Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold

The pathogenesis of carcinoma is believed to come from the combined effect of polygenic variation, and the initiation and progression of malignant tumors are closely related to the dysregulation of biological pathways. Quantifying the alteration in pathway activation and identifying coordinated patterns of pathway dysfunction are the imperative part of understanding the malignancy process and distinguishing different tumor stages or clinical outcomes of individual patients. In this study, we have conducted in silico pathway activation analysis using Riemannian manifold (RiePath) toward pan-cancer personalized characterization, which is the first attempt to apply the Riemannian manifold theory to measure the extent of pathway dysregulation in individual patient on the tangent space of the Riemannian manifold. RiePath effectively integrates pathway and gene expression information, not only generating a relatively low-dimensional and biologically relevant representation, but also identifying a robust panel of biologically meaningful pathway signatures as biomarkers. The pan-cancer analysis across 16 cancer types reveals the capability of RiePath to evaluate pathway activation accurately and identify clinical outcome-related pathways. We believe that RiePath has the potential to provide new prospects in understanding the molecular mechanisms of complex diseases and may find broader applications in predicting biomarkers for other intricate diseases.

Racial disparities in metastatic colorectal cancer outcomes revealed by tumor microbiome and transcriptome analysis with bevacizumab treatment

Background: Metastatic colorectal cancer (mCRC) is a heterogeneous disease, often associated with poor outcomes and resistance to therapies. The racial variations in the molecular and microbiological profiles of mCRC patients, however, remain under-explored. Methods: Using RNA-SEQ data, we extracted and analyzed actively transcribing microbiota within the tumor milieu, ensuring that the identified bacteria were not merely transient inhabitants but engaged in the tumor ecosystem. Also, we independently acquired samples from 12 mCRC patients, specifically, 6 White individuals and 6 of Black or African American descent. These samples underwent 16S rRNA sequencing. Results: Our study revealed notable racial disparities in the molecular signatures and microbiota profiles of mCRC patients. The intersection of these data showcased the potential modulating effects of specific bacteria on gene expression. Particularly, the bacteria Helicobacter cinaedi and Sphingobium herbicidovorans emerged as significant influencers, with strong correlations to the genes SELENBP1 and SNORA38, respectively. Discussion: These findings underscore the intricate interplay between host genomics and actively transcribing tumor microbiota in mCRC's pathogenesis. The identified correlations between specific bacteria and genes highlight potential avenues for targeted therapies and a more personalized therapeutic approach.

A large-scale cancer-specific protein-DNA interaction network

Cancer development and progression are generally associated with dysregulation of gene expression, often resulting from changes in transcription factor (TF) sequence or expression. Identifying key TFs involved in cancer gene regulation provides a framework for potential new therapeutics. This study presents a large-scale cancer gene TF-DNA interaction network as well as an extensive promoter clone resource for future studies. Most highly connected TFs do not show a preference for binding to promoters of genes associated with either good or poor cancer prognosis, suggesting that emerging strategies aimed at shifting gene expression balance between these two prognostic groups may be inherently complex. However, we identified potential for oncogene targeted therapeutics, with half of the tested oncogenes being potentially repressed by influencing specific activator or bifunctional TFs. Finally, we investigate the role of intrinsically disordered regions within the key cancer-related TF estrogen receptor ɑ (ESR1) on DNA binding and transcriptional activity, and found that these regions can have complex trade-offs in TF function. Altogether, our study not only broadens our knowledge of TFs involved in the cancer gene regulatory network but also provides a valuable resource for future studies, laying a foundation for potential therapeutic strategies targeting TFs in cancer.

Reconstructing the immunosenescence core pathway reveals global characteristics in pan-cancer

Immunosenescence has been demonstrated to play an important role in tumor progression. However, there is lacking comprehensive analyses of immunosenescence-related pathways. Meanwhile, the sex disparities of immunosenescence in cancer are still poorly understood. In this study, we analyzed the multi-omics data of 12,836 tumor samples, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics. We systematically identified immunosenescence pathways that were disordered across cancer types. The mutations and copy number variations of immunosenescence pathways were found to be more active in pan-cancer. We reconstructed the immunosenescence core pathways (ISC-pathways) to improve the ability of prognostic stratification in 33 cancer types. We also found the head and neck squamous carcinoma (HNSC) contained abundant sex-specific immunosenescence features and showed sex differences in survival. We found that OSI-027 was a potential sex-specific drug in HNSC tumors, which tended to be more effective in male HNSC by targeting the MTOR gene in the PI3K-Akt signaling pathway. In conclusion, our study provided a systematic understanding of immunosenescence pathways and revealed the global characteristics of immunosenescence in pan-cancer. We highlighted MTOR gene could be a powerful immunosenescence biomarker of HNSC that helps to develop sex-specific immunosenescence drugs.

PFKP is a prospective prognostic, diagnostic, immunological and drug sensitivity predictor across pan-cancer

Phosphofructokinase, platelet (PFKP) is a rate-limiting enzyme of glycolysis that plays a decisive role in various human physio-pathological processes. PFKP has been reported to have multiple functions in different cancer types, including lung cancer and breast cancer. However, no systematic pancancer analysis of PFKP has been performed; this type of analysis could elucidate the clinical value of PFKP in terms of diagnosis, prognosis, drug sensitivity, and immunological correlation. Systematic bioinformation analysis of PFKP was performed based on several public datasets, including The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression Project (GTEx), and Human Protein Atlas (HPA). Prospective carcinogenesis of PFKP across cancers was estimated by expression analysis, effect on patient prognosis, diagnosis significance evaluation, and immunity regulation estimation. Then, pancancer functional enrichment of PFKP was also assessed through its effect on the signaling score and gene expression profile. Finally, upstream expression regulation of PFKP was explored by promoter DNA methylation and transcription factor (TF) prediction. Our analysis revealed that high expression of PFKP was found in most cancer types. Additionally, a high level of PFKP displayed a significant correlation with poor prognosis in patients across cancers. The diagnostic value of PFKP was performed based on its positive correlation with programmed cell death-ligand 1 (PD-L1). We also found an obvious immune-regulating effect of PFKP in most cancer types. PFKP also had a strong negative correlation with several cancer drugs. Finally, ectopic expression of PFKP may depend on DNA methylation and several predicated transcription factors, including the KLF (KLF transcription factor) and Sp (Sp transcription factor) families. This pancancer analysis revealed that a high expression level of PFKP might be a useful biomarker and predictor in most cancer types. Additionally, the performance of PFKP across cancers also suggested its meaningful role in cancer immunity regulation, even in immunotherapy and drug resistance. Overall, PFKP might be explored as an auxiliary monitor for pancancer early prognosis and diagnosis.

CircRNAs regulate the crosstalk between inflammation and tumorigenesis: The bilateral association and molecular mechanisms

Inflammation, a hallmark of cancer, has been associated with tumor progression, transition into malignant phenotype and efficacy of the chemotherapeutic agents in cancer. Chronic inflammation provides a favorable environment for tumorigenesis by inducing immunosuppression, whereas acute inflammation prompts tumor suppression by generating anti-tumor immune responses. Inflammatory factors derived from interstitial cells or tumor cells can stimulate cell proliferation and survival by modulating oncogenes and/or tumor suppressors. Recently, a new class of RNAs, i.e., circular RNAs (circRNAs), has been implicated in inflammatory diseases. Although there are reports on circRNAs imparting functions in inflammatory insults, whether these circularized transcripts hold the potential to regulate inflammation-induced cancer or tumor-related inflammation, and modulate the interactions between tumor microenvironment (TME) and the inflammatory stromal/immune cells, awaits further elucidation. Contextually, the current review describes the molecular association between inflammation and cancer, and spotlights the regulatory mechanisms by which circRNAs can moderate TME in response to inflammatory signals/triggers. We also present comprehensive information about the immune cell(s)-specific expression and functions of the circRNAs in TME, modulation of inflammatory signaling pathways to drive tumorigenesis, and their plausible roles in inflammasomes and tumor development. Moreover, the therapeutic potential of these circRNAs in harnessing inflammatory responses in cancer is also discussed.

CiTSA: a comprehensive platform provides experimentally supported signatures of cancer immunotherapy and analysis tools based on bulk and scRNA-seq data

Immunotherapy has greatly changed the status of cancer treatment, and many patients do not respond or develop acquired resistance. The related research is blocked by lacking of comprehensive resources for researchers to discovery and analysis signatures, then further exploring the mechanisms. Here, we first offered a benchmarking dataset of experimentally supported signatures of cancer immunotherapy by manually curated from published literature works and provided an overview. We then developed CiTSA ( http://bio-bigdata.hrbmu.edu.cn/CiTSA/ ) which stores 878 entries of experimentally supported associations between 412 signatures such as genes, cells, and immunotherapy across 30 cancer types. CiTSA also provides flexible online tools to identify and visualize molecular/cell feature and interaction, to perform function, correlation, and survival analysis, and to execute cell clustering, cluster activity, and cell-cell communication analysis based on single cell and bulk datasets of cancer immunotherapy. In summary, we provided an overview of experimentally supported cancer immunotherapy signatures and developed CiTSA which is a comprehensive and high-quality resource and is helpful for understanding the mechanism of cancer immunity and immunotherapy, developing novel therapeutic targets and promoting precision immunotherapy for cancer.

CRAC and SK Channels: Their Molecular Mechanisms Associated with Cancer Cell Development

Cancer represents a major health burden worldwide. Several molecular targets have been discovered alongside treatments with positive clinical outcomes. However, the reoccurrence of cancer due to therapy resistance remains the primary cause of mortality. Endeavors in pinpointing new markers as molecular targets in cancer therapy are highly desired. The significance of the co-regulation of Ca²⁺-permeating and Ca²⁺-regulated ion channels in cancer cell development, proliferation, and migration make them promising molecular targets in cancer therapy. In particular, the co-regulation of the Orai1 and SK3 channels has been well-studied in breast and colon cancer cells, where it finally leads to an invasion-metastasis cascade. Nevertheless, many questions remain unanswered, such as which key molecular components determine and regulate their interplay. To provide a solid foundation for a better understanding of this ion channel co-regulation in cancer, we first shed light on the physiological role of Ca²⁺ and how this ion is linked to carcinogenesis. Then, we highlight the structure/function relationship of Orai1 and SK3, both individually and in concert, their role in the development of different types of cancer, and aspects that are not yet known in this context.

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagnosis, guide treatment, and improve patients' prognoses. Instead of single-omics data, multi-omics data can improve the clustering performance because it obtains a comprehensive landscape for understanding biological systems and mechanisms. However, heterogeneous data from multiple sources raises high complexity and different kinds of noise, which are detrimental to the extraction of clustering information. We propose an end-to-end deep learning based method, called Multi-omics Clustering Variational Autoencoders (MCluster-VAEs), that can extract cluster-friendly representations on multi-omics data. First, a unified network architecture with an attention mechanism was developed for accurately modeling multi-omics data. Then, using a novel objective function built from the Variational Bayes technique, the model was trained to effectively obtain the posterior estimation of the clustering assignments. Compared with 12 other state-of-the-art multi-omics clustering methods, MCluster-VAEs achieved an outstanding performance on benchmark datasets from the TCGA database. On the Pan Cancer dataset, MCluster-VAEs achieved an adjusted Rand index of approximately 0.78 for cancer category recognition, an increase of more than 18% compared with other methods. Furthermore, a survival analysis and clinical parameter enrichment tests conducted on 10 cancer datasets demonstrated that MCluster-VAEs provides comparable and even better results than many common integrative approaches. These results demonstrate that MCluster-VAEs are a powerful new tool for dissecting complex multi-omics relationships and providing new insights for cancer subtype discovery.

Glycolysis-Related SLC2A1 Is a Potential Pan-Cancer Biomarker for Prognosis and Immunotherapy

SLC2A1 plays a pivotal role in cancer glycometabolism. SLC2A1 has been proposed as a putative driver gene in various cancers. However, a pan-cancer analysis of SLC2A1 has not yet been performed. In this study, we explored the expression and prognosis of SLC2A1 in pan-cancer across multiple databases. We conducted genetic alteration, epigenetic, and functional enrichment analyses of SLC2A. We calculated the correlation between SLC2A1 and tumor microenvironment using the TCGA pan-cancer dataset. We observed high expression levels of SLC2A1 with poor prognosis in most cancers. The overall genetic alteration frequency of SLC2A1 was 1.8% in pan-cancer, and the SLC2A1 promoter was hypomethylation in several cancers. Most m6A-methylation-related genes positively correlated with the expression of SLC2A1 in 33 TCGA cancers. Moreover, SLC2A1 was mainly related to the functions including epithelial-mesenchymal transition, glycolysis, hypoxia, cell-cycle regulation, and DNA repair. Finally, SLC2A1 positively associated with neutrophils and cancer-associated fibroblasts in the tumor microenvironment of most cancers and significantly correlated with TMB and MSI in various cancers. Notably, SLC2A1 was remarkably positively correlated with PD-L1 and CTLA4 in most cancers. SLC2A1 might serve as an attractive pan-cancer biomarker for providing new insights into cancer therapeutics.

Quantification of ferroptosis pathway status revealed heterogeneity in breast cancer patients with distinct immune microenvironment

Clinical significance and biological functions of the ferroptosis pathway were addressed in all aspect of cancer regarding multi-omics level; however, the overall status of ferroptosis pathway alteration was hard to evaluate. The aim of this study is to comprehensively analyze the putative biological, pathological, and clinical functions of the ferroptosis pathway in breast cancer on a pathway level. By adopting the bioinformatic algorithm “pathifier”, we quantified five programmed cell death (PCD) pathways (KO04210 Apoptosis; KO04216 Ferroptosis; KO04217 Necroptosis; GO:0070269 Pyroptosis; GO:0048102 Autophagic cell death) in breast cancer patients, and we featured the clinical characteristics and prognostic value of each pathway in breast cancer and found significantly activated PCD in cancer patients, among which ferroptosis demonstrated a significant correlation with the prognosis of breast cancer. Correlation analysis between PCD pathways identified intra-tumor heterogeneity of breast cancer. Therefore, clustering of patients based on the status of PCD pathways was done. Comparisons between subgroups highlighted specifically activated ferroptosis in cluster 2 patients, which showed the distinct status of tumor immunity and microenvironment from other clusters, indicating putative correlations with ferroptosis. NDUFA13 was identified and selected as a putative biomarker for cluster 2 patients. Experimental validations were executed on cellular level and NDUFA13 showed an important role in regulating ferroptosis activation and can work as a biomarker for ferroptosis pathway status. In conclusion, the status of the ferroptosis pathway significantly correlated with the clinical outcomes and intra-tumor heterogeneity of breast cancer, and NDUFA13 expression was identified as a positive biomarker for ferroptosis pathway activation in breast cancer patients.

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer

Dysregulation of signaling pathways plays an essential role in cancer. However, there is not a comprehensive understanding on how oncogenic signaling pathways affect the occurrence and development with a common molecular mechanism of pan-cancer. Here, we investigated the oncogenic signaling pathway dysregulation by using multi-omics data on patients from TCGA from a pan-cancer perspective to identify commonalities across different cancer types. First, the pathway dysregulation profile was constructed by integrating typical oncogenic signaling pathways and the gene expression of TCGA samples, and four molecular subtypes with significant phenotypic and clinical differences induced by different oncogenic signaling pathways were identified: TGF-β+ subtype; cell cycle, MYC, and NF2− subtype; cell cycle and TP53+ subtype; and TGF-β and TP53− subtype. Patients in the TGF-β+ subtype have the best prognosis; meanwhile, the TGF-β+ subtype is associated with hypomethylation. Moreover, there is a higher level of immune cell infiltration but a slightly worse survival prognosis in the cell cycle, MYC, and NF2− subtype patients due to the effect of T-cell dysfunction. Then, the prognosis and subtype classifiers constructed by differential genes on a multi-omics level show great performance, indicating that these genes can be considered as biomarkers with potential therapeutic and prognostic significance for cancers. In summary, our study identified four oncogenic signaling pathway–driven patterns presented as molecular subtypes and their related potential prognostic biomarkers by integrating multiple omics data. Our discovery provides a perspective for understanding the role of oncogenic signaling pathways in pan-cancer.

Application of Biocompatible Drug Delivery Nanosystems for the Treatment of Naturally Occurring Cancer in Dogs

Background: Cancer is a common disease in dogs, with a growing incidence related to the age of the animal. Nanotechnology is being employed in the veterinary field in the same manner as in human therapy. Aim: This review focuses on the application of biocompatible nanocarriers for the treatment of canine cancer, paying attention to the experimental studies performed on dogs with spontaneously occurring cancer. Methods: The most important experimental investigations based on the use of lipid and non-lipid nanosystems proposed for the treatment of canine cancer, such as liposomes and polymeric nanoparticles containing doxorubicin, paclitaxel and cisplatin, are described and their in vivo fate and antitumor features discussed. Conclusions: Dogs affected by spontaneous cancers are useful models for evaluating the efficacy of drug delivery systems containing antitumor compounds.

Hsa_Circ_0066351 Acts as a Prognostic and Immunotherapeutic Biomarker in Colorectal Cancer

Circular RNA (circRNA), a novel class of non-coding RNA, has been reported in various diseases, especially in tumors. However, the key signatures of circRNA-competitive endogenous RNA (ceRNA) network are largely unclear in colorectal cancer (CRC). We first characterized circRNAs profile by using circRNA-seq analysis from real-word dataset. The expression level of hsa_circ_0066351 in CRC tissues and cell lines was detected by quantitative real-time PCR. Then, cell proliferation assay was used to confirm the proliferation function of hsa_circ_0066351. Next, Cytoscape was used to construct circRNA–miRNA–mRNA networks. Last but not least, the landscape of hsa_circ_0066351–miRNA–mRNA in CRC had been investigated in the bulk tissue RNA-Seq level and single-cell Seq level. We proved that hsa_circ_0066351 was significantly downregulated in CRC cell lines and tissues (P < 0.001), and was negatively associated with distant metastasis (P < 0.01). Significantly, the expression of hsa_circ_0066351 was associated with better survival in patients with CRC. Function assays showed that hsa_circ_0066351 could inhibit CRC cells proliferation. In addition, a ceRNA network, including hsa_circ_0066351, two miRNAs, and ten mRNAs, was constructed. Our analyses showed that these ten mRNAs were consistently downregulated in pan-cancer and enriched in tumor suppressive function. A risk score model constructed by these ten downstream genes also indicated that they were related to the prognosis and immune response in CRC. In conclusion, we demonstrated that a novel circRNA (hsa_circ_0066351) inhibited CRC proliferation, and revealed a potential prognostic and immunotherapeutic biomarker in CRC.

Individualized pathway activity algorithm identifies oncogenic pathways in pan-cancer analysis

Background

Accumulative evidences have shown that dysregulation of biological pathways contributed to the initiation and progression of malignant tumours. Several methods for pathway activity measurement have been proposed, but they are restricted to making comparisons between groups or sensitive to experimental batch effects.

Methods

We introduced a novel method for individualized pathway activity measurement (IPAM) that is based on the ranking of gene expression levels in individual sample. Taking advantage of IPAM, we calculated the pathway activity of 318 pathways from KEGG database in the 10528 tumour/normal samples of 33 cancer types from TCGA to identify characteristic dysregulated pathways among different cancer types.

Findings

IPAM precisely quantified the level of activity of each pathway in pan-cancer analysis and exhibited better performance in cancer classification and prognosis prediction over five widely used tools. The average ROC-AUC of cancer diagnostic model using tumour-educated platelets (TEPs) reached 92.84%, suggesting the potential of our algorithm in early diagnosis of cancer. We identified several pathways significantly deregulated and associated with patient survival in a large fraction of cancer types, such as tyrosine metabolism, fatty acid degradation, cell cycle, p53 signalling pathway and DNA replication. We also confirmed the dominant role of metabolic pathways in cancer pathway dysregulation and identified the driving factors of specific pathway dysregulation, such as PPARA for branched-chain amino acid metabolism and NR1I2, NR1I3 for fatty acid metabolism.

Interpretation

Our study will provide novel clues for understanding the pathological mechanisms of cancer, ultimately paving the way for personalized medicine of cancer.

Funding

A full list of funding can be found in the Acknowledgements section.

Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data

Cancer is a highly heterogeneous disease with different functional disorders among individuals. The initiation and progression of cancer is usually related to dysregulation of local regions within pathways. Identification of individualized risk pathways is crucial for revealing the mechanisms of tumorigenesis and heterogeneity. However, approach that focused on mining patient-specific risk subpathway regions is still lacking. Here, we developed an individualized cancer risk subpathway identification method that was referred as InCRiS by integrating multi-omics data. Then, the method was applied to nearly 3000 samples across 9 TCGA cancer types and its robustness and reliability were evaluated. Dissecting dysregulated subpathways in these tumor samples revealed several key regions which may play oncogenic roles in cancer. The construction of risk subpathway dysregulation profile of pan-cancers revealed 11 pan-cancer molecular classification (InCRiS subtypes) with significantly different clinical outcomes. Moreover, subpathway regions that tend to be disordered in individuals of specific subtypes were examined for understanding the pathogenesis of tumor and some key genes such as CTNNB1, EP300 and PRKDC were nominated in different subtypes. In summary, the proposed method and resulting data presented useful resources to study the mechanism of tumor heterogeneity and to discovery novel therapeutic targets for precise treatment of cancer.

NetAUC: A network-based multi-biomarker identification method by AUC optimization

Complex diseases are caused by a variety of factors, and their diagnosis, treatment and prognosis are usually difficult. Proteins play an indispensable role in living organisms and perform specific biological functions by interacting with other proteins or biomolecules, their dysfunction may lead to diseases, it is a natural way to mine disease-related biomarkers from protein-protein interaction network. AUC, the area under the receiver operating characteristics (ROC) curve, is regarded as a gold standard to evaluate the effectiveness of a binary classifier, which measures the classification ability of an algorithm under arbitrary distribution or any misclassification cost. In this study, we have proposed a network-based multi-biomarker identification method by AUC optimization (NetAUC), which integrates gene expression and the network information to identify biomarkers for the complex disease analysis. The main purpose is to optimize two objectives simultaneously: maximizing AUC and minimizing the number of selected features. We have applied NetAUC to two types of disease analysis: 1) prognosis of breast cancer, 2) classification of similar diseases. The results show that NetAUC can identify a small panel of disease-related biomarkers which have the powerful classification ability and the functional interpretability.

Anti-neoplastic characteristics and potential targets of calycosin against bisphenol A-related osteosarcoma: bioinformatics analysis

Environmentally, bisphenol A (BPA) is a well-known pollutant caused human health risk, including osteosarcoma (OS). OS, a deadly bone neoplasia, may occur in children and adults. However, the anti-OS pharmacotherapy prescribes limitedly in clinical practice. Interestingly, previous experimental evidences indicate calycosin-exerting potential anti-OS actions. Thus, in this report, we aimed to further characterize and detail the therapeutic targets and molecular mechanisms of calycosin-anti-BPA-related OS by using network pharmacology and molecular docking analyses. In results, the bioinformatics data disclosed all mapped, core targets, biological functions, molecular pathways of calycosin to treat BPA-related OS. The computational analysis using molecular docking indicated that potential binding ability of core targets in calycosin to treat BPA-related OS was identified. Moreover, detailed biological functions and optimal pathways of calycosin-anti-BPA-related OS were revealed, as shown in integrated network maps. Taken together, these network pharmacology and structural biology findings illustrate the core biotargets, pharmacological functions and pathways of calycosin-anti-BPA-related OS. Potentially, these core targets identified by molecular docking may attribute to the potential clinical application of calycosin against BPA-related OS.

Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning

Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, current diagnostic methods often subject patients to unnecessary surgical burden. In this study, we developed and validated an automated, highly accurate multi-study-derived diagnostic model for PTCs using personalized biological pathways coupled with a sophisticated machine learning algorithm. Surprisingly, the algorithm achieved near-perfect performance in discriminating PTCs from non-tumoral thyroid samples with an overall cross-study-validated area under the receiver operating characteristic curve (AUROC) of 0.999 (95% confidence interval [CI]: 0.995-1) and a Brier score of 0.013 on three independent development cohorts. In addition, the algorithm showed excellent generalizability and transferability on two large-scale external blind PTC cohorts consisting of The Cancer Genome Atlas (TCGA), which is the largest genomic PTC cohort studied to date, and the post-Chernobyl cohort, which includes PTCs reported after exposure to radiation from the Chernobyl accident. When applied to the TCGA cohort, the model yielded an AUROC of 0.969 (95% CI: 0.950-0.987) and a Brier score of 0.109. On the post-Chernobyl cohort, it yielded an AUROC of 0.962 (95% CI: 0.918-1) and a Brier score of 0.073. This algorithm also is robust against other various types of clinical scenarios, discriminating malignant from benign lesions as well as clinically aggressive thyroid cancer with poor prognosis from indolent ones. Furthermore, we discovered novel pathway alterations and prognostic signatures for PTC, which can provide directions for follow-up studies.

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.

Functional benefit and molecular mechanism of vitamin C against perfluorooctanesulfonate-associated leukemia

Perfluorooctanesulfonate (PFOS) is a persistent pollutant that can induce toxic effects, including leukemia, on blood cells. Vitamin C (VC), a functional nutrient, has been found to possess potent cytoprotective effects. However, there are currently no reports on its ability to treat PFOS-associated leukemia. This study used a molecular networking analysis to reveal the functional action and pharmacological mechanism of VC against PFOS-associated leukemia. The biological informatics findings revealed a total of 17 intersection targets against PFOS-associated leukemia. In addition, seven core-functional targets, including tumor protein p53 (TP53), mitogen-activated protein kinase 1 (MAPK1), estrogen receptor 1 (ESR1), sirtuin 1 (SIRT1), nitric oxide synthase 3 (NOS3), myeloid cell leukemia-1 (MCL1), and telomerase reverse transcriptase (TERT), were screened and identified. Notably, the molecular docking findings indicated that TP53, MAPK1, and ESR1 were potent pharmacological targets of VC against PFOS-associated leukemia. Moreover, the pharmacological functions including biological processes, cell components, and molecular pathways of VC against PFOS-associated leukemia were determined. According to the computational findings, we conclude that VC protects against PFOS-associated leukemia action by suppressing leukemia-associated cell proliferation and tumor growth. The validated genes of TP53, MAPK1, ESR1 may become potential biomarkers for monitoring and treating PFOS-associated leukemia.

Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data

Background

Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy.

Methods

In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations.

Results

Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response.

Conclusions

Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-2010-4) contains supplementary material, which is available to authorized users.