Cancer Biomarkers最新文献

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: 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: 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: 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.

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: Lung adenocarcinoma (LUAD) is a major histological subtype of lung cancer with a high mortality rate worldwide. Heat shock protein family D member 1 (HSPD1, also known as HSP60) is reported to be increased in tumor tissues of lung cancer patients compared with healthy control tissues.

Objective: We aimed to investigate the roles of HSPD1 in prognosis, carcinogenesis, and immune infiltration in LUAD using an integrative bioinformatic analysis.

Methods: HSPD1 expression in LUAD was investigated in several transcriptome-based and protein databases. Survival analysis was performed using the KM plotter and OSluca databases, while prognostic significance was independently confirmed through univariate and multivariate analyses. Integrative gene interaction network and enrichment analyses of HSPD1-correlated genes were performed to investigate the roles of HSPD1 in LUAD carcinogenesis. TIMER and TISIDB were used to analyze correlation between HSPD1 expression and immune cell infiltration.

Results: The mRNA and protein expressions of HSPD1 were higher in LUAD compared with normal tissues. High HSPD1 expression was associated with male gender and LUAD with advanced stages. High HSPD1 expression was an independent prognostic factor associated with poor survival in LUAD patients. HSPD1-correlated genes with prognostic impact were mainly involved in aberrant ribosome biogenesis, while LUAD patients with high HSPD1 expression had low tumor infiltrations of activated and immature B cells and CD4+ T cells.

Conclusions: HSPD1 may play a role in the regulation of ribosome biogenesis and B cell-mediated immunity in LUAD. It could serve as a predictive biomarker for prognosis and immunotherapy response in LUAD.

Cuproptosis a novel copper-dependent cell death modality, plays a crucial part in the oncogenesis, progression and prognosis of tumors. However, the relationships among DNA-methylation located in cuproptosis-related genes (CRGs), overall survival (OS) and the tumor microenvironment remain undefined. In this study, we systematically assessed the prognostic value of CRG-located DNA-methylation for lower-grade glioma (LGG). Clinical and molecular data were sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We employed Cox hazard regression to examine the associations between CRG-located DNA-methylation and OS, leading to the development of a prognostic signature. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses were utilized to gauge the accuracy of the signature. Gene Set Enrichment Analysis (GSEA) was applied to uncover potential biological functions of differentially expressed genes between high- and low-risk groups. A three CRG-located DNA-methylation prognostic signature was established based on TCGA database and validated in GEO dataset. The 1-year, 3-year, and 5-year area under the curve (AUC) of ROC curves in the TCGA dataset were 0.884, 0.888, and 0.859 while those in the GEO dataset were 0.943, 0.761 and 0.725, respectively. Cox-regression-analyses revealed the risk signature as an independent risk factor for LGG patients. Immunogenomic profiling suggested that the signature was associated with immune infiltration level and immune checkpoints. Functional enrichment analysis indicated differential enrichment in cell differentiation in the hindbrain, ECM receptor interactions, glycolysis and reactive oxygen species pathway across different groups. We developed and verified a novel CRG-located DNA-methylation signature to predict the prognosis in LGG patients. Our findings emphasize the potential clinical implications of CRG-located DNA-methylation indicating that it may serve as a promising therapeutic target for LGG patients.

Background: Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations.

Objective: This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images.

Methods: The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF).

Results: The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%).

Conclusion: Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.