Cancer Biomarkers最新文献

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.

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.

Epithelial membrane protein 3 (EMP3) belongs to the peripheral myelin protein 22 kDa (PMP22) gene family, characterized by four transmembrane domains and widespread expression across various human tissues and organs. Other members of the PMP22 family, including EMP1, EMP2, and PMP22, have been linked to various cancers, such as glioblastoma, laryngeal cancer, nasopharyngeal cancer, gastric cancer, breast cancer, and endometrial cancer. However, few studies report on the function and relevance of EMP3 in tumorigenicity. Given the significant structural similarities among members of the PMP22 family, there are likely potential functional similarities as well. Previous studies have established the regulatory role of EMP3 in immune cells like T cells and macrophages. Additionally, EMP3 is found to be involved in critical signaling pathways, including HER-2/PI3K/Akt, MAPK/ERK, and TGF-beta/Smad. Furthermore, EMP3 is associated with cell cycle regulation, cellular proliferation, and apoptosis. Hence, it is likely that EMP3 participates in cancer development through these aforementioned pathways and mechanisms. This review aims to systematically examine and summarize the structure and function of EMP3 and its association to various cancers. EMP3 is expected to emerge as a significant biological marker for tumor prognosis and a potential target in cancer therapeutics.

Background: WEE1 is a critical kinase in the DNA damage response pathway and has been shown to be effective in treating serous uterine cancer. However, its role in gliomas, specifically low-grade glioma (LGG), remains unclear. The impact of DNA methylation on WEE1 expression and its correlation with the immune landscape in gliomas also need further investigation.

Methods: This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) and utilized various bioinformatics tools to analyze gene expression, survival, gene correlation, immune score, immune infiltration, genomic alterations, tumor mutation burden, microsatellite instability, clinical characteristics of glioma patients, WEE1 DNA methylation, prognostic analysis, single-cell gene expression distribution in glioma tissue samples, and immunotherapy response prediction based on WEE1 expression.

Results: WEE1 was upregulated in LGG and glioblastoma (GBM), but it had a more significant prognostic impact in LGG compared to other cancers. High WEE1 expression was associated with poorer prognosis in LGG, particularly when combined with wild-type IDH. The WEE1 inhibitor MK-1775 effectively inhibited the proliferation and migration of LGG cell lines, which were more sensitive to WEE1 inhibition. DNA methylation negatively regulated WEE1, and high DNA hypermethylation of WEE1 was associated with better prognosis in LGG than in GBM. Combining WEE1 inhibition and DNA methyltransferase inhibition showed a synergistic effect. Additionally, downregulation of WEE1 had favorable predictive value in immunotherapy response. Co-expression network analysis identified key genes involved in WEE1-mediated regulation of immune landscape, differentiation, and metastasis in LGG.

Conclusion: Our study shows that WEE1 is a promising indicator for targeted therapy and prognosis evaluation. Notably, significant differences were observed in the role of WEE1 between LGG and GBM. Further investigation into WEE1 inhibition, either in combination with DNA methyltransferase inhibition or immunotherapy, is warranted in the context of LGG.

Background: Cms1 ribosomal small subunit homolog (CMSS1) is an RNA-binding protein that may play an important role in tumorigenesis and development.

Objective: RNA-seq data from the GEPIA database and the UALCAN database were used to analyze the expression of CMSS1 in liver hepatocellular carcinoma (LIHC) and its relationship with the clinicopathological features of the patients.

Methods: LinkedOmics was used to identify genes associated with CMSS1 expression and to identify miRNAs and transcription factors significantly associated with CMSS1 by GSEA.

Results: The expression level of CMSS1 in hepatocellular carcinoma tissues was significantly higher than that in normal tissues. In addition, the expression level of CMSS1 in advanced tumors was significantly higher than that in early tumors. The expression level of CMSS1 was higher in TP53-mutated tumors than in non-TP53-mutated tumors. CMSS1 expression levels were strongly correlated with disease-free survival (DFS) and overall survival (OS) in patients with LIHC, and high CMSS1 expression predicted poorer OS (P< 0.01) and DFS (P< 0.01). Meanwhile, our results suggested that CMSS1 is associated with the composition of the immune microenvironment of LIHC.

Conclusions: The present study suggests that CMSS1 is a potential molecular marker for the diagnosis and prognostic of LIHC.

Background: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images.

Objective: To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques.

Methods: This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models.

Results: Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images.

Conclusion: This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

Background: The correlation between the preoperative albuminalkaline phosphatase ratio (AAPR) and the prognosis of hepatocellular carcinoma (HCC) patients after radical resection is still not comprehensive.

Objective: This study aims to observe the correlation between preoperative AAPR and the prognosis of HCC patients after radical resection.

Methods: We constructed a retrospective cohort study and included 656 HCC patients who underwent radical resection. The patients were grouped after determining an optimum AAPR cut-off value. We used the Cox proportional regression model to assess the correlation between preoperative AAPR and the prognosis of HCC patients after radical resection.

Results: The optimal cut-off value of AAPR for assessing the prognosis of HCC patients after radical resection was 0.52 which was acquired by using X-tile software. Kaplan-Meier analysis curves showed that a low AAPR (⩽ 0.52) had a significantly lower rate of overall survival (OS) and recurrence-free survival (RFS) (P< 0.05). Multiple Cox proportional regression showed that an AAPR > 0.52 was a protective factor for OS (HR = 0.66, 95%CI 0.45-0.97, p= 0.036) and RFS (HR = 0.70, 95% CI 0.53-0.92, p= 0.011).

Conclusions: The preoperative AAPR level was related to the prognosis of HCC patients after radical resection and can be used as a routine preoperative test, which is important for early detection of high-risk patients and taking personalized adjuvant treatment.

Background: Abelson interactor 1 (ABI1) is associated with the metastasis and prognosis of many malignancies. The association between ABI1 transcript spliced variants, their molecular constitutive exons and exon-exon junctions (EEJs) in 14 cancer types and clinical outcomes remains unsolved.

Objective: To identify novel cancer metastatic and prognostic biomarkers from ABI1 total mRNA, TSVs, and molecular constitutive elements.

Methods: Using data from TCGA and TSVdb database, the standard median of ABI1 total mRNA, TSV, exon, and EEJ expression was used as a cut-off value. Kaplan-Meier analysis, Chi-squared test (X2) and Kendall's tau statistic were used to identify novel metastatic and prognostic biomarkers, and Cox regression analysis was performed to screen and identify independent prognostic factors.

Results: A total of 35 ABI1-related factors were found to be closely related to the prognosis of eight candidate cancer types. A total of 14 ABI1 TSVs and molecular constitutive elements were identified as novel metastatic and prognostic biomarkers in four cancer types. A total of 13 ABI1 molecular constitutive elements were identified as independent prognostic biomarkers in six cancer types.

Conclusions: In this study, we identified 14 ABI1-related novel metastatic and prognostic markers and 21 independent prognostic factors in total 8 candidate cancer types.