Long noncoding RNAs (lncRNAs) function as regulators and play critical roles in diverse biological processes, however, the majority of lncRNAs are not characterized, and their roles in regulation remain to be elucidat...
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Efforts on cancer genomic study have revealed that mutations in cancer-driving oncogenes tend to mutually exclusive. To understand this tumorigenic mechanism and common cancer features, large-scale genomic study and m...
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ISBN:
(纸本)9781728118680
Efforts on cancer genomic study have revealed that mutations in cancer-driving oncogenes tend to mutually exclusive. To understand this tumorigenic mechanism and common cancer features, large-scale genomic study and molecular analyses across many cancer types are essential. We compiled somatic mutation and transcriptome profiles for a pan-cancer dataset that contains tumor tissue samples from 32 cancer types for over 10,000 patients. The genomic and molecular analysis was applied to four pan-cancer subgroups including pan-gastrointestinal, pan-gynecological, pan-kidney and pan-squamous, as well as the entire patient group. We identified seven genes, TP53, PIK3CA, PTEN, PIK3R1, LRP1B, CDH1, and TRRAP exhibited mutually exclusive mutation patterns across all cancer types (adjusted ). The mutations in these genes occurred in 55.9% of all cancer patients and over 70% of patients with pan-gastrointestinal, pan-gynecological and pan-squamous. Excepting LRP1B, the other six genes are included in the known cancer pathways. Notably, TP53, PIK3CA, and PIK3R1 are involved in over 20 cancer-related pathways. The co-expression network analysis suggested that LRP1B was associated with multiple genes that showed significant enrichment in Wnt signal pathway (adjusted p=0.0023) and ERBB signal pathway (adjusted p=0.0097). We further constructed a regulatory network to infer novel regulations of LRP1B in cancer. In addition, based on the mutations in the seven genes, we divided the patients into different groups for survival analysis. We found that the survival rate of the patient group with TP53 mutations was significantly shorter than in other groups (log rank p = 5.88e-10) in pan-gynecological. Our analysis systematically revealed the mutually exclusive mutation patterns of seven cancer genes across 32 cancer types. It helps us to better understanding the relations among different cancer types and pan-cancer subgroups, providing new insights into common mechanisms underlying diver
Breast cancer is the second most common cancer diagnosed with women in the United States. Treatment of the disease has been substantially improved in recent years. However, the 5-year survival rate of patients with di...
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ISBN:
(数字)9781728155845
ISBN:
(纸本)9781728155852
Breast cancer is the second most common cancer diagnosed with women in the United States. Treatment of the disease has been substantially improved in recent years. However, the 5-year survival rate of patients with distant metastatic breast cancer is still below 30%. The transcription factors (TFs) regulate the expression of many genes and are commonly mutated in cancer. It has been shown that targeting transcript factor activity is effective in cancer treatment. In this study, we performed a large-scale integrative analysis to reveal the TFs that play essential regulatory roles in disease development. We first constructed expression networks based on gene expression correlation patterns in normal and tumor tissues. Incorporating the Bayesian analysis, TF and target interactions and methylation analysis with the expression networks, we identified a total of 19 regulatory networks consisting of differentially expressed genes. Gene set enrichment analysis of TF targets further revealed four core regulatory networks that were mainly controlled by 10 transcription factors. The dysregulated gene networks and their major TF regulators inferred by our study provide new insights into the regulatory mechanisms of breast cancer and offer new therapeutic targets.
作者:
Wenjuan ZhangAlex LeeMary Qu YangInformation Science Department
MidSouth Bioinformatics Center and Bioinformatics Graduate Program University of Arkansas Little Rock and Univ. of Arkansas for Medical Sciences University of Arkansas Little Rock Little Rock USA Biology Department
University of Arkansas Little Rock Little Rock USA
The immune system plays a central role in recognizing and eliminating cancerous cells. The activity and functionality of immune cells within the tumor microenvironment can significantly impact the prognosis of cancer ...
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ISBN:
(数字)9798350361513
ISBN:
(纸本)9798350372304
The immune system plays a central role in recognizing and eliminating cancerous cells. The activity and functionality of immune cells within the tumor microenvironment can significantly impact the prognosis of cancer patients. Leveraging single-cell RNA sequencing technology, we conducted an in-depth investigation into single-cell-based gene signatures for predicting cancer patient survival. The study utilized single-cell RNA sequencing data from breast cancer patients, encompassing over 14,875 immune cells. Gene signatures from major innate immune cell types, including Natural Killer (NK) cells, Macrophages, and Neutrophils, as well as adaptive immune cells, including Cytotoxic T Lymphocytes (T cells) and B lymphocytes (B cells), were identified. To elucidate their relationship in breast cancer patients, we constructed Cox proportional hazard regression models for predicting patient survival, utilizing the concordance index as a measure of prediction model performance. The evaluation incorporated three independent breast cancer patient cohorts: TCGA (The Cancer Genome Atlas program), METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), and UK breast cancer data. Our results showed that the T cell-based signature model outperformed the NK cell-based and Neutrophil-based prediction models. Moreover, the
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cell-based signature model exhibited robust performance across independent patient datasets. These findings suggest that gene signatures derived from cells of the adaptive immune system have a better survival prediction value than those derived from the innate immune system, underscoring the potential clinical relevance of harnessing the adaptive immune response for refining prognostic assessments in breast cancer. These insights contribute to advancing our understanding of the complex interplay between immune cells and cancer prognosis, paving the way for more tailored and effective therapeutic interventions.
The emergence of single-cell sequencing technologies has enabled the production of high-resolution data at the individual cell level, providing unprecedented opportunities to capture cell population diversity and diss...
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ISBN:
(纸本)9781665458429
The emergence of single-cell sequencing technologies has enabled the production of high-resolution data at the individual cell level, providing unprecedented opportunities to capture cell population diversity and dissect the cellular heterogeneity of complex diseases. At the same time, relatively high biological and technical noise poses new challenges for single-cell data analysis. The single-cell RNA sequencing (scRNA-seq) data often contains substantial missing values due to gene dropout events. Here, we developed a convolutional neural network based model to recover missing values for scRNA-seq data. We first calculated the probability of dropout employing gamma-normal expectation maximum algorithm. Unlike most existing approaches, our model only recovered the expression values that have a dropout probability larger than a threshold. The mean square error and Pearson correlation coefficient were used to assess the accuracy of predicted expression values. The purity and entropy were computed to measure the homogeneity of cell clusters using imputed gene expression profiles. Across various scRNAseq datasets, our model demonstrated robust performance and achieved comparable or better results compared to the other imputation methods.
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