitute one of the largest families of membrane proteins in most organisms; however, their functions in hepatocellular carcinoma (HCC) remain unclear.

MAIN METHODS

A set of bioinformatic tools was integrated to analyze the expression of 49 members of the ABC transporter family. The function of members which had prognostic values in HCC was explored by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.

KEY FINDINGS

ABCA8 and ABCA9 were significantly down-regulated in HCC. Prognostic analysis indicated that HCC patients with low expression of ABCA8 and ABCA9 had significantly shorter survival time. On the contrary, ABCB6 was over-expressed in the disease and high expression of ABCB6 was associated with worse prognosis. Co-expression analysis, and subsequently GO and KEGG analysis indicated that ABCA8 and ABCA9 might participate in the catabolic processes of multiple metabolites, while ABCB6 might regulate ferroptosis.

SIGNIFICANCE

This study reveals a previously unrecognized function of ABCB6 in HCC, by regulating ferroptosis. Since ABCB6 is over-expressed in HCC and ferroptosis involves in cancer development, ABCB6 might be a promising therapeutic target in the disease.

The role of CDC25C in cell cycle regulation and clinical cancer therapy: a systematic review

One of the most prominent features of tumor cells is uncontrolled cell proliferation caused by an abnormal cell cycle, and the abnormal expression of cell cycle-related proteins gives tumor cells their invasive, metastatic, drug-resistance, and anti-apoptotic abilities. Recently, an increasing number of cell cycle-associated proteins have become the candidate biomarkers for early diagnosis of malignant tumors and potential targets for cancer therapies. As an important cell cycle regulatory protein, Cell Division Cycle 25C (CDC25C) participates in regulating G2/M progression and in mediating DNA damage repair. CDC25C is a cyclin of the specific phosphatase family that activates the cyclin B1/CDK1 complex in cells for entering mitosis and regulates G2/M progression and plays an important role in checkpoint protein regulation in case of DNA damage, which can ensure accurate DNA information transmission to the daughter cells. The regulation of CDC25C in the cell cycle is affected by multiple signaling pathways, such as cyclin B1/CDK1, PLK1/Aurora A, ATR/CHK1, ATM/CHK2, CHK2/ERK, Wee1/Myt1, p53/Pin1, and ASK1/JNK-/38. Recently, it has evident that changes in the expression of CDC25C are closely related to tumorigenesis and tumor development and can be used as a potential target for cancer treatment. This review summarizes the role of CDC25C phosphatase in regulating cell cycle. Based on the role of CDC25 family proteins in the development of tumors, it will become a hot target for a new generation of cancer treatments.