PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation.

Despite advanced diagnostics, 3%-5% of cases remain classified as cancer of unknown primary (CUP). DNA methylation, an important epigenetic feature, is essential for determining the origin of metastatic tumors. We presented PathMethy, a novel Transformer model integrated with functional categories and crosstalk of pathways, to accurately trace the origin of tumors in CUP samples based on DNA methylation. PathMethy outperformed seven competing methods in F1-score across nine cancer datasets and predicted accurately the molecular subtypes within nine primary tumor types. It not only excelled at tracing the origins of both primary and metastatic tumors but also demonstrated a high degree of agreement with previously diagnosed sites in cases of CUP. PathMethy provided biological insights by highlighting key pathways, functional categories, and their interactions. Using functional categories of pathways, we gained a global understanding of biological processes. For broader access, a user-friendly web server for researchers and clinicians is available at https://cup.pathmethy.com.

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis.

Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. eNet is robust and widely applicable in various human or mouse tissues datasets. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells.

Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high-resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq data. We proposed scRankXMBD, a computational framework to prioritize prognostic-associated cell subpopulations based on within-cell relative expression orderings of gene pairs from single-cell transcriptomes. scRankXMBD achieves higher precision and concordance compared with five existing methods. Moreover, we developed single-cell gene pair signatures to predict recurrence risk for patients individually. Our work facilitates the application of the rank-based method in scRNA-seq data for prognostic biomarker discovery and precision oncology. scRankXMBD is available at https://github.com/xmuyulab/scRank-XMBD. (XMBD:Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.).

Application of individualized differential expression analysis in human cancer proteome.

Liquid chromatography-mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.

Single-cell RNA-seq reveals altered NK cell subsets and reduced levels of cytotoxic molecules in patients with ankylosing spondylitis.

Ankylosing spondylitis (AS) is an autoimmune disease with unknown aetiology. To unravel the mechanisms mediating AS pathogenesis, we profiled peripheral blood mononuclear cells (PBMCs) from AS patients and healthy subjects using 10X single-cell RNA sequencing. The frequencies of immune cell subsets were evaluated by flow cytometry. NK cells were purified from PBMCs using isolation kit and were examined for gene expression by RT-qPCR. Plasma levels of cytolytic molecules were examined by enzyme-linked immunosorbent assay. Compared to healthy controls, AS patients showed a significant decrease in total NK cells as well as CD56dim NK subset, whereas CD56bright NK cells were increased. Additionally, impaired expression of cytotoxic genes in NK cells of AS patients was observed by bioinformatics algorithm and verified by RT-qPCR and flow cytometry. Consistent with changes in transcriptomics, we found decreased plasma levels of granzymes, but not granulysin, in AS patients. Furthermore, Pearson correlation analysis revealed a negative correlation between plasma GZMB levels and disease activity (r = -0.5275, p = 0.0358). No correlation was observed between plasma cytolytic molecules and biochemical indexes (ESR and CRP). Our findings uncover altered NK cell subsets and cytotoxic profiles in peripheral circulation of AS patients at single-cell resolution.

Personalized analysis of human cancer multi-omics for precision oncology.

Multi-omics technologies, encompassing genomics, proteomics, and transcriptomics, provide profound insights into cancer biology. A fundamental computational approach for analyzing multi-omics data is differential analysis, which identifies molecular distinctions between cancerous and normal tissues. Traditional methods, however, often fail to address the distinct heterogeneity of individual tumors, thereby neglecting crucial patient-specific molecular traits. This shortcoming underscores the necessity for tailored differential analysis algorithms, which focus on particular patient variations. Such approaches offer a more nuanced understanding of cancer biology and are instrumental in pinpointing personalized therapeutic strategies. In this review, we summarize the principles of current individualized techniques. We also review their efficacy in analyzing cancer multi-omics data and discuss their potential applications in clinical practice.

Tracing unknown tumor origins with a biological-pathway-based transformer model.

Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.

SIMarker: Cellular similarity detection and its application to diagnosis and prognosis of liver cancer.

BACKGROUND: The emergence of single-cell technology offers a unique opportunity to explore cellular similarity and heterogeneity between precancerous diseases and solid tumors. However, there is lacking a systematic study for identifying and characterizing similarities at single-cell resolution. METHODS: We developed SIMarker, a computational framework to detect cellular similarities between precancerous diseases and solid tumors based on gene expression at single-cell resolution. Taking hepatocellular carcinoma (HCC) as a case study, we quantified the cellular and molecular connections between HCC and cirrhosis. Core analysis modules of SIMarker is publicly available at https://github.com/xmuhuanglab/SIMarker ("SIM" means "similarity" and "Marker" means "biomarkers). RESULTS: We found PGA5+ hepatocytes in HCC showed cirrhosis-like characteristics, including similar transcriptional programs and gene regulatory networks. Consequently, the genes constituting the gene expression program of these cirrhosis-like subpopulations were designated as cirrhosis-like signatures (CLS). Strikingly, our utilization of CLS enabled the development of diagnosis and prognosis biomarkers based on within-sample relative expression orderings of gene pairs. These biomarkers achieved high precision and concordance compared with previous studies. CONCLUSIONS: Our work provides a systematic method to investigate the clinical translational significance of cellular similarities between HCC and cirrhosis, which opens avenues for identifying similar paradigms in other categories of cancers and diseases.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics.

BACKGROUND: Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application. METHODS: Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system. RESULTS: The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis. CONCLUSIONS: PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

Lymphatic-localized Treg-mregDC crosstalk limits antigen trafficking and restrains anti-tumor immunity.

The tumor microenvironment (TME) has a significant impact on tumor growth and immunotherapy efficacies. However, the precise cellular interactions and spatial organizations within the TME that drive these effects remain elusive. Using advanced multiplex imaging techniques, we have discovered that regulatory T cells (Tregs) accumulate around lymphatic vessels in the peripheral tumor stroma. This localized accumulation is facilitated by mature dendritic cells enriched in immunoregulatory molecules (mregDCs), which promote chemotaxis of Tregs, establishing a peri-lymphatic Treg-mregDC niche. Within this niche, mregDCs facilitate Treg activation, which in turn restrains the trafficking of tumor antigens to the draining mesenteric lymph nodes, thereby impeding the initiation of anti-tumor adaptive immune responses. Disrupting Treg recruitment to mregDCs inhibits tumor progression. Our study provides valuable insights into the organization of TME and how local crosstalk between lymphoid and myeloid cells suppresses anti-tumor immune responses.

CovidShiny: An Integrated Web Tool for SARS-CoV-2 Mutation Profiling and Molecular Diagnosis Assay Evaluation In Silico.

The coronavirus disease 2019 (COVID-19) pandemic is still ongoing, with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continuing to evolve and accumulate mutations. While various bioinformatics tools have been developed for SARS-CoV-2, a well-curated mutation-tracking database integrated with in silico evaluation for molecular diagnostic assays is currently unavailable. To address this, we introduce CovidShiny, a web tool that integrates mutation profiling, in silico evaluation, and data download capabilities for genomic sequence-based SARS-CoV-2 assays and data download. It offers a feasible framework for surveilling the mutation of SARS-CoV-2 and evaluating the coverage of the molecular diagnostic assay for SARS-CoV-2. With CovidShiny, we examined the dynamic mutation pattern of SARS-CoV-2 and evaluated the coverage of commonly used assays on a large scale. Based on our in silico analysis, we stress the importance of using multiple target molecular diagnostic assays for SARS-CoV-2 to avoid potential false-negative results caused by viral mutations. Overall, CovidShiny is a valuable tool for SARS-CoV-2 mutation surveillance and in silico assay design and evaluation.

WISP2 downregulation inhibits the osteogenic differentiation of BMSCs in congenital scoliosis by regulating Wnt/ß-catenin pathway.

OBJECTIVES: Bone marrow mesenchymal stem cells (BMSCs) are instrumental in bone development, metabolism, and marrow microenvironment homeostasis. Despite this, the relevant effects and mechanisms of BMSCs on congenital scoliosis (CS) remain undefined. Herein, it becomes our focus to reveal the corresponding effects and mechanisms implicated. METHODS: BMSCs from CS patients (hereafter referred as CS-BMSCs) and healthy donors (NC-BMSCs) were observed and identified. Differentially expressed genes in BMSCs were analyzed utilizing scRNA-seq and RNA-seq profiles. The multi-differentiation potential of BMSCs following the transfection or infection was evaluated. The expression levels of factors related to osteogenic differentiation and Wnt/ß-catenin pathway were further determined as appropriate. RESULTS: A decreased osteogenic differentiation ability was shown in CS-BMSCs. Both the proportion of LEPR+ BMSCs and the expression level of WNT1-inducible-signaling pathway protein 2 (WISP2) were decreased in CS-BMSCs. WISP2 knockdown suppressed the osteogenic differentiation of NC-BMSCs, while WISP2 overexpression facilitated the osteogenesis of CS-BMSCs via acting on the Wnt/ß-catenin pathway. CONCLUSIONS: Our study collectively indicates WISP2 knockdown blocks the osteogenic differentiation of BMSCs in CS by regulating Wnt/ß-catenin signaling, thus providing new insights into the aetiology of CS.

[Current understanding of intervertebral space height in anterior cervical fusion].

Anterior cervical fusion surgery is the first choice for spine surgeons in the treatment of cervical spine diseases. It has significant effects in treating cervical degenerative diseases, trauma and tumors and other cervical diseases. In anterior cervical fusion, it is necessary to use a distractor to properly distract the intervertebral space, so as to fully expose and relieve the compressive factors, restore the physiological height, curvature and stability of the lesion segment, and achieve the best surgical effect. However, there is currently no consensus on the standard distraction height for the intervertebral space during anterior cervical surgery. This article reviewsed the progress of intervertebral space height in anterior cervical fusion from three dimensions:the relationship between intervertebral space height and cervical disc degeneration mechanism, the selection of intervertebral space height during operation, the recovery of intervertebral space height and the postoperative effect, so as to provide theoretical basis and reference for spinal surgeons when performing intervertebral distraction during operation.

NQO1 is a determinant for cellular sensitivity to anti-tumor agent Napabucasin.

Napabucasin (NAPA) is thought to be a potent cancer stemness inhibitor in different types of cancer cell lines. While it has shown promising activity in early phase clinical trials, two recent phase III NAPA clinical trials failed to meet the primary endpoint of overall survival. The reason for the failure is not clear, but a possible way to revive the clinical trial is to stratify patients with biomarkers that could predict NAPA response. Here, we report the identification of NAD(P)H dehydrogenase 1 (NQO1) as a major determinant of NAPA efficacy. A proteomic profiling of cancer cell lines revealed that NQO1 abundance is negatively correlated with IC50; in vitro assays showed that NAPA is a substrate for NQO1, which mediates the generation of ROS that leads to cell death. Furthermore, activation of an NQO1 transcription factor NRF2 by chemicals, including an FDA approved drug, can increase the NAPA cytotoxicity. Our findings suggest a potential use of NQO1 expression as a companion diagnostic test to identify patients in future NAPA trials and a combination strategy to expand the application of NAPA-based regimens for cancer therapy.

Molecular subtyping of cancer and nomination of kinase candidates for inhibition with phosphoproteomics: Reanalysis of CPTAC ovarian cancer.

BACKGROUND: Molecular subtyping of cancer aimed to predict patient overall survival (OS) and nominate drug targets for patient treatments is central to precision oncology. Owing to the rapid development of phosphoproteomics, we can now measure thousands of phosphoproteins in human cancer tissues. However, limited studies report how to analyse the complex phosphoproteomic data for cancer subtyping and to nominate druggable kinase candidates. FINDINGS: In this work, we reanalysed the phosphoproteomic data of high-grade serous ovarian cancer (HGSOC) from the Clinical Proteomic Tumour Analysis Consortium (CPTAC). Our analysis classified HGSOC into 5 major subtypes that were associated with different OS and appeared to be more accurate than that achieved with protein profiling. We provided a workflow to identify 29 kinases whose increased activities in tumours are associated with poor survival. The altered kinase signalling landscape of HGSOC included the PI3K/AKT/mTOR, cell cycle and MAP kinase signalling pathways. We also developed a "patient-specific" hierarchy of clinically actionable kinases and selected kinase inhibitors by considering kinase activation and kinase inhibitor selectivity. INTERPRETATION: Our study offered a global phosphoproteomics data analysis workflow to aid in cancer molecular subtyping, determining phosphorylation-based cancer hallmarks and facilitating nomination of kinase inhibition in cancer.

Phosphoproteomics Enables Molecular Subtyping and Nomination of Kinase Candidates for Individual Patients of Diffuse-Type Gastric Cancer.

The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments.

Differential expression analysis for individual cancer samples based on robust within-sample relative gene expression orderings across multiple profiling platforms.

The highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue are widely reversed in the cancer condition. Based on this finding, we have recently proposed an algorithm named RankComp to detect differentially expressed genes (DEGs) for individual disease samples measured by a particular platform. In this paper, with 461 normal lung tissue samples separately measured by four commonly used platforms, we demonstrated that tens of millions of gene pairs with significantly stable REOs in normal lung tissue can be consistently detected in samples measured by different platforms. However, about 20% of stable REOs commonly detected by two different platforms (e.g., Affymetrix and Illumina platforms) showed inconsistent REO patterns due to the differences in probe design principles. Based on the significantly stable REOs (FDR<0.01) for normal lung tissue consistently detected by the four platforms, which tended to have large rank differences, RankComp detected averagely 1184, 1335 and 1116 DEGs per sample with averagely 96.51%, 95.95% and 94.78% precisions in three evaluation datasets with 25, 57 and 58 paired lung cancer and normal samples, respectively. Individualized pathway analysis revealed some common and subtype-specific functional mechanisms of lung cancer. Similar results were observed for colorectal cancer. In conclusion, based on the cross-platform significantly stable REOs for a particular normal tissue, differentially expressed genes and pathways in any disease sample measured by any of the platforms can be readily and accurately detected, which could be further exploited for dissecting the heterogeneity of cancer.

An individualized prognostic signature and multi­omics distinction for early stage hepatocellular carcinoma patients with surgical resection.

Previously reported prognostic signatures for predicting the prognoses of postsurgical hepatocellular carcinoma (HCC) patients are commonly based on predefined risk scores, which are hardly applicable to samples measured by different laboratories. To solve this problem, using gene expression profiles of 170 stage I/II HCC samples, we identified a prognostic signature consisting of 20 gene pairs whose within-sample relative expression orderings (REOs) could robustly predict the disease-free survival and overall survival of HCC patients. This REOs-based prognostic signature was validated in two independent datasets. Functional enrichment analysis showed that the patients with high-risk of recurrence were characterized by the activations of pathways related to cell proliferation and tumor microenvironment, whereas the low-risk patients were characterized by the activations of various metabolism pathways. We further investigated the distinct epigenomic and genomic characteristics of the two prognostic groups using The Cancer Genome Atlas samples with multi-omics data. Epigenetic analysis showed that the transcriptional differences between the two prognostic groups were significantly concordant with DNA methylation alternations. The signaling network analysis identified several key genes (e.g. TP53, MYC) with epigenomic or genomic alternations driving poor prognoses of HCC patients. These results help us understand the multi-omics mechanisms determining the outcomes of HCC patients.

Identifying Reproducible Molecular Biomarkers for Gastric Cancer Metastasis with the Aid of Recurrence Information.

To precisely diagnose metastasis state is important for tailoring treatments for gastric cancer patients. However, the routinely employed radiological and pathologic tests for tumour metastasis have considerable high false negative rates, which may retard the identification of reproducible metastasis-related molecular biomarkers for gastric cancer. In this research, using three datasets, we firstly shwed that differentially expressed genes (DEGs) between metastatic tissue samples and non-metastatic tissue samples could hardly be reproducibly detected with a proper statistical control when the metastatic and non-metastatic samples were defined by TNM stage alone. Then, assuming that undetectable micrometastases are the prime cause for recurrence of early stage patients with curative resection, we reclassified all the "non-metastatic" samples as metastatic samples whenever the patients experienced tumour recurrence during follow-up after tumour resection. In this way, we were able to find distinct and reproducible DEGs between the reclassified metastatic and non-metastatic tissue samples and concordantly significant DNA methylation alterations distinguishing metastatic tissues and non-metastatic tissues of gastric cancer. Our analyses suggested that the follow-up recurrence information for patients should be employed in the research of tumour metastasis in order to decrease the confounding effects of false non-metastatic samples with undetected micrometastases.

An individualized prognostic signature for gastric cancer patients treated with 5-Fluorouracil-based chemotherapy and distinct multi-omics characteristics of prognostic groups.

5-Fluorouracil (5-FU)-based chemotherapy is currently the first-line treatment for gastric cancer. In this study, using gene expression profiles for a panel of cell lines with drug sensitivity data and two cohorts of patients, we extracted a signature consisting of two gene pairs (KCNE2 and API5, KCNE2 and PRPF3) whose within-sample relative expression orderings (REOs) could robustly predict prognoses of gastric cancer patients treated with 5-FU-based chemotherapy. This REOs-based signature was insensitive to experimental batch effects and could be directly applied to samples measured by different laboratories. Taking this unique advantage of the REOs-based signature, we classified gastric cancer samples of The Cancer Genome Atlas (TCGA) into two prognostic groups with distinct transcriptional characteristics, circumventing the usage of confounded TCGA survival data. We further showed that the two prognostic groups displayed distinct copy number, gene mutation and DNA methylation landscapes using the TCGA multi-omics data. The results provided hints for understanding molecular mechanisms determining prognoses of gastric cancer patients treated with 5-FU-based chemotherapy.