Cancer Biomarkers最新文献

Glioma is the most common primary tumor of the central nervous system (CNS). Glioblastoma (GBM) is incurable with current treatment strategies. Additionally, the treatment of recurrent GBM (rGBM) is often referred to as terminal treatment, necessitating hospice-level care and management. The presence of the blood-brain barrier (BBB) gives GBM a more challenging or "cold" tumor microenvironment (TME) than that of other cancers and gloma stem cells (GSCs) play an important role in the TME remodeling, occurrence, development and recurrence of giloma. In this review, our primary focus will be on discussing the following topics: niche-associated GSCs and macrophages, new theories regarding GSC and TME involving pyroptosis and ferroptosis in GBM, metabolic adaptations of GSCs, the influence of the cold environment in GBM on immunotherapy, potential strategies to transform the cold GBM TME into a hot one, and the advancement of GBM immunotherapy and GBM models.

Background: Epithelial-mesenchymal transition (EMT) is an important biological process by which malignant tumor cells to acquire migration and invasion abilities. This study explored the role of KLF5 in the EMT process of in cervical cancer cell lines.

Objective: Krüpple-like factor 5 (KLF5) is a basic transcriptional factor that plays a key role in cell-cycle arrest and inhibition of apoptosis. However, the molecular mechanism by which KLF5 mediates the biological functions of cervical cancer cell lines has not been elucidated. Here, we focus on the potential function of ELF5 in regulating the EMT process in in vitro model of cervical cancer cell lines.

Method: Western-blot and real-time quantitative PCR were used to detect the expression of EMT-related genes in HeLa cells. MTT assays, cell scratch and Transwell assays were used to assess HeLa cells proliferation and invasion capability. Using the bioinformatics tool JASPAR, we identified a high-scoring KLF5-like binding sequence in the SNAI1 gene promoter. Luciferase reporter assays was used to detect transcriptional activity for different SNAI1 promoter truncates.

Result: After overexpressing the KLF5 gene in HeLa cells, KLF5 not only significantly inhibited the invasion and migration of HeLa cells, but also increased the expression of E-cadherin and decreased the expression of N-cadherin and MMP9. In addition, the mRNA expression of upstream regulators of E-cadherin, such as SNAI1, SLUG, ZEB1/2 and TWIST1 was also decreased. Furthermore, KLF5 inhibiting the expression of the SNAI1 gene via binding its promoter region, and the EMT of Hela cells was promoted after overexpression of the SNAI1 gene.

Conclusion: These results indicate that KLF5 can downregulate the EMT process of HeLa cells by decreasing the expression of the SNAI1 gene, thereby inhibiting the migration and invasion of HeLa cervical cancer cells.

Background: N6-methyladenosine (m6A) is the most frequent RNA modification in mammals, and its role in bladder cancer (BC) remains rarely revealed.

Objective: To predict the value of m6A-related genes in prognosis and immunity in BC.

Methods: We performed multiple omics analysis of 618 TCGA and GEO patients and used principal component analysis (PCA) to calculate the m6A score for BC patients.

Results: We described the multiple omics status of 23 m6A methylation-related genes (MRGs), and four m6A clusters were identified, which showed significant differences in immune infiltration and biological pathways. Next, we intersected the differential genes among m6A clusters, and 11 survival-related genes were identified, which were used to calculate the m6A score for the patients. We found that the high-score (HS) group showed lower tumor mutation burden (TMB) and TP53 mutations and better prognosis than the low-score (LS) group. Lower immune infiltration, higher expression of PD-L1, PD-1, and CTLA4, and higher immune dysfunction and immune exclusion scores were identified in the LS group, suggesting a higher possibility of immune escape. Finally, the experimental verification shows that the m6A related genes, such as IGFBP1, plays an important role in the growth and metastasis of bladder cancer.

Conclusions: These findings revealed the important roles of m6A MRGs in predicting prognosis, TMB status, TP53 mutation, immune functions and immunotherapeutic response in BC.

Background: The association between infection with cagA-positive H. pylori and an elevated susceptibility to gastric cancer has been firmly established. PIM2 is known to be overexpressed in various types of cancers; however, the specific mechanism by which cagA influences the regulation of PIM2 expression in gastric cancer remains unidentified at present.

Materials and methods: A mutant NCTC11637ΔcagA strain of H. pylori and the eukaryotic expression vector pcDNA-cagA were constructed for evaluating PIM2 expression levels in gastric cancer cells (HGC27, SGC7901, and AG) co-cultured with the NCTC11637 and NCTC11637ΔcagA strain, as well as pcDNA-cagA and the empty vector pcDNA3.1 (+).

Results: Co-culturing gastric cancer cells with NCTC11637 significantly increased PIM2 expression levels (P< 0.001) compared to the negative control group. Additionally, the expression of PIM2 in cells co-cultured with NCTC11637 was higher than that co-cultured with NCTC11637ΔcagA (P< 0.001). Furthermore, successful construction of the eukaryotic expression vector pcDNA-cagA resulted in a significant increase in PIM2 mRNA expression levels after its transfection into gastric cancer cells compared to the control group after 48 hours.

Conclusions: The findings indicate that H. pylori/cagA A could be one of the key factors in regulating PIM2 expression levels, potentially influencing the progression of H. pylori-related Gastric Cancer.

Background: Numerous studies have shown that m6A plays an important regulatory role in the development of tumors. HNRNPA2B1, one of the m6A RNA methylation reading proteins, has been proven to be elevated in human cancers.

Objective: In this study, we aimed to identify the role of HNRNPA2B1 in breast cancer.

Methods: HNRNPA2B1 expression was investigated via RT-qPCR and TCGA database in breast cancer. Then, the function of HNRNPA2B1 on cancer cell was measured by CCK8 assays, colony formation and scratch assays. In addition, HNRNPA2B1 expression in BRCA was explored via the Wilcoxon signed-rank test, KruskalWallis test and logistic regression. The association with HNRNPA2B1 expression and survival were considered by KaplanMeier and Cox regression analyses. The biological function of HNRNPA2B1 was analyzed via gene set enrichment analysis (GSEA) and the cluster Profiler R software package.

Results: We found that HNRNPA2B1 was highly expressed and induced cell proliferation and migration in breast cancer. Moreover, we observed HNRNPA2B1 induced tumor growth in vivo. In addition, we also found HNRNPA2B1 expression was associated with characteristics and prognosis in breast cancer patients.

Conclusion: Our findings suggested that HNRNPA2B1 promoted tumor growth and could function as a new potential molecular marker in breast cancer.

Background: Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process.

Objective: In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis.

Methods: Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues.

Results: The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes.

Conclusion: Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

Background: Ovarian cancer (OC) is mostly diagnosed in advanced stages with high incidence-to-mortality rate. Nevertheless, some patients achieve long-term disease-free survival. However, the prognostic markers have not been well established.

Objective: The primary objective of this study was to analyse the association of the suggested prognostic marker rs2185379 in PRDM1 with long-term survival in a large independent cohort of advanced OC patients.

Methods: We genotyped 545 well-characterized advanced OC patients. All patients were tested for OC predisposition. The effect of PRDM1 rs2185379 and other monitored clinicopathological and genetic variables on survival were analysed.

Results: The univariate analysis revealed no significant effect of PRDM1 rs2185379 on survival whereas significantly worse prognosis was observed in postmenopausal patients (HR = 2.49; 95%CI 1.90-3.26; p= 4.14 × 10 - 11) with mortality linearly increasing with age (HR = 1.05 per year; 95%CI 1.04-1.07; p= 2 × 10 - 6), in patients diagnosed with non-high-grade serous OC (HR = 0.44; 95%CI 0.32-0.60; p= 1.95 × 10 - 7) and in patients carrying a gBRCA1 pathogenic variant (HR = 0.65; 95%CI 0.48-0.87; p= 4.53 × 10 - 3). The multivariate analysis interrogating the effect of PRDM1 rs2185379 with other significant prognostic factors revealed marginal association of PRDM1 rs2185379 with worse survival in postmenopausal women (HR = 1.54; 95%CI 1.01-2.38; p= 0.046).

Conclusions: Unlike age at diagnosis, OC histology or gBRCA1 status, rs2185379 in PRDM1 is unlikely a marker of long-term survival in patients with advance OC.

Background: Radioiodine-131 (I-131) therapy is the common postoperative adjuvant therapy for differentiated thyroid cancer (DTC) However, methods to evaluate the efficacy and toxicity of I-131 on DTC are still lacking.

Objective: To evaluate the association between vitamin D receptor (VDR) gene polymorphisms and the efficacy and toxicity of I-131 in DTC patients.

Methods: A total of 256 DTC patients who received I-131 therapy were enrolled. The patients were divided into effective group and ineffective group. 4 single nucleotide polymorphisms (SNPs) (rs7975232, rs731236, rs1544410 and rs10735810) of VDR were analyzed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) Cell counting kit-8 (CCK-8) and flow cytometry were used to detect the proliferation and apoptosis of thyroid cancer cells.

Results: Patients in effective group had more CC genotype of rs7975232 and GG genotype of rs10735810 compared with patients in ineffective group They were also independent factors for influencing the efficacy of I-131. PTC-1 and FTC-133 cells transfected with CC genotype of rs7975232 showed lower proliferative activity and higher apoptosis rate after being treated with I-131 In addition, patients with CC genotype at rs7975232 had fewer adverse reactions after I-131 treatment.

Conclusions: VDR gene polymorphisms may be associated with the efficacy and toxicity of I-131 in DTC patients, which will help to personalize the treatment for patients.

Objectives: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images.

Methods: CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis.

Results: The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843-0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC).

Conclusion: Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.

Background: Post-transcriptional regulation of mRNA induced by microRNA is known crucial in tumor occurrence, progression, and metastasis. This study aims at identifying significant miRNA-mRNA axes for stomach adenocarcinomas (STAD).

Method: RNA expression profiles were collected from The Cancer Genome Atlas (TCGA) and GEO database for screening differently expressed RNAs and miRNAs (DE-miRNAs/DE-mRNAs). Functional enrichment analysis was conducted with Hiplot and DAVID-mirPath. Connectivity MAP was applied in compounds prediction. MiRNA-mRNA axes were forecasted by TarBase and MiRTarBase. Real-time reverse transcription polymerase chain reaction (RT-qPCR) of stomach specimen verified these miRNA-mRNA pairs. Diagnosis efficacy of miRNA-mRNA interactions was measured by Receiver operation characteristic curve and Decision Curve Analysis. Clinical and survival analysis were also carried out. CIBERSORT and ESTIMATE was employed for immune microenvironment measurement.

Result: Totally 228 DE-mRNAs (105 upregulated and 123 downregulated) and 38 DE-miRNAs (22 upregulated and 16 downregulated) were considered significant. TarBase and MiRTarBase identified 18 miRNA-mRNA pairs, 12 of which were verified in RT-qPCR. The network of miR-301a-3p/ELL2 and miR-1-3p/ANXA2 were established and verified in external validation. The model containing all 4 signatures showed better diagnosis ability. Via interacting with M0 macrophage and resting mast cell, these miRNA-mRNA axes may influence tumor microenvironment.

Conclusion: This study established a miRNA-mRNA network via bioinformatic analysis and experiment validation for STAD.