High expression of APC is an unfavorable prognostic biomarker in T4 gastric cancer patients

Association of APC Expression with Its Promoter Methylation Status and the Prognosis of Hepatocellular Carcinoma

OBJECTIVE

The present study was aimed to investigate the APC expression, its promoter methylation status, the expression of β-Catenin, c-Myc and Cyclin D1 and further explore their prognostic value in Hepatocellular carcinoma (HCC).

PATIENTS AND METHODS

Serum samples from 90 HCC patients and 27 healthy donors were collected in this study. The methylation-specific PCR (MSP) was performed to evaluate promoter methylation status of APC gene. RT-qPCR was used to detect the mRNA expression of APC, β-Catenin, c-Myc and Cyclin D1, meanwhile the protein expression were analyzed by Western blot.

RESULTS

The positive rate of APC gene methylation in HCC patients (46.67%) was higher than healthy donors (11.11%). APC gene exhibited marked hypermethylation in the patients of TNM III-IV stage when compared to the patients of TNM I-II stage , the methylation status of APC gene was correlated with tumor size and lymph node metastasis whereas the APC gene methylation showed no relationship with the patient's sex and age. APC methylation may be associated with APC expression level, APC expression in HCC cells is silenced by aberrant promoter hypermethylation. In HCC patients with methylated APC, the mRNA and protein expression of β-Catenin, c-Myc and Cyclin D1 were higher than the unmethylated patient subgroup and healthy donors.

CONCLUTIONS

The downregulation of APC in HCC samples was associated with promoter hypermethylation. APC methylation could be used as a novel diagnostic biomarker in HCC, which was associated with regulation of Wnt/β-Catenin signal pathway.

To Identify Adenomatous Polyposis Coli Gene Mutation as a Predictive Marker of Endometrial Cancer Immunotherapy

The adenomatous polyposis coli (APC) gene is the chromatin-remodeling-related gene and a typical tumor suppressor. Patients with a high expression of programmed death-ligand 1 (PD-L1) or a high level of tumor mutational burden (TMB) may benefit from immunotherapy in endometrial cancer (EC). This study aimed to demonstrate the role of APC in the diagnosis and immunotherapy treatment of EC. We performed an integrative analysis of a commercial panel including 520 cancer-related genes on 99 tumors from an endometrial cancer cohort in China and DNA-seq data from The Cancer Genome Atlas (TCGA) to identify new gene mutations as endometrial cancer immunotherapy markers. We found that the significant mutant genes that correlated with the PD-L1 expression and TMB were related to the chromatin state and generated a discovery set having 12 mutated genes, including the APC gene, which was identified as a new marker for immunotherapy. Further analysis revealed that tumors with the APC mutation had high TMB, increased expression of PD-L1, and increased lymphocytic infiltration. Next, we verified that APC has an inactive mutation in EC, which may affect the immune response, including PD-L1 expression, microsatellite instability, and lymphocytic infiltrate. Furthermore, patients with the APC mutation had longer overall survival. Our study demonstrates that APC could play an important role in enhancing the response to endometrial cancer treatment, particularly immunotherapy.

Clinicopathological characteristics, survival outcomes, and genetic alterations of younger patients with gastric cancer: Results from the China National Cancer Center and cBioPortal datasets

Background

The survival outcomes of younger patients with gastric cancer (GC) have remained controversial. This study explores the clinicopathological characteristics, survival outcomes, and genetic alterations of younger and older patients with GC.

Methods

Patients with GC were identified from the China National Cancer Center Gastric Cancer Database (NCCGCDB) during 1998–2018. Survival analysis was conducted using Kaplan–Meier estimates and Cox proportional hazards models. Sequencing datasets were enrolled from The Cancer Genome Atlas (TCGA) and Memorial Sloan–Kettering Cancer Center (MSKCC) databases.

Results

A total of 1146 younger (<40 years of age) and 16,988 older (≥40 years of age) cases were included in the study. Younger patients had more poorly differentiated lesions than older patients (53.7% vs. 33.8%, respectively; p < 0.0001), and were more often pTNM stage IV (19.5% vs. 11.8%, respectively; p < 0.001). The 5‐year overall survival (OS) of patients from the NCCGCDB increased from 1998 to 2018. Younger patients with pTNM stage III had a lower survival rate than older patients (p = 0.014), while no differences by age were observed at other stages. The mutation frequency of the LRP1B, GNAS, APC, and KMT2D genes was higher for older than younger patients (p < 0.05 for all genes). While not significantly different, younger patients from the TCGA and MSKCC databases were more likely to have CDH1, RHOA, and CTNNB1 gene mutations.

Conclusions

A stable proportion and improved survival of younger patients were reported using NCCGCDB data. Younger patients with pTNM stage III had lower rates of survival than older patients. Distinct molecular characteristics were identified in younger GC patients which may partly explain the histopathology and prognosis specific to this subpopulation.

A pan-cancer analysis on the carcinogenic effect of human adenomatous polyposis coli

Adenomatous polyposis coli (APC) is the most commonly mutated gene in colon cancer and can cause familial adenomatous polyposis (FAP). Hypermethylation of the APC promoter can also promote the development of breast cancer, indicating that APC is not limited to association with colorectal neoplasms. However, no pan-cancer analysis has been conducted. We studied the location and structure of APC and the expression and potential role of APC in a variety of tumors by using The Cancer Genome Atlas and Gene Expression Omnibus databases and online bioinformatics analysis tools. The APC is located at 5q22.2, and its protein structure is conserved among H. sapiens, M. musculus with C. elaphus hippelaphus. The APC identity similarity between homo sapiens and mus musculus reaches 90.1%. Moreover, APC is highly specifically expressed in brain tissues and bipolar cells but has low expression in most cancers. APC is mainly expressed on the cell membrane and is not detected in plasma by mass spectrometry. APC is low expressed in most tumor tissues, and there is a significant correlation between the expressed level of APC and the main pathological stages as well as the survival and prognosis of tumor patients. In most tumors, APC gene has mutation and methylation and an enhanced phosphorylation level of some phosphorylation sites, such as T1438 and S2260. The expressed level of APC is also involved in the level of CD8+ T-cell infiltration, Tregs infiltration, and cancer-associated fibroblast infiltration. We conducted a gene correlation study, but the findings seemed to contradict the previous analysis results of the low expression of the APC gene in most cancers. Our research provides a comparative wholesale understanding of the carcinogenic effects of APC in various cancers, which will help anti-cancer research.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study

BACKGROUND

Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.

OBJECTIVE

This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.

METHODS

We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic's electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.

RESULTS

With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.

CONCLUSIONS

Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

Establishment of a 5-gene risk model related to regulatory T cells for predicting gastric cancer prognosis

Background

Gastric cancer (GC) is one of the high-risk cancers that lacks effective methods for prognosis prediction. Therefore, we searched for immune cells related to the prognosis of GC and studied the role of related genes in GC prognosis.

Methods

In this study, we collected the mRNA data of GC from The Cancer Genome Atlas (TCGA) database and studied the immune cells that were closely related to the prognosis of GC. Spearman correlation analysis was performed to show the association between immune cell-related genes and the differentially expressed genes (DEGs) of GC. Univariate and multivariate Cox regression analyses were conducted on the immune cell-related genes with a high correlation with GC. A prognostic risk score model was constructed and the most significant feature genes were identified. Kaplan–Meier method was then used to compare the overall survival (OS) of patients with high-risk and low-risk, and receiver operating characteristic (ROC) analysis was used to assess the accuracy of the risk model. In addition, GC patients were grouped according to the median expression of the features genes, and survival analysis was further carried out.

Results

It was noted that regulatory T cells (Tregs) were significantly correlated with the prognosis of GC, and 172 genes related to Tregs were found to be closely associated with GC. An optimal prognostic risk model was constructed, and a 5-gene (including LRFN4, ADAMTS12, MCEMP1, HP and MUC15) signature-based risk score was established. Survival analysis showed significant difference in OS between low-risk and high-risk samples. ROC analysis results indicated that the risk model had a high accuracy for the prognosis prediction of samples (AUC = 0.717). The results of survival analysis on each feature gene based on expression levels were consistent with the results of multivariate Cox analysis for predicting the risk rate of the 5 genes.

Conclusion

These results proved that the 5-gene signature-based risk score could be used to predict the survival of GC patients, and these 5 genes were closely related to Tregs. These findings are of great significance for studying the role of immune cells and related immune factors in regulating the prognosis of GC.