Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival

PRR13 expression as a prognostic biomarker in breast cancer: correlations with immune infiltration and clinical outcomes

Introduction

Breast cancer continues to be a primary cause of cancer-related mortality among women globally. Identifying novel biomarkers is essential for enhancing patient prognosis and informing therapeutic decisions. The PRR13 gene, associated with taxol resistance and the progression of various cancers, remains under-characterized in breast cancer. This study aimed to investigate the role of PRR13 in breast cancer and its potential as a prognostic biomarker.

Methods

We performed a comparative analysis of PRR13 gene expression utilizing the TCGA database against non-cancerous tissues and employed STRING to evaluate PRR13's protein-protein interactions and associated pathways. Additionally, we investigated the relationship between PRR13 mRNA expression and immune cell infiltration in breast cancer (BRCA) using two methodologies. Furthermore, a retrospective analysis of 160 patients was conducted, wherein clinical data were collected and PRR13 expression was evaluated through immunohistochemistry and qRT-PCR to determine its association with clinicopathological features and patient survival.

Results

Analysis of the TCGA database revealed significant upregulation of PRR13 expression across 12 different cancer types, including breast cancer. High PRR13 expression was positively correlated with various immune cells, including NK cells, eosinophils, Th17 cells, and mast cells, whereas a negative correlation was observed with B cells, macrophages, and other immune subsets. Enrichment analysis of PRR13 and its 50 interacting proteins revealed significant associations with biological processes such as cell adhesion and migration, and pathways including ECMreceptor interaction and PI3K-Akt signaling. Single-cell analysis demonstrated associations between PRR13 and pathways pertinent to inflammation and apoptosis. Validation studies confirmed elevated PRR13 expression in tumor tissue compared to adjacent non-cancerous tissue. Immunohistochemistry demonstrated high PRR13 expression in 55.6% of cancer cases, particularly associated with advanced clinical stage and lymph node metastasis. Moreover, high PRR13 expression significantly correlated with shorter overall survival and served as an independent prognostic factor. Subgroup analysis underscored the prognostic significance of PRR13 in aggressive tumor subtypes, with particularly strong associations observed in T3, N1-3, and moderately to poorly differentiated tumors.

Discussion

In conclusion, PRR13 expression is upregulated in breast cancer tissues and may serve as a valuable prognostic indicator for breast cancer patients, potentially impacting patient survival and therapeutic strategies.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Hsp90 is a potential risk factor for ovarian cancer prognosis: an evidence of a Chinese clinical center

BACKGROUND

The potential treatment effects of heat shock protein 90 (Hsp90) inhibitors in ovarian cancer (OC) are controversial. This research aims to investigate the relationship between the level of Hsp90 in peripheral blood and the prognosis of OC patients, as well as the clinicopathological indicators.

MATERIALS AND METHODS

We retrospectively collected the clinicopathological indicators of OC patients who were admitted to the Department of Obstetrics and Gynecology of the First Affiliated Hospital of Anhui Medical University from 2017 to 2022. Hsp90 level in patient blood was detected by enzyme-linked immunosorbent assay, and the correlation between Hsp90 level and OC prognosis was systematically investigated. Kaplan-Meier method was used to draw the survival curve, and the average survival time and survival rate were calculated. The log-rank test and Cox model were used for univariate survival analysis, and the Cox proportional hazards model was applied for multivariate survival analysis. Based on the TCGA dataset of OC obtained by cBioPortal, Pearson's correlation coefficients between Hsp90 level values and other mRNA expression values were calculated to further conduct bioinformatics analysis. GSEA and GSVA analysis were also conducted for gene functional enrichment. The expression of Hsp90 in OC tissues were evaluated and compared by Immunohistochemical staining.

RESULTS

According to the established screening criteria, 106 patients were selected. The enzyme-linked immunosorbent assay results showed that 50.94% OC patients with abnormal Hsp90 level. According to the outcome of Kaplan-Meier curves, the results revealed that the abnormal level of Hsp90 was suggested to poor prognosis (P = 0.001) of OC patients. Furthermore, the result of multivariate Cox proportional hazards regression model analysis also predicted that abnormal Hsp90 level (HR = 2.838, 95%CI = 1.139-7.069, P = 0.025) was linked to poor prognosis, which could be an independent prognostic factor for the prognosis of OC patients. Moreover, top 100 genes screened by Pearson's value associated with Hsp90, indicating that Hsp90 participated in the regulation of ATF5 target genes, PRAGC1A target genes and BANP target genes and also enriched in the metabolic processes of cell response to DNA damage stimulus, response to heat and protein folding.

CONCLUSION

Hsp90 level is positively associated with OC mortality and is a potential prognostic indicator of OC.

Sources of Cancer Neoantigens beyond Single-Nucleotide Variants

The success of checkpoint blockade therapy against cancer has unequivocally shown that cancer cells can be effectively recognized by the immune system and eliminated. However, the identity of the cancer antigens that elicit protective immunity remains to be fully explored. Over the last decade, most of the focus has been on somatic mutations derived from non-synonymous single-nucleotide variants (SNVs) and small insertion/deletion mutations (indels) that accumulate during cancer progression. Mutated peptides can be presented on MHC molecules and give rise to novel antigens or neoantigens, which have been shown to induce potent anti-tumor immune responses. A limitation with SNV-neoantigens is that they are patient-specific and their accurate prediction is critical for the development of effective immunotherapies. In addition, cancer types with low mutation burden may not display sufficient high-quality [SNV/small indels] neoantigens to alone stimulate effective T cell responses. Accumulating evidence suggests the existence of alternative sources of cancer neoantigens, such as gene fusions, alternative splicing variants, post-translational modifications, and transposable elements, which may be attractive novel targets for immunotherapy. In this review, we describe the recent technological advances in the identification of these novel sources of neoantigens, the experimental evidence for their presentation on MHC molecules and their immunogenicity, as well as the current clinical development stage of immunotherapy targeting these neoantigens.

Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies

Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.

Screening and Identification of Survival-Associated Splicing Factors in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is a disease with high morbidity and mortality. Many studies have shown that aberrant alternative splicing (AS) can lead to tumorigenesis, and splicing factors (SFs) serve as an important function during AS. In this research, we propose an analysis method based on synergy to screen key factors that regulate the initiation and progression of LUSC. We first screened alternative splicing events (ASEs) associated with survival in LUSC patients by bivariate Cox regression analysis. Then an association network consisting of OS-ASEs, SFs, and their targeting relationship was constructed to identify key SFs. Finally, 10 key SFs were selected in terms of degree centrality. The validation on TCGA and cross-platform GEO datasets showed that some SFs were significantly differentially expressed in cancer and paracancer tissues, and some of them were associated with prognosis, indicating that our method is valid and accurate. It is expected that our method would be applied to a wide range of research fields and provide new insights in the future.

A systematic view of computational methods for identifying driver genes based on somatic mutation data

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.

Breast Cancer and the Other Non-Coding RNAs

Breast cancer is very heterogenous and the most common gynaecological cancer, with various factors affecting its development. While its impact on human lives and national health budgets is still rising in almost all global areas, many molecular mechanisms affecting its onset and development remain unclear. Conventional treatments still prove inadequate in some aspects, and appropriate molecular therapeutic targets are required for improved outcomes. Recent scientific interest has therefore focused on the non-coding RNAs roles in tumour development and their potential as therapeutic targets. These RNAs comprise the majority of the human transcript and their broad action mechanisms range from gene silencing to chromatin remodelling. Many non-coding RNAs also have altered expression in breast cancer cell lines and tissues, and this is often connected with increased proliferation, a degraded extracellular environment, and higher endothelial to mesenchymal transition. Herein, we summarise the known abnormalities in the function and expression of long non-coding RNAs, Piwi interacting RNAs, small nucleolar RNAs and small nuclear RNAs in breast cancer, and how these abnormalities affect the development of this deadly disease. Finally, the use of RNA interference to suppress breast cancer growth is summarised.

Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles

Background and Objectives: Breast cancer is a heterogeneous disease categorized into four subtypes. Previous studies have shown that copy number alterations of several genes are implicated with the development and progression of many cancers. This study evaluates the effects of DNA copy number alterations on gene expression levels in different breast cancer subtypes. Materials and Methods: We performed a computational analysis integrating copy number alterations and gene expression profiles in 1024 breast cancer samples grouped into four molecular subtypes: luminal A, luminal B, HER2, and basal. Results: Our analyses identified several genes correlated in all subtypes such as KIAA1967 and MCPH1. In addition, several subtype-specific genes that showed a significant correlation between copy number and gene expression profiles were detected: SMARCB1, AZIN1, MTDH in luminal A, PPP2R5E, APEX1, GCN5 in luminal B, TNFAIP1, PCYT2, DIABLO in HER2, and FAM175B, SENP5, SCAF1 in basal subtype. Conclusions: This study showed that computational analyses integrating copy number and gene expression can contribute to unveil the molecular mechanisms of cancer and identify new subtype-specific biomarkers.

Identification of Survival-Associated Alternative Splicing Signatures in Lung Squamous Cell Carcinoma

Purpose: Alternative splicing (AS) is a post-transcriptional process that plays a significant role in enhancing the diversity of transcription and protein. Accumulating evidences have demonstrated that dysregulation of AS is associated with oncogenic processes. However, AS signature specifically in lung squamous cell carcinoma (LUSC) remains unknown. This study aimed to evaluate the prognostic values of AS events in LUSC patients.

Methods: The RNA-seq data, AS events data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify survival-related AS events and survival-related parent genes were subjected to Gene Ontology enrichment analysis and gene network analysis. The least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression analysis were used to construct prognostic prediction models, and their predictive values were assessed by Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Then a nomogram was established to predict the survival of LUSC patients. And the interaction network of splicing factors (SFs) and survival-related AS events was constructed by Spearman correlation analysis and visualized by Cytoscape.

Results: Totally, 467 LUSC patients were included in this study and 1,991 survival-related AS events within 1,433 genes were identified. SMAD4, FOS, POLR2L, and RNPS1 were the hub genes in the gene interaction network. Eight prognostic prediction models (seven types of AS and all AS) were constructed and all exhibited high efficiency in distinguishing good or poor survival of LUSC patients. The final integrated prediction model including all types of AS events exhibited the best prognostic power with the maximum AUC values of 0.778, 0.816, 0.814 in 1, 3, 5 years ROC curves, respectively. Meanwhile, the nomogram performed well in predicting the 1-, 3-, and 5-year survival of LUSC patients. In addition, the SF-AS regulatory network uncovered a significant correlation between SFs and survival-related AS events.

Conclusion: This is the first comprehensive study to analyze the role of AS events in LUSC specifically, which improves our understanding of the prognostic value of survival-related AS events for LUSC. And these survival-related AS events might serve as novel prognostic biomarkers and drug therapeutic targets for LUSC.

Genome-Wide Profiling of Alternative Splicing Signature Reveals Prognostic Predictor for Esophageal Carcinoma

Background

Alternative splicing (AS) is a molecular event that drives protein diversity through the generation of multiple mRNA isoforms. Growing evidence demonstrates that dysregulation of AS is associated with tumorigenesis. However, an integrated analysis in identifying the AS biomarkers attributed to esophageal carcinoma (ESCA) is largely unexplored.

Methods

AS percent-splice-in (PSI) data were obtained from the TCGA SpliceSeq database. Univariate and multivariate Cox regression analysis was successively performed to identify the overall survival (OS)-associated AS events, followed by the construction of AS predictor through different splicing patterns. Then, a nomogram that combines the final AS predictor and clinicopathological characteristics was established. Finally, a splicing regulatory network was created according to the correlation between the AS events and the splicing factors (SF).

Results

We identified a total of 2389 AS events with the potential to be used as prognostic markers that are associated with the OS of ESCA patients. Based on splicing patterns, we then built eight AS predictors that are highly capable in distinguishing high- and low-risk patients, and in predicting ESCA prognosis. Notably, the area under curve (AUC) value for the exon skip (ES) prognostic predictor was shown to reach a score of 0.885, indicating that ES has the highest prediction strength in predicting ESCA prognosis. In addition, a nomogram that comprises the pathological stage and risk group was shown to be highly efficient in predicting the survival possibility of ESCA patients. Lastly, the splicing correlation network analysis revealed the opposite roles of splicing factors (SFs) in ESCA.

Conclusion

In this study, the AS events may provide reliable biomarkers for the prognosis of ESCA. The splicing correlation networks could provide new insights in the identification of potential regulatory mechanisms during the ESCA development.

Systematic profiling of alternative splicing signature reveals prognostic predictor for prostate cancer

Alternative splicing (AS) provides the primary mechanism for producing protein diversity. There is growing evidence that AS is involved in the development and progression of cancers. The rapid accumulation of high‐throughput sequencing technologies and clinical data sets offers an opportunity to systematically profile the relationship between mRNA variants and clinical outcomes. However, there is a lack of systematic analysis of AS in prostate cancer: Download RNA‐seq data and clinical information from The Cancer Genome Atlas (TCGA) data portal. Evaluate RNA splicing patterns by SpliceSeq and calculate splicing percentage (PSI) values. Different expressions were identified as differently expressed AS events (DEAs) based on PSI values. Bioinformatics methods were used for further analysis of DEAs and their splicing networks. Kaplan‐Meier, Cox proportional regression, and unsupervised cluster analysis were used to assess the correlation between DEAs and clinical characteristics. In total, 43 834 AS events were identified, of which 1628 AS events were differentially expressed. The parental genes of these DEAs played a significant role in the regulation of prostate cancer‐related processes. In total, 226 DEAs events were found to be associated with disease‐free survival. Four clusters of molecules with different survival modes were revealed by unsupervised cluster analysis of DEAs. AS events may be important determinants of prognosis and bio‐modulation in prostate cancer. In this study, we established strong prognostic predictors, discovered a splicing network that may be a potential mechanism, and provided further validated therapeutic targets.

Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma

Growing evidence has revealed that abnormal alternative splicing (AS) events are closely related to carcinogenic processes. However, the comprehensive study on the prognostic value of splicing events involved in uveal melanoma (UM) is still lacking. Therefore, splicing data of 80 UM patients were obtained from the Cancer Genome Atlas (TCGA) SpliceSeq and RNA sequence data of UM and patient clinical features were downloaded from the Cancer Genome Atlas (TCGA) database to identify survival related splicing events in UM. As a result, a total of 37996 AS events of 17911 genes in UM were detected, among which 5299 AS events of 3529 genes were significantly associated with UM patients’ survival. Functional enrichment analysis revealed that this survival related splicing genes are corelated with mRNA catabolic process and ribosome pathway. Based on survival related splicing events, seven types of prognostic markers and the final overall prognostic signature could independently predict the overall survival of UM patients. Finally, an 11 spliced gene was identified in the final signature. On the basis of these 11 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. The results showed that the 11 genes could determine short- and long-term survival with a predicted accuracy of 97.5%. Besides, the splicing factors and alternative splicing events correlation network was constructed to serve as therapeutic targets for UM treatment. Thus, our study depicts a comprehensive landscape of alternative splicing events in the prognosis of UM. The correlation network and associated pathways would provide additional potential targets for therapy and prognosis.

Systematic analysis of survival-associated alternative splicing signatures in clear cell renal cell carcinoma

Alternative splicing (AS) constitutes a major reason for messenger RNA (mRNA) and protein diversity. Increasing studies have shown a link to splicing dysfunction associated with malignant neoplasia. Systematic analysis of AS events in kidney cancer remains poorly reported. Therefore, we generated AS profiles in 533 kidney renal clear cell carcinoma (KIRC) patients in The Cancer Genome Atlas (TCGA) database using RNA-seq data. Then, prognostic models were developed in a primary cohort (N = 351) and validated in a validation cohort (N = 182). In addition, splicing networks were built by integrating bioinformatics analyses. A total of 11 268 and 8083 AS variants were significantly associated with patient overall survival time in the primary and validation KIRC cohorts, respectively, including STAT1, DAZAP1, IDS, NUDT7, and KLHDC4. The AS events in the primary KIRC cohorts served as candidate AS events to screen the independent risk factors associated with survival in the primary cohort and to develop prognostic models. The area under the curve of the receiver-operator characteristic curve for prognostic prediction in the primary and validation KIRC cohorts was 0.84 and 0.82 at 2500 days of overall survival, respectively. In addition, splicing correlation networks revealed key splicing factors (SFs) in KIRC, such as HNRNPH1, HNRNPU, KHDBS1, KHDBS3, SRSF9, RBMX, SFQ, SRP54, HNRNPA0, and SRSF6. In this study, we analyzed the AS landscape in the TCGA KIRC cohort and detected predictors (prognostic) based on AS variants with high performance for risk stratification of the KIRC cohort and revealed key SFs in splicing networks, which could act as underlying mechanisms.

Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events

Cholangiocarcinoma (CCA) is a type of malignant tumor that originates in the mucosal epithelial cells of the biliary system. It is a highly aggressive cancer that progresses rapidly, has low surgical resection rates and a high recurrence. At present, no prognostic molecular biomarker for CCA has been identified. However, CCA progression is affected by mRNA precursors that modify gene expression levels and protein structures through alternative splicing (AS) events, which create molecular indicators that may potentially be used to predict CCA outcomes. The present study aimed to construct a model to predict CCA prognosis based on AS events. Using prognostic data available from The Cancer Genome Atlas, including the percent spliced index of AS events obtained from TCGASpliceSeq in 32 CCA cases, univariate and multivariate Cox regression analyses were performed to assess the associations between AS events and the overall survival (OS) rates of patients with CCA. Additional multivariate Cox regression analyses were used to identify AS events that were significantly associated with prognosis, which were used to construct a prediction model with a prognostic index (PI). A receiver operating characteristic (ROC) curve was used to determine the predictive value of the PI, and Pearson's correlation analysis was used to determine the association between OS-related AS events and splicing factors. A total of 38,804 AS events were identified in 9,673 CCA genes, among which univariate Cox regression analysis identified 1,639 AS events associated with OS (P<0.05); multivariate Cox regression analysis narrowed this list to 23 CCA AS events (P<0.001). The final PI model was constructed to predict the survival of patients with CCA; the ROC curve demonstrated that it had a high predictive power for CCA prognosis, with a highest area under the curve of 0.986. Correlations between 23 OS-related AS events and splicing factors were also noted, and may thus, these AS events may be used to improve predictions of OS. In conclusion, AS events exhibited potential for predicting the prognosis of patients with CCA, and thus, the effects of AS events in CCA required further examination.

Differential Cumulative Risk of Genetic Polymorphisms in Familial and Nonfamilial Esophageal Squamous Cell Carcinoma

BACKGROUND

To explore the relationship between family history of esophageal cancer, SNPs, and the risk of esophageal squamous cell carcinoma (ESCC), we performed a population-based case-control study and developed a genetic family history-related risk (GFR) score and non-family history-related risk (GnFR) score to quantify the cumulative number of risk genotypes carried by each individual.

METHODS

We used data of 700 patients with nonfamilial ESCC, 341 patients with familial ESCC, 1,445 controls without a family history of esophageal cancer, and 319 controls with a family history. We genotyped 87 genetic variants associated with the risk for ESCC, and constructed GFR and GnFR scores for cases and controls.

RESULTS

Our results show that ESCC risk increased with higher GFR score (P _trend = 0.0096). Among the familial subgroup, we observed a nearly 7-fold [95% confidence interval (CI), 1.92-24.77] higher risk of ESCC in the highest GFR score group. The corresponding estimate was only 2-fold (95% CI, 1.41-3.93) higher risk of ESCC, in the stratum without a reported family history of esophageal cancer. Certain cell signaling pathways and immune-related pathways were enriched, specifically in familial ESCC. Results from a reconstructed cohort analysis demonstrated that cumulative risk to get esophageal cancer by age 75 years was 13.3%, 10.2%, 8.2%, and 5.1%, respectively, in four subgroups as defined by first-degree relatives of cases or controls with high or low genetic risk score. In particular, the cohort of relatives of ESCC cases with low genetic risk score exhibit a higher cumulative risk than the cohort of relatives of controls with high genetic risk score. It demonstrates that environmental factors play a major role in esophageal cancer.

CONCLUSIONS

Further studies are warranted to dissect the mechanisms of shared environmental and genetic susceptibility affecting the risk of getting ESCC.

IMPACT

Our study highlights that the need of preventive strategies to screen certain genetic polymorphisms, especially in individuals whose relatives had ESCC.

Integration of Cancer Genomics Data for Tree-based Dimensionality Reduction and Cancer Outcome Prediction

Accurate outcome prediction is crucial for precision medicine and personalized treatment of cancer. Researchers have found that multi-dimensional cancer omics studies outperform each data type (mRNA, microRNA, methylation or somatic copy number alteration) study in human disease research. Existing methods leveraging multiple level of molecular data often suffer from various limitations, e. g., heterogeneity, poor robustness or loss of generality. To overcome these limitations, we presented the tree-based dimensionality reduction approach for the identification of smooth tree graph and developed accurate predictive model for clinical outcome prediction. We demonstrated that 1) Discriminative Dimensionality Reduction via learning a Tree (DDRTree) achieved reduced dimension space tree with statistical significance; 2) Tree based support vector machine (SVM) classifier improved prediction performance of cancer recurrence as compared to t-test based SVM classifier; 3) Tree based SVM classifier was robust with regard to the different number of multi-markers; 4) Combining multiple omics data improved prediction performance of cancer recurrence as compared to a single-omics data; and 5) Tree based SVM classifier achieved similar or better prediction performance when compared to the features from state-of-the-art feature selection methods. Our results demonstrated great potential of the tree-based dimensionality reduction approach based clinical outcome prediction.

Survival-Associated Alternative Messenger RNA Splicing Signatures in Pancreatic Ductal Adenocarcinoma: A Study Based on RNA-Sequencing Data

Multiple studies have shown that cancer-specific alternative splicing (AS) alterations are associated with clinical outcome. In this study, we aimed to profile prognostic AS signatures for pancreatic ductal adenocarcinoma (PDAC). We integrated the percent-spliced-in (PSI) data of AS in 140 PDAC patients based on the Cancer Genome Atlas (TCGA) dataset. We identified overall survival (OS)-associated AS events using univariate Cox regression analysis. Then, prognostic AS signatures were constructed for OS and chemoresistance prediction using the least absolute shrinkage and selection operator (LASSO) method. We also analyzed splicing factors (SFs) regulatory networks by Pearson's correlation. We detected 677 OS-related AS events in 485 genes by profiling 10,354 AS events obtained from 140 PDAC patients. Gene functional enrichment analysis demonstrated the pathways enriched by survival-associated AS. The AS signatures constructed with significant survival-associated AS events revealed high performance in predicting PDAC survival and gemcitabine chemoresistance. The area under the receiver operator characteristic curve was 0.937 in training cohort and 0.748 in validation cohort at 2000 days of OS. Furthermore, we identified prognostic SFs (e.g., ESRP1 and HNRNPC) to build the AS regulatory network. We constructed AS signatures for OS and gemcitabine chemoresistance in PDAC patients, which may provide clues for further experiment-based mechanism study.

Profiling of prognostic alternative splicing in melanoma

Alternative splicing can lead to the coding of proteins that act as promoters of cancer, which is associated with the progression of cancer. However, to the best of our knowledge, no systematic survival analysis of alternative splicing in melanoma has previously been reported. The present study conducted an in-depth analysis of integrated alternative splicing events detected in 96 patients with melanoma using data obtained from The Cancer Genome Atlas. Prognostic models and an alternative splicing correlation network were built for patients with melanoma. A total of 41,446 mRNA splicing events were detected in 9,780 genes and 2,348 alternative splicing events were identified to be significantly associated with overall survival of patients with melanoma. Of all the events used in the prognostic model, the model with alternate terminator alternative splicing events exhibited the highest efficiency for evaluating the outcome of patients with melanoma, with an area under the curve of 0.902. The present study identified prognostic predictors for melanoma and revealed alternative splicing networks in melanoma that could indicate underlying mechanisms.

Discovering personalized driver mutation profiles of single samples in cancer by network control strategy

Motivation

It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective.

Results

SCS integrates mutation data and expression data into a reference molecular network for each patient to obtain the driver mutation profiles in a personalized-sample manner. This is the first such a computational framework, to bridge the personalized driver mutation discovery problem and the structural network controllability problem. The key idea of SCS is to detect those mutated genes which can achieve the transition from the normal state to the disease state based on each individual omics data from network controllability perspective. We widely validate the driver mutation profiles of our SCS from three aspects: (i) the improved precision for the predicted driver genes in the population compared with other driver-focus methods; (ii) the effectiveness for discovering the personalized driver genes and (iii) the application to the risk assessment through the integration of the driver mutation signature and expression data, respectively, across the five distinct benchmarks from The Cancer Genome Atlas. In conclusion, our SCS makes efficient and robust personalized driver mutation profiles predictions, opening new avenues in personalized medicine and targeted cancer therapy.

Availability and implementation

The MATLAB-package for our SCS is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm.

Contact

zhangsw@nwpu.edu.cn or zengtao@sibs.ac.cn or lnchen@sibs.ac.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration

The identification of the cancer driver genes is essential for personalized therapy. The mutation frequency of most driver genes is in the middle (2-20%) or even lower range, which makes it difficult to find the driver genes with low-frequency mutations. Other forms of genomic aberrations, such as copy number variations (CNVs) and epigenetic changes, may also reflect cancer progression. In this work, a method for identifying the potential cancer driver genes (iPDG) based on molecular data integration is proposed. DNA copy number variation, somatic mutation, and gene expression data of matched cancer samples are integrated. In combination with the method of iKEEG, the "key genes" of cancer are identified, and the change in their expression levels is used for auxiliary evaluation of whether the mutated genes are potential drivers. For a mutated gene, the concept of mutational effect is defined, which takes into account the effects of copy number variation, mutation gene itself, and its neighbor genes. The method mainly includes two steps: the first step is data preprocessing. First, DNA copy number variation and somatic mutation data are integrated. Then, the integrated data are mapped to a given interaction network, and the diffusion kernel is used to form the mutation effect matrix. The second step is to obtain the key genes by using the iKGGE method, and construct the connection matrix by means of the gene expression data of the key genes and mutation impact matrix of the mutated genes. Experiments on TCGA breast cancer and Glioblastoma multiforme datasets demonstrate that iPDG is effective not only to identify the known cancer driver genes but also to discover the rare potential driver genes. When measured by functional enrichment analysis, we find that these genes are clearly associated with these two types of cancers.

Prognostic alternative mRNA splicing signature in hepatocellular carcinoma: a study based on large-scale sequencing data

Most genes are alternatively spliced and increasing number of evidences show that alternative splicing (AS) is modified and related to tumor progression. Systematic profiles of AS signature in hepatocellular carcinoma (HCC) is absent and urgently needed. Here, differentially spliced AS transcripts between HCC and non-HCC tissues were compared, prognosis-associated AS events by using univariate Cox regression analysis were selected. Our gene functional enrichment analysis demonstrated the potential pathways enriched by survival-associated AS. Prognostic AS signatures were then constructed for HCC prognosis prediction by Lasso regression model. We also analyzed splicing factors (SFs) regulating underlying mechanisms by Pearson correlation and then built corresponding regulatory networks. In addition, we explored the performance of AS signature in the mutated HCC samples. Genome-wide AS events in 377 HCC patients from TCGA were profiled. Among 34 163 AS events in 8985 genes, 3950 AS events in 2403 genes associated with overall survival (OS) significantly for HCC were detected. In addition, computational algorithm results showed that metabolic and ribosome pathways may be the potential molecular mechanisms regulating the poor prognosis. More importantly, survival-associated AS signatures revealed high performance in predicting HCC prognosis. The area under curve for AS signature was 0.806 in all HCC and 0.944 in TP53 mutated HCC samples at 2000 days of OS. We submitted prognostic SFs to build the AS regulatory network, from which we found prognostic AS events were significantly enriched in metabolism-related pathways. A robust AS signature for HCC patients and revealed the regulatory splicing networks contributing to the potential significantly enriched metabolism-related pathways.

Survival-associated alternative splicing signatures in esophageal carcinoma

Alternative splicing (AS), a major mechanism for the enhancement of transcriptome and proteome diversity, has been widely demonstrated to be involved in the full spectrum of oncogenic processes. High-throughput sequencing technology and the rapid accumulation of clinical data sets have provided an opportunity to systemically analyze the association between messenger RNA AS variants and patient clinical outcomes. Here, we compared differentially spliced AS transcripts between esophageal carcinoma (ESCA) and non-tumor tissues, profiled genome-wide survival-associated AS events in 87 patients with esophageal adenocarcinoma (EAC) and 79 patients with esophageal squamous cell carcinoma (ESCC) using The Cancer Genome Atlas (TCGA) RNA-seq data set, and constructed predictive models as well as splicing regulation networks by integrated bioinformatic analysis. A total of 2326 AS events in 1738 genes and 1812 AS events in 1360 genes were determined to be significantly associated with overall survival (OS) of patients in the EAC and ESCC cohorts, respectively, including some essential participants in the oncogenic process. The predictive model of each splice type performed reasonably well in distinguishing good and poor outcomes of patients with esophageal cancer, and values for the area under curve reached 0.942 and 0.815 in the EAC exon skip predictive model and the ESCC alternate acceptor site predictive model, respectively. The splicing regulation networks revealed an interesting correlation between survival-associated splicing factors and prognostic AS genes. In summary, we created prognostic models for patients with esophageal cancer based on AS signatures and constructed novel splicing correlation networks.

Accumulation of potential driver genes with genomic alterations predicts survival of high-risk neuroblastoma patients

Background

Neuroblastoma is the most common pediatric malignancy with heterogeneous clinical behaviors, ranging from spontaneous regression to aggressive progression. Many studies have identified aberrations related to the pathogenesis and prognosis, broadly classifying neuroblastoma patients into high- and low-risk groups, but predicting tumor progression and clinical management of high-risk patients remains a big challenge.

Results

We integrate gene-level expression, array-based comparative genomic hybridization and functional gene-interaction network of 145 neuroblastoma patients to detect potential driver genes. The drivers are summarized into a driver-gene score (DGscore) for each patient, and we then validate its clinical relevance in terms of association with patient survival. Focusing on a subset of 48 clinically defined high-risk patients, we identify 193 recurrent regions of copy number alterations (CNAs), resulting in 274 altered genes whose copy-number gain or loss have parallel impact on the gene expression. Using a network enrichment analysis, we detect four common driver genes, ERCC6, HECTD2, KIAA1279, EMX2, and 66 patient-specific driver genes. Patients with high DGscore, thus carrying more copy-number-altered genes with correspondingly up- or down-regulated expression and functional implications, have worse survival than those with low DGscore (P = 0.006). Furthermore, Cox proportional-hazards regression analysis shows that, adjusted for age, tumor stage and MYCN amplification, DGscore is the only significant prognostic factor for high-risk neuroblastoma patients (P = 0.008).

Conclusions

Integration of genomic copy number alteration, expression and functional interaction-network data reveals clinically relevant and prognostic putative driver genes in high-risk neuroblastoma patients. The identified putative drivers are potential drug targets for individualized therapy.

Reviewers

This article was reviewed by Armand Valsesia, Susmita Datta and Aleksandra Gruca.

Electronic supplementary material

The online version of this article (10.1186/s13062-018-0218-5) contains supplementary material, which is available to authorized users.

Family specific genetic predisposition to breast cancer: results from Tunisian whole exome sequenced breast cancer cases

BACKGROUND

A family history of breast cancer has long been thought to indicate the presence of inherited genetic events that predispose to this disease. In North Africa, many specific epidemio-genetic characteristics have been observed in breast cancer families when compared to Western populations. Despite these specificities, the majority of breast cancer genetics studies performed in North Africa remain restricted to the investigation of the BRCA1 and BRCA2 genes. Thus, comprehensive data at a whole exome or whole genome level from local patients are lacking.

METHODS

A whole exome sequencing (WES) of seven breast cancer Tunisian families have been performed using a family-based approach. We focused our analysis on BC-TN-F001 family that included two affected members that have been sequenced using WES. Relevant variants identified in BC-TN-F001 have been confirmed using Sanger sequencing. Then, we conducted an integrative analysis by combining our results with those from other WES studies in order to figure out the genetic transmission model of the newly identified genes. Biological network construction and protein-protein interactions analyses have been performed to decipher the molecular mechanisms likely accounting for the role of these genes in breast cancer risk.

RESULTS

Sequencing, filtering strategies, and validation analysis have been achieved. For BC-TN-F001, no deleterious mutations have been identified on known breast cancer genes. However, 373 heterozygous, exonic and rare variants have been identified on other candidate genes. After applying several filters, 12 relevant high-risk variants have been selected. Our results showed that these variants seem to be inherited in a family specific model. This hypothesis has been confirmed following a thorough analysis of the reported WES studies. Enriched biological process and protein-protein interaction networks resulted in the identification of four novel breast cancer candidate genes namely MMS19, DNAH3, POLK and KATB6.

CONCLUSIONS

In this first WES application on Tunisian breast cancer patients, we highlighted the impact of next generation sequencing technologies in the identification of novel breast cancer candidate genes which may bring new insights into the biological mechanisms of breast carcinogenesis. Our findings showed that the breast cancer predisposition in non-BRCA families may be ethnic and/or family specific.

Pan-cancer analysis of somatic mutations and transcriptomes reveals common functional gene clusters shared by multiple cancer types

To discover functional gene clusters across cancers, we performed a systematic pan-cancer analysis of 33 cancer types. We identified genes that were associated with somatic mutations and were the cores of a co-expression network. We found that multiple cancer types have relatively exclusive hub genes individually; however, the hub genes cooperate with each other based on their functional relationship. When we built a protein-protein interaction network of hub genes and found nine functional gene clusters across cancer types, the gene clusters divided not only the region of the network map, but also the function of the network by their distinct roles related to the development and progression of cancer. This functional relationship between the clusters and cancers was underpinned by the high expression of module genes and enrichment of programmed cell death, and known candidate cancer genes. In addition to protein-coding hub genes, non-coding hub genes had a possible relationship with cancer. Overall, our approach of investigating cancer genes enabled finding pan-cancer hub genes and common functional gene clusters shared by multiple cancer types based on the expression status of the primary tumour and the functional relationship of genes in the biological network.

Vitamin D Receptor Signaling and Cancer

The vitamin D receptor (VDR) binds the secosteroid hormone 1,25(OH)₂D₃ with high affinity and regulates gene programs that control a serum calcium levels, as well as cell proliferation and differentiation. A significant focus has been to exploit the VDR in cancer settings. Although preclinical studies have been strongly encouraging, to date clinical trials have delivered equivocal findings that have paused the clinical translation of these compounds. However, it is entirely possible that mining of genomic data will help to refine precisely what are the key anticancer actions of vitamin D compounds and where these can be used most effectively.

Systematic profiling of alternative splicing signature reveals prognostic predictor for ovarian cancer

OBJECTIVE

The majority of genes are alternatively spliced and growing evidence suggests that alternative splicing is modified in cancer and is associated with cancer progression. Systematic analysis of alternative splicing signature in ovarian cancer is lacking and greatly needed.

METHODS

We profiled genome-wide alternative splicing events in 408 ovarian serous cystadenocarcinoma (OV) patients in TCGA. Seven types of alternative splicing events were curated and prognostic analyses were performed with predictive models and splicing network built for OV patients.

RESULTS

Among 48,049 mRNA splicing events in 10,582 genes, we detected 2,611 alternative splicing events in 2,036 genes which were significant associated with overall survival of OV patients. Exon skip events were the most powerful prognostic factors among the seven types. The area under the curve of the receiver-operator characteristic curve for prognostic predictor, which was built with top significant alternative splicing events, was 0.937 at 2,000 days of overall survival, indicating powerful efficiency in distinguishing patient outcome. Interestingly, splicing correlation network suggested obvious trends in the role of splicing factors in OV.

CONCLUSIONS

In summary, we built powerful prognostic predictors for OV patients and uncovered interesting splicing networks which could be underlying mechanisms.

Bioinformatic approaches to interrogating vitamin D receptor signaling

Bioinformatics applies unbiased approaches to develop statistically-robust insight into health and disease. At the global, or "20,000 foot" view bioinformatic analyses of vitamin D receptor (NR1I1/VDR) signaling can measure where the VDR gene or protein exerts a genome-wide significant impact on biology; VDR is significantly implicated in bone biology and immune systems, but not in cancer. With a more VDR-centric, or "2000 foot" view, bioinformatic approaches can interrogate events downstream of VDR activity. Integrative approaches can combine VDR ChIP-Seq in cell systems where significant volumes of publically available data are available. For example, VDR ChIP-Seq studies can be combined with genome-wide association studies to reveal significant associations to immune phenotypes. Similarly, VDR ChIP-Seq can be combined with data from Cancer Genome Atlas (TCGA) to infer the impact of VDR target genes in cancer progression. Therefore, bioinformatic approaches can reveal what aspects of VDR downstream networks are significantly related to disease or phenotype.

Overexpressed somatic alleles are enriched in functional elements in Breast Cancer

Asymmetric allele content in the transcriptome can be indicative of functional and selective features of the underlying genetic variants. Yet, imbalanced alleles, especially from diploid genome regions, are poorly explored in cancer. Here we systematically quantify and integrate the variant allele fraction from corresponding RNA and DNA sequence data from patients with breast cancer acquired through The Cancer Genome Atlas (TCGA). We test for correlation between allele prevalence and functionality in known cancer-implicated genes from the Cancer Gene Census (CGC). We document significant allele-preferential expression of functional variants in CGC genes and across the entire dataset. Notably, we find frequent allele-specific overexpression of variants in tumor-suppressor genes. We also report a list of over-expressed variants from non-CGC genes. Overall, our analysis presents an integrated set of features of somatic allele expression and points to the vast information content of the asymmetric alleles in the cancer transcriptome.

HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: "multihitting time," the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients' survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.

Prognostic alternative mRNA splicing signature in non-small cell lung cancer

Alternative splicing provides a major mechanism to generate protein diversity. Increasing evidence suggests a link of dysregulation of splicing associated with cancer. Genome-wide alternative splicing profiling in lung cancer remains largely unstudied. We generated alternative splicing profiles in 491 lung adenocarcinoma (LUAD) and 471 lung squamous cell carcinoma (LUSC) patients in TCGA using RNA-seq data, prognostic models and splicing networks were built by integrated bioinformatics analysis. A total of 3691 and 2403 alternative splicing events were significantly associated with patient survival in LUAD and LUSC, respectively, including EGFR, CD44, PIK3C3, RRAS2, MAPKAP1 and FGFR2. The area under the curve of the receiver-operator characteristic curve for prognostic predictor in NSCLC was 0.817 at 2000 days of overall survival which were also over 0.8 in LUAD and LUSC, separately. Interestingly, splicing correlation networks uncovered opposite roles of splicing factors in LUAD and LUSC. We created prognostic predictors based on alternative splicing events with high performances for risk stratification in NSCLC patients and uncovered interesting splicing networks in LUAD and LUSC which could be underlying mechanisms.

LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network

BACKGROUND

Cancer is a complex disease which is characterized by the accumulation of genetic alterations during the patient's lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations.

RESULTS

In this paper, in order to identify driver genes effectively, we applied a generalized additive model to mutation profiles to filter genes with long length and constructed a new gene-gene interaction network. Then we integrated the mutation data and expression data into the gene-gene interaction network. Lastly, greedy algorithm was used to prioritize candidate driver genes from the integrated data. We named the proposed method Length-Net-Driver (LNDriver).

CONCLUSIONS

Experiments on three TCGA datasets, i.e., head and neck squamous cell carcinoma, kidney renal clear cell carcinoma and thyroid carcinoma, demonstrated that the proposed method was effective. Also, it can identify not only frequently mutated drivers, but also rare candidate driver genes.

SURVIV for survival analysis of mRNA isoform variation

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity

Background

High throughput technologies have been used to profile genes in multiple different dimensions, such as genetic variation, copy number, gene and protein expression, epigenetics, metabolomics. Computational analyses often treat these different data types as independent, leading to an explosion in the number of features making studies under-powered and more importantly do not provide a comprehensive view of the gene’s state. We sought to infer gene activity by integrating different dimensions using biological knowledge of oncogenes and tumor suppressors.

Results

This paper proposes an integrative model of oncogene and tumor suppressor activity in cells which is used to identify cancer drivers and compute patient-specific gene activity scores. We have developed a Fuzzy Logic Modeling (FLM) framework to incorporate biological knowledge with multi-omics data such as somatic mutation, gene expression and copy number measurements. The advantage of using a fuzzy logic approach is to abstract meaningful biological rules from low-level numerical data. Biological knowledge is often qualitative, thus combining it with quantitative numerical measurements may leverage new biological insights about a gene’s state. We show that the oncogenic and altered tumor suppressing state of a gene can be better characterized by integrating different molecular measurements with biological knowledge than by each data type alone. We validate the gene activity score using data from the Cancer Cell Line Encyclopedia and drug sensitivity data for five compounds: BYL719 (PIK3CA inhibitor), PLX4720 (BRAF inhibitor), AZD6244 (MEK inhibitor), Erlotinib (EGFR inhibitor), and Nutlin-3 (MDM2 inhibitor). The integrative score improves prediction of drug sensitivity for the known drug targets of these compounds compared to each data type alone. The gene activity scores are also used to cluster colorectal cancer cell lines. Two subtypes of CRCs were found and potential cancer drivers and therapeutic targets for each of the subtypes were identified.

Conclusions

We propose a fuzzy logic based approach to infer gene activity in cancer by integrating numerical data with descriptive biological knowledge. We compute general patient-specific gene-level scores useful to determine the oncogenic or tumor suppressor status of cancer gene drivers and to cluster or classify patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0260-9) contains supplementary material, which is available to authorized users.