Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer

The multifaceted role of the mineralocorticoid receptor in cancers

The mineralocorticoid receptor (MR/NR3C2) is a member of the family of steroid receptors (SR) which also includes the estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR) and glucocorticoid receptor (GR). They function primarily as nuclear receptors to regulate gene expression. While the other steroid hormone receptors are known to play important roles in the pathogenesis and progression of many cancers, relatively little is understood about the role of MR in cancer biology. This review focuses on examining new insights into the potential roles and mechanisms of action of MR in cancers.

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%-98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma

The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.

Comprehensive analysis of the expression, prognosis and biological significance of FSCN family in clear cell renal cell carcinoma

Fascin (FSCN) is an actin-binding protein that serves a critical role in cell migration and invasion, contributing to tumor metastasis. However, there is little known about the function of FSCN family in kidney renal clear cell carcinoma (KIRC). The present study used the UALCAN, gene expression profiling interactive analysis, The Cancer Genome Atlas, cBioPortal, STRING and The Tumor Immune Estimation Resource databases to investigate the transcription level, genetic alteration and biological function of FSCNs in KIRC and their association with the prognosis value and immune cell infiltration in patients with KIRC. Results showed that the expression of FSCN1 and FSCN3 was markedly upregulated in patients with KIRC, while the expression of FSCN2 showed an opposite trend, which was the same as the experiments. Furthermore, the expression levels of FSCNs were associated with pathological stage, molecular subtypes and tumor grade. The expression levels of FSCNs were statistically correlated with the immune cell infiltration in KIRC. Higher expression levels of FSCN1 and FSCN3 were associated with worse overall survival (OS) and progression-free interval of patients bearing KIRC. Univariate and multivariate analysis demonstrated that FSCN2 was an independent risk factor for OS time in KIRC. Furthermore, mutations in FSCNs were significantly associated with poor OS and progression-free survival in patients with KIRC. The FSCNs were involved in pathways including focal adhesion, endocytosis, hypertrophic cardiomyopathy, regulation of actin cytoskeleton. The results indicated that FSCN2 might serve as an independent prognostic factor for OS of KIRC and that FSCN1 and FSCN3 can be used as favorable biomarkers for predicting clinical outcomes in KIRC.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.

Molecular differences in renal cell carcinoma between males and females

PURPOSE

The disparity in renal cell carcinoma (RCC) risk and treatment outcome between males and females is well documented, but the underlying molecular mechanisms remain poorly elucidated.

METHODS

We performed a narrative review synthesizing contemporary evidence on sex-specific molecular differences in healthy kidney tissue and RCC.

RESULTS

In healthy kidney tissue, gene expression differs significantly between males and females, including autosomal and sex-chromosome-linked genes. The differences are most prominent for sex-chromosome-linked genes and attributable to Escape from X chromosome-linked inactivation and Y chromosome loss. The frequency distribution of RCC histologies varies between the sexes, particularly for papillary, chromophobe, and translocation RCC. In clear-cell and papillary RCC, sex-specific gene expressions are pronounced, and some of these genes are amenable to pharmacotherapy. However, for many, the impact on tumorigenesis remains poorly understood. In clear-cell RCC, molecular subtypes and gene expression pathways have distinct sex-specific trends, which also apply to the expression of genes implicated in tumor progression.

CONCLUSION

Current evidence suggests meaningful genomic differences between male and female RCC, highlighting the need for sex-specific RCC research and personalized sex-specific treatment approaches.

Role of HIKESHI on Hyperthermia for Castration-Resistant Prostate Cancer and Application of a Novel Magnetic Nanoparticle with Carbon Nanohorn for Magnetic Hyperthermia

The prognosis of castration-resistant prostate cancer (CRPC) is technically scarce; therefore, a novel treatment for CRPC remains warranted. To this end, hyperthermia (HT) was investigated as an alternative therapy. In this study, the analysis focused on the association between CRPC and heat shock protein nuclear import factor "hikeshi (HIKESHI)", a factor of heat tolerance. Silencing the HIKESHI expression of 22Rv1 cells (human CRPC cell line) treated with siRNAs inhibited the translocation of heat shock protein 70 from the cytoplasm to the nucleus under heat shock and enhanced the effect of hyperthermia. Moreover, a novel magnetic nanoparticle was developed via binding carbon nanohorn (CNH) and iron oxide nanoparticle (IONP) with 3-aminopropylsilyl (APS). Tumor-bearing model mice implanted with 22 Rv1 cells were examined to determine the effect of magnetic HT (mHT). We locally injected CNH-APS-IONP into the tumor, which was set under an alternative magnetic field and showed that tumor growth in the treatment group was significantly suppressed compared with other groups. This study suggests that HIKESHI silencing enhances the sensitivity of 22Rv1 cells to HT, and CNH-APTES-IONP deserves consideration for mHT.

Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma

We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n = 435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n = 95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics

As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, limitations stymying clinical artificial intelligence (AI) will not be extinguished without thorough validation and interrogation of ML tools by pathologists and regulatory bodies alike. ML solutions deployed in clinical settings for applications in prostate pathology yield promising results. Recent breakthroughs in clinical artificial intelligence for genitourinary pathology demonstrate unprecedented generalizability, heralding prospects for a future in which AI-driven assistive solutions may be seen as laboratory faculty, rather than novelty.

Efficient Multitask Multiple Kernel Learning With Application to Cancer Research

Multitask multiple kernel learning (MKL) algorithms combine the capabilities of incorporating different data sources into the prediction model and using the data from one task to improve the accuracy on others. However, these methods do not necessarily produce interpretable results. Restricting the solutions to the set of interpretable solutions increases the computational burden of the learning problem significantly, leading to computationally prohibitive run times for some important biomedical applications. That is why we propose a multitask MKL formulation with a clustering of tasks and develop a highly time-efficient solution approach for it. Our solution method is based on the Benders decomposition and treating the clustering problem as finding a given number of tree structures in a graph; hence, it is called the forest formulation. We use our method to discriminate early-stage and late-stage cancers using genomic data and gene sets and compare our algorithm against two other algorithms. The two other algorithms are based on different approaches for linearization of the problem while all algorithms make use of the cutting-plane method. Our results indicate that as the number of tasks and/or the number of desired clusters increase, the forest formulation becomes increasingly favorable in terms of computational performance.

The potential value of cuprotosis (copper-induced cell death) in the therapy of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for 75% of the total incidence of renal cancer, and every year the number of morbidity and mortality increases, posing a serious threat to public health. The current main treatment methods for kidney cancer include drug-targeted therapy and immunotherapy. Although there are many treatment options for kidney cancer, they all have limitations, including drug resistance, unsatisfied long-term benefits, and adverse effects. Therefore, it is crucial to identify more effective therapeutic targets. As a newly discovered mechanism of cell death, copper-induced cell death (cuprotosis) is closely related to changes in cell metabolism, particularly in copper metabolism. Current studies have shown that the key signaling pathway of cuprotosis, the FDX1 (Ferredoxin 1)-LIAS (Lipoic Acid Synthetase) axis, plays an important role in the regulation of cellular oxidative stress, which can directly affect cell survival via inducing or promoting cancer cell death. Therefore, we speculated that this regulatory cell death mechanism might serve as a potential therapeutic target for the clinical treatment of renal cancer. To test this, we first performed a pan-cancer analysis based on cuprotosis-related genomic and transcriptomic levels to reveal the expression of cuprotosis in cancer. Next, GSVA-clustering analysis was performed with data from the Cancer Genome Atlas (TCGA) cohort, and the cohort was divided into three clusters according to the gene enrichment levels of cuprotosis marker genes. In addition, we analyzed the potential of using cuprotosis in clinical treatment from multiple perspectives, including chemotherapeutic drug susceptibility test, immune target inhibition treatment responsiveness, and histone modification. Combining the results of multi-omics analysis, we focused on the feasibility of this novel regulatory cell death mechanism in ccRCC treatment and further constructed a prognostic model. Finally, we verified our results by integrating the patient's gene expression information and radiomics information. Our study provides new insights into the development and clinical application of targeting cuprotosis pathway.

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

Use of SVM-based ensemble feature selection method for gene expression data analysis

Gene selection is one of the key steps for gene expression data analysis. An SVM-based ensemble feature selection method is proposed in this paper. Firstly, the method builds many subsets by using Monte Carlo sampling. Secondly, ranking all the features on each of the subsets and integrating them to obtain a final ranking list. Finally, the optimum feature set is determined by a backward feature elimination strategy. This method is applied to the analysis of 4 public datasets: the Leukemia, Prostate, Colorectal, and SMK_CAN, resulting 7, 10, 13, and 32 features. The AUC obtained from independent test sets are 0.9867, 0.9796, 0.9571, and 0.9575, respectively. These results indicate that the features selected by the proposed method can improve sample classification accuracy, and thus be effective for gene selection from gene expression data.

DNASE1L3 inhibits proliferation, invasion and metastasis of hepatocellular carcinoma by interacting with β-catenin to promote its ubiquitin degradation pathway

As a member of the deoxyribonuclease 1 family, DNASE1L3 plays a significant role both inside and outside the cell. However, the role of DNASE1L3 in hepatocellular carcinoma (HCC) and its molecular basis remains to be further investigated. In this study, we report that DNASE1L3 is downregulated in clinical HCC samples and evaluate the relationship between its expression and HCC clinical features. In vivo and in vitro experiments showed that DNASE1L3 negatively regulates the proliferation, invasion and metastasis of HCC cells. Mechanistic studies showed that DNASE1L3 recruits components of the cytoplasmic β‐catenin destruction complex (GSK‐3β and Axin), promotes the ubiquitination degradation of β‐catenin, and inhibits its nuclear transfer, thus, decreasing c‐Myc, P21 and P27 level. Ultimately, cell cycle and EMT signals are restrained. In general, this study provides new insight into the mechanism for HCC and suggests that DNASE1L3 can become a considerable target for HCC.

Artificial Intelligence in Kidney Cancer

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.

Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors. However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (HCC) remains unknown. The Cancer Genome Atlas (TCGA) database was analyzed using weighted gene coexpression network analysis (WGCNA) and differential analysis to discover essential inflammatory response-related genes (IFRGs). Cox regression studies, both univariate and multivariate, were employed to develop a prognostic IFRGs signature. Additionally, Gene Set Enrichment Analysis (GSEA) was used to deduce the biological function of the IFRGs signature. Finally, we estimated immune cell infiltration using a single sample GSEA (ssGSEA) and x-cell. Our results revealed that, among the major HCC IFRGs, two (DNASE1L3 and KLKB1) were employed to create a predictive IFRG signature. The IFRG signature could correctly predict overall survival (O.S) as per Kaplan-Meier time-dependent roc curves analysis. It was also linked to pathological tumor stage and T stage and might be used as a prognostic predictor in HCC. GSEA analysis concluded that the IFRG signature might influence the immune response in HCC. Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups. As a result of our findings, DNASILE may play a role in the tumor microenvironment. However, more research is necessary to confirm the role of DNASE1L3 and KLKB1.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

DNASE1L3 as a Novel Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma Based on Data Mining

Previous researches have highlighted that low-expressing deoxyribonuclease1-like 3 (DNASE1L3) may play a role as a potential prognostic biomarker in several cancers. However, the diagnosis and prognosis roles of DNASE1L3 gene in lung adenocarcinoma (LUAD) remain largely unknown. This research aimed to explore the diagnosis value, prognostic value, and potential oncogenic roles of DNASE1L3 in LUAD. We performed bioinformatics analysis on LUAD datasets downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analyzed with various online databases. We found that both the mRNA and protein levels of DNASE1L3 in patients with LUAD were noticeably lower than that in normal tissues. Low DNASE1L3 expression was significantly associated with higher pathological stages, T stages, and poor prognosis in LUAD cohorts. Multivariate analysis revealed that DNASE1L3 was an independent factor affecting overall survival (HR = 0.680, p = 0.027). Moreover, decreased DNASE1L3 showed strong diagnostic efficiency for LUAD. Results indicated that the mRNA level of DNASE1L3 was positively correlated with the infiltration of various immune cells, immune checkpoints in LUAD, especially with some m6A methylation regulators. In addition, enrichment function analysis revealed that the co-expressed genes may participate in the process of intercellular signal transduction and transmission. GSEA indicated that DNASE1L3 was positively related to G protein-coupled receptor ligand biding (NES = 1.738; P adjust = 0.044; FDR = 0.033) and G alpha (i) signaling events (NES = 1.635; P adjust = 0.044; FDR = 0.033). Our results demonstrated that decreased DNASE1L3 may serve as a novel diagnostic and prognostic biomarker associating with immune infiltrates in lung adenocarcinoma.

Transcriptome-Wide Map of N6-Methyladenosine Methylome Profiling in Human Bladder Cancer

N⁶-Methyladenosine (m⁶A) is the most widespread internal RNA modification in several species. In spite of latest advances in researching the biological roles of m⁶A, its function in the development and progression of bladder cancer remains unclear. In this study, we used MeRIPty -55-seq and RNA-seq methods to obtain a comprehensive transcriptome-wide m⁶A profiling and gene expression pattern in bladder cancer and paired normal adjacent tissues. Our findings showed that there were 2,331 hypomethylated and 3,819 hypermethylated mRNAs, 32 hypomethylated and 105 hypermethylated lncRNAs, and 15 hypomethylated and 238 hypermethylated circRNAs in bladder cancer tissues compared to adjacent normal tissues. Furthermore, m⁶A is most often harbored in the coding sequence (CDS), with some near the start and stop codons between two groups. Functional enrichment analysis revealed that differentially methylated mRNAs, lncRNAs, and circRNAs were mostly enriched in transcriptional misregulation in cancer and TNF signaling pathway. We also found that different m⁶A methylation levels of gene might regulate its expression. In summary, our results for the first time provide an m⁶A landscape of human bladder cancer, which expand the understanding of m⁶A modifications and uncover the regulation of mRNAs, lncRNAs, and circRNAs through m⁶A modification in bladder cancer.

A New Survival Model Based on Cholesterol Biosynthesis-Related Genes for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

In our study, the value of cholesterol biosynthesis is related to clinical analysis in 32 cancer forms in the GSEA database facility. We have a mutation between 25 CBRGs. In The Cancer Genome Atlas database, clear cell renal cell carcinoma (ccRCC, n = 539) was upregulated or downregulated in 22 out of 25 cases (n = 72) compared with normal kidney tissue. Then, using LASSO regression analysis, the survival model that is based on nine risk-related CBRGs (CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, SQLE, ACAT2, FDPS, and NSDHL) is established. ROC curves confirmed the good omen of the new survival mode, and the area under the curve is 0.72 (5 years) and 0.709 (10 years). High SQLE and ACAT2 expression and low NSDHL, FDPS, CYP51A1, FDFT1, HMGCS1, HMGCR, and IDI1 expression were closely related to patients with high-risk renal clear cell carcinoma. Two types of Cox regression, uni- and multivariate, were used to determine risk scores, age, staging, and grade as independent risk factors for prognosis in patients with clear cell renal cell carcinoma. The results showed the prediction model established by 9 selected CBRGs could predict the prognosis more accurately.

Deoxyribonuclease 1-like 3 may be a potential prognostic biomarker associated with immune infiltration in colon cancer

Colon adenocarcinoma (COAD) is a common cancer of the digestive system. It’s high morbidity and mortality make it one of the leading causes of cancer deaths. In this study, we studied the microenvironment of colon cancer to find new diagnostic markers and immunotherapy targets for colon cancer. Tumor purity of colon cancer samples in TCGA database were obtained by ESTIMATE algorithm. Then, we analyzed the association of Immune, Stromal, and Estimate scores with tumor prognosis and clinicopathological features. By comparing the gene expression profiles between tumor and normal samples, the high and low immune score groups, 117 intersecting differentially expressed genes (DEGs) were obtained. The function, molecular pathway, and prognostic value of these 117 DEGs pointed toward the importance of deoxyribonuclease 1-like 3 (DNASE1L3). Validation results from multiple databases showed low expression of DNASE1L3 in colon cancer. A single GSEA and correlation analysis of immune cells indicated that DNASE1L3 was closely related to immunity. The low expression of DNASE1L3 in colon cancer samples was measured with qRT-PCR. The scratch and cell proliferation experiments suggested that DNASE1L3 may affect cell migration. Therefore, we concluded that DNASE1L3 might be a biomarker associated with prognosis and immune infiltration in colon cancer.

Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer

Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

Computational resources for identification of cancer biomarkers from omics data

Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

DNASE1L3 as a Prognostic Biomarker Associated with Immune Cell Infiltration in Cancer

Objectives

Deoxyribonuclease 1 like 3 (DNASE1L3) is critically involved in apoptosis and immune response, however, its role in cancer has yet to be deciphered. We aimed to explore the prognostic value of DNASE1L3 across a series of malignancies.

Methods

Based on Oncomine database and Tumor Immune Estimation Resource (TIMER), expression profiling of DNASE1L3 was detailed in malignancies. Using PrognoScan, Kaplan-Meier Plotter, GEPIA2, and bc-GenEcMiner v4.5, prognostic value of DNASE1L3 was estimated in diverse cancers. Based on TIMER, association between DNASEL13 expression and immune infiltration was examined in various cancers. Then, mRNA level of DNASE1L3 in hepatocellular carcinoma (HCC) samples (n=22) and stomach adenocarcinoma (STAD) samples (n=17) was measured with qRT-PCR. Immunohistochemistry was performed to confirm expression of DNASE1L3 in paraffin-embedded tissues of HCC (n=9) and lung adenocarcinoma (n=20).

Results

DNASE1L3 was downregulated in multiple cancers, including breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD). A lower level of DNASE1L3 correlated with poorer prognosis in various cancers, especially in breast, liver, kidney, stomach, lung adenocarcinoma and sarcoma (SARC). Moreover, DNASE1L3 was positively related to immune cell infiltration in many cancers, including BRCA, LIHC, STAD, LUAD, and SARC. DNASE1L3 was significantly associated with CCR7/CCL19 in cancers. DNASE1L3 was downregulated in HCC and STAD tissues as demonstrated by qRT-PCR, as well as in HCC and LUAD samples, as shown by immunohistochemistry.

Conclusion

DNASE1L3 has potential to serve as a prognostic biomarker in cancer of the breast, kidney, liver, stomach, lung adenocarcinoma and sarcoma. Down-regulation of DNASE1L3 may participate in immune escape via CCR7/CCL19 axis.

CancerLivER: a database of liver cancer gene expression resources and biomarkers

Liver cancer is the fourth major lethal malignancy worldwide. To understand the development and progression of liver cancer, biomedical research generated a tremendous amount of transcriptomics and disease-specific biomarker data. However, dispersed information poses pragmatic hurdles to delineate the significant markers for the disease. Hence, a dedicated resource for liver cancer is required that integrates scattered multiple formatted datasets and information regarding disease-specific biomarkers. Liver Cancer Expression Resource (CancerLivER) is a database that maintains gene expression datasets of liver cancer along with the putative biomarkers defined for the same in the literature. It manages 115 datasets that include gene-expression profiles of 9611 samples. Each of incorporated datasets was manually curated to remove any artefact; subsequently, a standard and uniform pipeline according to the specific technique is employed for their processing. Additionally, it contains comprehensive information on 594 liver cancer biomarkers which include mainly 315 gene biomarkers or signatures and 178 protein- and 46 miRNA-based biomarkers. To explore the full potential of data on liver cancer, a web-based interactive platform was developed to perform search, browsing and analyses. Analysis tools were also integrated to explore and visualize the expression patterns of desired genes among different types of samples based on individual gene, GO ontology and pathways. Furthermore, a dataset matrix download facility was provided to facilitate the users for their extensive analysis to elucidate more robust disease-specific signatures. Eventually, CancerLivER is a comprehensive resource which is highly useful for the scientific community working in the field of liver cancer.Availability: CancerLivER can be accessed on the web at https://webs.iiitd.edu.in/raghava/cancerliver.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Integrated analysis of the functions of RNA binding proteins in clear cell renal cell carcinoma

RNA binding proteins (RBPs) dysregulation is involved in the processes of various tumors. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. In present study, we first performed consensus clustering and identified two clusters, of which cluster 2 was closely correlated with the malignancy of ccRCC. Differentially expressed RBPs between normal and tumor tissues were obtained, comprising 71 up-regulated and 44 down-regulated ones. Then, ten hub genes were selected and validated using The Human Protein Atlas database and receiver operating characteristic curves, showing good diagnostic value for cancers. Besides, we identified ten RBPs with the most useful prognostic values, and were used to construct a risk score model. The model could be used to stratify patients with different prognosis and phenotype distributions. The model showed good performance and can be used as a complementation for clinical factors to guide clinical practice in the future.

Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS > 5 years (149 patients) and those with <1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74–0.82), 0.75 (0.70–0.80), and 0.70 (0.60–0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.

FGF18 Inhibits Clear Cell Renal Cell Carcinoma Proliferation and Invasion via Regulating Epithelial-Mesenchymal Transition

Fibroblast growth factor 18 (FGF18) is a member of the FGF family and contributes to a broad range of biological events. The important role of the overexpression of FGF18 has been identified in the progression of several types of cancers. However, there is still little information on the biological role of FGF18 on clear cell renal cell carcinoma (ccRCC), which is of interest in investigating the biological functions of FGF18 in ccRCC. Our results showed that FGF18 was lowly expressed in ccRCC tissues compared to paired normal renal tissue from the TCGA database and clinical cohort of Huashan Hospital and that high expression of FGF18 correlated with a good prognosis in ccRCC patients. In addition, overexpression of FGF18 significantly inhibited the proliferation ability of ccRCC cell lines in vitro and in vivo. Gene set enrichment analysis (GSEA) identified epithelial-mesenchymal transition (EMT) involved in a high FGF18 expression group of ccRCC patients in the TCGA cohort, which was further validated with EMT related markers in FGF18 overexpressed ccRCC cell lines. Furthermore, FGF18 overexpression significantly inhibited the PI3K/Akt pathway in ccRCC cells. Taken together, this study concludes that FGF18 is of potential value as a target for ccRCC.

Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study

BACKGROUND

As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised.

OBJECTIVE

The main purpose of this study was to verify whether genomic data is sufficient to predict a patient's personal information.

METHODS

RNA expression data and matched patient personal information were collected from 9538 patients in The Cancer Genome Atlas program. Five personal information variables (age, gender, race, cancer type, and cancer stage) were recorded for each patient. Four different machine learning algorithms (support vector machine, decision tree, random forest, and artificial neural network) were used to determine whether a patient's personal information could be accurately predicted from RNA expression data. Performance measurement of the prediction models was based on the accuracy and area under the receiver operating characteristic curve. We selected five cancer types (breast carcinoma, kidney renal clear cell carcinoma, head and neck squamous cell carcinoma, low-grade glioma, and lung adenocarcinoma) with large samples sizes to verify whether predictive accuracy would differ between them. We also validated the efficacy of our four machine learning models in analyzing normal samples from 593 cancer patients.

RESULTS

In most samples, personal information with high genetic relevance, such as gender and cancer type, could be predicted from RNA expression data alone. The prediction accuracies for gender and cancer type, which were the best models, were 0.93-0.99 and 0.78-0.94, respectively. Other aspects of personal information, such as age, race, and cancer stage, were difficult to predict from RNA expression data, with accuracies ranging from 0.0026-0.29, 0.76-0.96, and 0.45-0.79, respectively. Among the tested machine learning methods, the highest predictive accuracy was obtained using the support vector machine algorithm (mean accuracy 0.77), while the lowest accuracy was obtained using the random forest method (mean accuracy 0.65). Gender and race were predicted more accurately than other variables in the samples. On average, the accuracy of cancer stage prediction ranged between 0.71-0.67, while the age prediction accuracy ranged between 0.18-0.23 for the five cancer types.

CONCLUSIONS

We attempted to predict patient information using RNA expression data. We found that some identifiers could be predicted, but most others could not. This study showed that personal information available from RNA expression data is limited and this information cannot be used to identify specific patients.

An improved clear cell renal cell carcinoma stage prediction model based on gene sets

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment.

RESULTS

In this study, we developed new strategies to extract important gene features and trained machine learning-based classifiers to predict stages of ccRCC samples. The novelty of our approach is that (i) We improved the feature preprocessing procedure by binning and coding, and increased the stability of data and robustness of the classification model. (ii) We proposed a joint gene selection algorithm by combining the Fast-Correlation-Based Filter (FCBF) search with the information value, the linear correlation coefficient, and variance inflation factor, and removed irrelevant/redundant features. Then the logistic regression-based feature selection method was used to determine influencing factors. (iii) Classification models were developed using machine learning algorithms. This method is evaluated on RNA expression value of clear cell renal cell carcinoma derived from The Cancer Genome Atlas (TCGA). The results showed that the result on the testing set (accuracy of 81.15% and AUC 0.86) outperformed state-of-the-art models (accuracy of 72.64% and AUC 0.81) and a gene set FJL-set was developed, which contained 23 genes, far less than 64. Furthermore, a gene function analysis was used to explore molecular mechanisms that might affect cancer development.

CONCLUSIONS

The results suggested that our model can extract more prognostic information, and is worthy of further investigation and validation in order to understand the progression mechanism.

DNASE1L2, as a Carcinogenic Marker, Affects the Phenotype of Breast Cancer Cells Via Regulating Epithelial-Mesenchymal Transition Process

Purpose: The authors explore the role of DNASE1L2 in breast cancer (BC) and its affect on the cell phenotype. Methods: Breast invasive ductal carcinoma RNA-Seq data set was downloaded from The Cancer Genome Atlas database for analyzing DNASE1L2 levels. Overall survival curve was plotted by Kaplan-Meier methods. The correlations between DNASE1L2 expression and clinical characteristics were analyzed by chi-square tests. Cox regression models were implemented for analyzing the potential prognosticators of BC. Small interference RNA-DNASE1L2 and pcDNA3.1-DNASE1L2 were transfected into BC cells to silence and overexpress DNASE1L2, respectively. Relative mRNA and protein levels were determined by quantitative real-time PCR (qRT-PCR) and Western blot, respectively. Cell counting Kit-8, clone formation, and Transwell assays were employed to measure the proliferative, invasive, and migratory abilities. Results: Bioinformatics analysis showed that the levels of DNASE1L2 were found to be elevated in BC tissues, which was further proved by qRT-PCR tests. Besides, high expression of DNASE1L2 was dramatically led to a poor overall survival. Furthermore, DNASE1L2 expression was remarkably associated with age and pathologic-stage. Silencing DNASE1L2 showed an inhibitory effect on the proliferation, invasion, and migration of MCF7 cells, whereas overexpression of DNASE1L2 in BT549 cells presented the opposite results. Mechanistically, downregulation of DNASE1L2 could significantly enhance the levels of E-cadherin, as well as suppress the levels of Vimentin, N-cadherin and Snail, whereas upregulation of DNASE1L2 showed the reverse outcomes. Conclusion: This study for the first time demonstrated that DNASE1L2 was upregulated in BC cells, and acted as an oncogene to affect the phenotype of BC cells by modulating the epithelial-mesenchymal transition process, which suggested that DNASE1L2 might be considered as a useful biomarker for BC therapeutics.

HIKESHI silencing can enhance mild hyperthermia sensitivity in human oral squamous cell carcinoma HSC‑3 cells

Hyperthermia (HT) is considered to be of value as a treatment modality in various cancers. However, the acquisition of thermotolerance in cancer cells due to the induction of heat shock proteins (HSPs) makes HT less effective. Recent findings have indicated that heat shock protein nuclear import factor hikeshi (HIKESHI), also referred to as C11orf73, acts as a nuclear import carrier of Hsp70 under heat stress conditions. The aim of the present study was to determine whether knockdown (KD) of HIKESHI by small interfering RNA (siRNA) can potentiate mild HT (MHT) sensitivity in human oral squamous cell carcinoma (OSCC) HSC‑3 cells. The mRNA and protein expression of HIKESHI was found to be markedly suppressed in HSC‑3 cells treated with siRNA for HIKESHI (siHIKE). Silencing HIKESHI significantly decreased the cell viability under MHT conditions (42˚C for 90 min). Immunocytochemical and western blot analyses clearly demonstrated that Hsp70 protein translocated from the cytoplasm to the nucleus under MHT conditions, and this translocation was significantly inhibited in cells treated with siHIKE. Treatment of the cells with MHT transiently increased the phosphorylation level of extracellular signal‑regulated kinase (ERK)2. Furthermore, the phosphorylation was sustained in HIKESHI‑KD cells under MHT conditions, and this sustained phosphorylation was abolished by pretreatment with U0126, an inhibitor of mitogen‑activated protein kinase/ERK. In addition, U0126 significantly decreased the viability of cells treated with the combination of HIKESHI‑KD and MHT. The data of the present study suggest that HIKESHI silencing enhanced the sensitivity of human OSCC HSC‑3 cells to MHT.

Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma

INTRODUCTION

Recently, the rise in the incidences of thyroid cancer worldwide renders it to be the sixth most common cancer among women. Commonly, Fine Needle Aspiration biopsy predominantly facilitates the diagnosis of the nature of thyroid nodules. However, it is inconsiderable in determining the tumor's state, i.e., benign or malignant. This study aims to identify the key RNA transcripts that can segregate the early and late-stage samples of Thyroid Carcinoma (THCA) using RNA expression profiles.

MATERIALS AND METHODS

In this study, we used the THCA RNA-Seq dataset of The Cancer Genome Atlas, consisting of 500 cancer and 58 normal (adjacent non-tumorous) samples obtained from the Genomics Data Commons (GDC) data portal. This dataset was dissected to identify key RNA expression features using various feature selection techniques. Subsequently, samples were classified based on selected features employing different machine learning algorithms.

RESULTS

Single gene ranking based on the Area Under the Receiver Operating Characteristics (AUROC) curve identified the DCN transcript that can classify the early-stage samples from late-stage samples with 0.66 AUROC. To further improve the performance, we identified a panel of 36 RNA transcripts that achieved F1 score of 0.75 with 0.73 AUROC (95% CI: 0.62-0.84) on the validation dataset. Moreover, prediction models based on 18-features from this panel correctly predicted 75% of the samples of the external validation dataset. In addition, the multiclass model classified normal, early, and late-stage samples with AUROC of 0.95 (95% CI: 0.84-1), 0.76 (95% CI: 0.66-0.85) and 0.72 (95% CI: 0.61-0.83) on the validation dataset. Besides, a five protein-coding transcripts panel was also recognized, which segregated cancer and normal samples in the validation dataset with F1 score of 0.97 and 0.99 AUROC (95% CI: 0.91-1).

CONCLUSION

We identified 36 important RNA transcripts whose expression segregated early and late-stage samples with reasonable accuracy. The models and dataset used in this study are available from the webserver CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).

Heterogeneous multiple kernel learning for breast cancer outcome evaluation

Background

Breast cancer is one of the common kinds of cancer among women, and it ranks second among all cancers in terms of incidence, after lung cancer. Therefore, it is of great necessity to study the detection methods of breast cancer. Recent research has focused on using gene expression data to predict outcomes, and kernel methods have received a lot of attention regarding the cancer outcome evaluation. However, selecting the appropriate kernels and their parameters still needs further investigation.

Results

We utilized heterogeneous kernels from a specific kernel set including the Hadamard, RBF and linear kernels. The mixed coefficients of the heterogeneous kernel were computed by solving the standard convex quadratic programming problem of the quadratic constraints. The algorithm is named the heterogeneous multiple kernel learning (HMKL). Using the particle swarm optimization (PSO) in HMKL, we selected the kernel parameters, then we employed HMKL to perform the breast cancer outcome evaluation. By testing real-world microarray datasets, the HMKL method outperforms the methods of the random forest, decision tree, GA with Rotation Forest, BFA + RF, SVM and MKL.

Conclusions

On one hand, HMKL is effective for the breast cancer evaluation and can be utilized by physicians to better understand the patient’s condition. On the other hand, HMKL can choose the function and parameters of the kernel. At the same time, this study proves that the Hadamard kernel is effective in HMKL. We hope that HMKL could be applied as a new method to more actual problems.

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics

Human leukocyte antigen (HLA) are essential components of the immune system that stimulate immune cells to provide protection and defense against cancer. Thousands of HLA alleles have been reported in the literature, but only a specific set of HLA alleles are present in an individual. The capability of the immune system to recognize cancer-associated mutations depends on the presence of a particular set of alleles, which elicit an immune response to fight against cancer. Therefore, the occurrence of specific HLA alleles affects the survival outcome of cancer patients. In the current study, prediction models were developed, using 401 cutaneous melanoma patients, to predict the overall survival (OS) of patients using their clinical data and HLA alleles. We observed that the presence of certain favorable superalleles like HLA-B∗55 (HR = 0.15, 95% CI 0.034-0.67), HLA-A∗01 (HR = 0.5, 95% CI 0.3-0.8), is responsible for the improved OS. In contrast, the presence of certain unfavorable superalleles such as HLA-B∗50 (HR = 2.76, 95% CI 1.284-5.941), HLA-DRB1∗12 (HR = 3.44, 95% CI 1.64-7.2) is responsible for the poor survival. We developed prediction models using key 14 HLA superalleles, demographic, and clinical characteristics for predicting high-risk cutaneous melanoma patients and achieved HR = 4.52 (95% CI 3.088-6.609, p-value = 8.01E-15). Eventually, we also provide a web-based service to the community for predicting the risk status in cutaneous melanoma patients (https://webs.iiitd.edu.in/raghava/skcmhrp/).

A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers

Motivation

Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest. Given that there are similarities in the underlying mechanisms of different cancers, in addition to the considerable correlation in the genomic data, there is a need for machine learning methods that can take these aspects of genomic data into account. Furthermore, using machine learning for studying multiple cancer cohorts together with a collection of molecular pathways creates an opportunity for knowledge extraction.

Results

We studied the problem of discriminating early- and late-stage tumors of several cancers using genomic information while enforcing interpretability on the solutions. To this end, we developed a multitask multiple kernel learning (MTMKL) method with a co-clustering step based on a cutting-plane algorithm to identify the relationships between the input tasks and kernels. We tested our algorithm on 15 cancer cohorts and observed that, in most cases, MTMKL outperforms other algorithms (including random forests, support vector machine and single-task multiple kernel learning) in terms of predictive power. Using the aggregate results from multiple replications, we also derived similarity matrices between cancer cohorts, which are, in many cases, in agreement with available relationships reported in the relevant literature.

Availability and implementation

Our implementations of support vector machine and multiple kernel learning algorithms in R are available at https://github.com/arezourahimi/mtgsbc together with the scripts that replicate the reported experiments.

Supplementary information

Supplementary data are available at Bioinformatics online.

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying IDC progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

DNASE1L3 as an indicator of favorable survival in hepatocellular carcinoma patients following resection

Hepatocellular carcinoma (HCC) is a common malignancy with a dismal prognosis. It is of great importance to identify biomarkers for the prediction of patients’ survival.

The mRNA expression level of deoxyribonuclease 1 like 3 (DNASE1L3) and its correlation with survival were accessed in 424 samples from The Cancer Genome Atlas database. Its expression level was confirmed by real-time quantitative polymerase chain reaction and western blotting in 20 pairs of postsurgical specimens. In addition, immunohistochemistry staining of DNASE1L3 was also performed in 113 postoperative samples, using a histochemistry score system. The relationship between patients’ survival and DNASE1L3 expression level was evaluated by the Kaplan-Meier method.

DNASE1L3 is downregulated in both mRNA and protein levels in HCC tissues, compared with adjacent normal tissues. 52 of 113 HCC specimens showed positive DNASE1L3 protein expression. Patients with positive DNASE1L3 expression had significantly longer overall survival, compared with patients with negative expression (p = 0.023). However, the DNASE1L3 fails to discriminate progression-free survival (p = 0.134). Multivariate COX analysis revealed that positive DNASE1L3 expression and higher differentiation were significantly associated with better overall survival.

This study demonstrated that positive DNASE1L3 expression is an independent prognostic factor for better survival in HCC patients following radical resection.

Novel genes exhibiting DNA methylation alterations in Korean patients with chronic lymphocytic leukaemia: a methyl-CpG-binding domain sequencing study

Chronic lymphocytic leukaemia (CLL) exhibits differences between Asians and Caucasians in terms of incidence rate, age at onset, immunophenotype, and genetic profile. We performed genome-wide methylation profiling of CLL in an Asian cohort for the first time. Eight Korean patients without somatic immunoglobulin heavy chain gene hypermutations underwent methyl-CpG-binding domain sequencing (MBD-seq), as did five control subjects. Gene Ontology, pathway analysis, and network-based prioritization of differentially methylated genes were also performed. More regions were hypomethylated (2,062 windows) than were hypermethylated (777 windows). Promoters contained the highest proportion of differentially methylated regions (0.08%), while distal intergenic and intron regions contained the largest number of differentially methylated regions. Protein-coding genes were the most abundant, followed by long noncoding and short noncoding genes. The most significantly over-represented signalling pathways in the differentially methylated gene list included immune/cancer-related pathways and B-cell receptor signalling. Among the top 10 hub genes identified via network-based prioritization, four (UBC, GRB2, CREBBP, and GAB2) had no known relevance to CLL, while the other six (STAT3, PTPN6, SYK, STAT5B, XPO1, and ABL1) have previously been linked to CLL in Caucasians. As such, our analysis identified four novel candidate genes of potential significance to Asian patients with CLL.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91–0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients

The metastatic Skin Cutaneous Melanoma (SKCM) has been associated with diminished survival rates and high mortality rates worldwide. Thus, segregating metastatic melanoma from the primary tumors is crucial to employ an optimal therapeutic strategy for the prolonged survival of patients. The SKCM mRNA, miRNA and methylation data of TCGA is comprehensively analysed to recognize key genomic features that can segregate metastatic and primary tumors. Further, machine learning models have been developed using selected features to distinguish the same. The Support Vector Classification with Weight (SVC-W) model developed using the expression of 17 mRNAs achieved Area under the Receiver Operating Characteristic (AUROC) curve of 0.95 and an accuracy of 89.47% on an independent validation dataset. This study reveals the genes C7, MMP3, KRT14, LOC642587, CASP7, S100A7 and miRNAs hsa-mir-205 and hsa-mir-203b as the key genomic features that may substantially contribute to the oncogenesis of melanoma. Our study also proposes genes ESM1, NFATC3, C7orf4, CDK14, ZNF827, and ZSWIM7 as novel putative markers for cutaneous melanoma metastasis. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP ( http://webs.iiitd.edu.in/raghava/cancerspp/ ).

Integrative analysis reveals CRHBP inhibits renal cell carcinoma progression by regulating inflammation and apoptosis

Patients with renal cell carcinoma (RCC) usually develop drug resistance and have poor prognosis owing to its insensitive property. However, the underlying mechanisms of RCC are still unclear. We implemented an integrative analysis of The Cancer Genome Atlas and Gene Expression Omnibus datasets. Three genes (CRHBP, RAB25 and PSAT1) were found to be potential biomarkers in ccRCC and validated by four independent cohorts. Then, ccRCC patients with a decreased expression of CRHBP in tumor tissues had significantly poor survival by TCGA ccRCC datasets and verified by clinical samples as well as RCC cell lines. Overexpression of CRHBP suppressed cell proliferation, migration, invasion as well as apoptosis in vitro and in vivo. Moreover, the results of western blot analysis showed the effects of CRHBP via upregulating NF-κB and p53-mediated mitochondria apoptotic pathway. Our results suggested that CRHBP may be an effective target to treat ccRCC patients.

Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles

Background

Liver Hepatocellular Carcinoma (LIHC) is one of the major cancers worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting the stage of LIHC from the genomic profile of samples.

Methods

The Cancer Genome Atlas (TCGA) dataset of 173 early stage (stage-I), 177 late stage (stage-II, Stage-III and stage-IV) and 50 adjacent normal tissue samples for 60,483 RNA transcripts and 485,577 methylation CpG sites, was extensively analyzed to identify the key transcriptomic expression and methylation-based features using different feature selection techniques. Further, different classification models were developed based on selected key features to categorize different classes of samples implementing different machine learning algorithms.

Results

In the current study, in silico models have been developed for classifying LIHC patients in the early vs. late stage and cancerous vs. normal samples using RNA expression and DNA methylation data. TCGA datasets were extensively analyzed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early vs. late stages and cancer vs. normal samples of LIHC with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC (Matthew’s correlation coefficient) 0.58 with an accuracy of 78.87% on the validation dataset in discrimination of early and late stage. Additionally, the prediction models developed based on 5 RNA transcripts and 5 CpG sites classify LIHC and normal samples with an accuracy of 96–98% and AUC (Area Under the Receiver Operating Characteristic curve) 0.99. Besides, multiclass models also developed for classifying samples in the normal, early and late stage of cancer and achieved an accuracy of 76.54% and AUC of 0.86.

Conclusion

Our study reveals stage prediction of LIHC samples with high accuracy based on the genomics and epigenomics profiling is a challenging task in comparison to the classification of cancerous and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP (http://webs.iiitd.edu.in/raghava/cancerlsp/).

Discriminating early- and late-stage cancers using multiple kernel learning on gene sets

Motivation

Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early- and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data. That is why we need algorithms that can also extract relevant information about these biological mechanisms using our prior knowledge about pathways/gene sets.

Results

In this study, we addressed the problem of separating early- and late-stage cancers from each other using their gene expression profiles. We proposed to use a multiple kernel learning (MKL) formulation that makes use of pathways/gene sets (i) to obtain satisfactory/improved predictive performance and (ii) to identify biological mechanisms that might have an effect in cancer progression. We extensively compared our proposed MKL on gene sets algorithm against two standard machine learning algorithms, namely, random forests and support vector machines, on 20 diseases from the Cancer Genome Atlas cohorts for two different sets of experiments. Our method obtained statistically significantly better or comparable predictive performance on most of the datasets using significantly fewer gene expression features. We also showed that our algorithm was able to extract meaningful and disease-specific information that gives clues about the progression mechanism.

Availability and implementation

Our implementations of support vector machine and multiple kernel learning algorithms in R are available at https://github.com/mehmetgonen/gsbc together with the scripts that replicate the reported experiments.

Development and internal validation of a novel model and markers to identify the candidates for lymph node metastasis in patients with prostate cancer

BACKGROUND

High-grade prostate cancer (PCa) has a poor prognosis, and up to 15% of patients worldwide experience lymph node invasion (LNI). To further improve the prediction lymph node invasion in prostate cancer, we adopted risk scores of the genes expression based on the nomogram in guidelines.

METHODS

We analyzed clinical data from 320 PCa patients from the Cancer Genome Atlas database. Weighted gene coexpression network analysis was used to identify the genes that were significantly associated with LNI in PCa (n = 390). Analyses using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were performed to identify the activated signaling pathways. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for the presence of LNI.

RESULTS

We found that patients with actual LNI and predicted LNI had the worst survival outcomes. The 7 most significant genes (CTNNAL1, ENSA, MAP6D1, MBD4, PRCC, SF3B2, TREML1) were selected for further analysis. Pathways in the cell cycle, DNA replication, oocyte meiosis, and 9 other pathways were dramatically activated during LNI in PCa. Multivariate analyses identified that the risk score (odds ratio [OR] = 1.05 for 1% increase, 95% confidence interval [CI]: 1.04-1.07, P < .001), serum PSA level, clinical stage, primary biopsy Gleason grade (OR = 2.52 for a grade increase, 95% CI: 1.27-5.22, P = .096), and secondary biopsy Gleason grade were independent predictors of LNI. A nomogram built using these predictive variables showed good calibration and a net clinical benefit, with an area under the curve (AUC) value of 90.2%.

CONCLUSIONS

In clinical practice, the application of our nomogram might contribute significantly to the selection of patients who are good candidates for surgery with extended pelvic lymph node dissection.

Gender-specific risk factors for renal cell carcinoma: a systematic review

PURPOSE OF REVIEW

The relationship between gender and kidney cancer incidence/outcomes has been largely evaluated and may significantly impact the management of patients diagnosed with these tumors. We reviewed and summarized the most relevant recent publications reporting about this clinically meaningful relationship.

RECENT FINDINGS

The incidence of kidney cancer is higher in men than in women. Male gender is clearly associated with more aggressive histological characteristics both in terms of tumor stage and grade. Similarly, male gender has been found to be associated with worse perioperative and oncological outcomes. Several genetic and molecular markers that may partly explain these observed differences have been evaluated. However, the impact of these markers on clinical practice has not been clearly demonstrated.

SUMMARY

Gender is significantly associated with kidney cancer incidence, characteristics and outcomes. Future efforts are still needed to explore the biological and molecular basis underlying of this relationship.