Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases

Biomedical research takes advantage of omic data, such as transcriptomics, to unravel the complexity of diseases. A conventional strategy identifies transcriptomic biomarkers characterized by expression patterns associated with a phenotype by relying on feature selection approaches. Hybrid ensemble feature selection (HEFS) has become increasingly popular as it ensures robustness of the selected features by performing data and functional perturbations. However, it remains difficult to make the best suited choices at each step when designing such approaches. We conducted an extensive analysis of four possible HEFS scenarios for the identification of Stage IV colorectal, Stage I kidney and lung and Stage III endometrial cancer biomarkers from transcriptomic data. These scenarios investigate the use of two types of feature reduction by filters (differentially expressed genes and variance) conjointly with two types of resampling strategies (repeated holdout by distribution-balanced stratified and random stratified) for downstream feature selection through an aggregation of thousands of wrapped machine learning models. Based on our results, we emphasize the advantages of using HEFS approaches to identify complex disease biomarkers, given their ability to produce generalizable and stable results to both data and functional perturbations. Finally, we highlight critical issues that need to be considered in the design of such strategies.

Targeting tumour metabolism in melanoma to enhance response to immune checkpoint inhibition: A balancing act

Immune checkpoint inhibition has transformed the treatment landscape of advanced melanoma and long-term survival of patients is now possible. However, at least half of the patients do not benefit sufficiently. Metabolic reprogramming is a hallmark of cancer cells and may contribute to both tumour growth and immune evasion by the tumour. Preclinical studies have indeed demonstrated that modulating tumour metabolism can reduce tumour growth while improving the functionality of immune cells. Since metabolic pathways are commonly shared between immune and tumour cells, it is essential to understand how modulating tumour metabolism in patients influences the intricate balance of pro-and anti-tumour immune effects in the tumour microenvironment. The key question is whether modulating tumour metabolism can inhibit tumour cell growth as well as facilitate an anti-tumour immune response. Here, we review current knowledge on the effect of tumour metabolism on the immune response in melanoma. We summarise metabolic pathways in melanoma and non-cancerous cells in the tumour microenvironment and discuss models and techniques available to study the metabolic-immune interaction. Finally, we discuss clinical use of these techniques to improve our understanding of how metabolic interventions can tip the balance towards a favourable, immune permissive microenvironment in melanoma patients.

Technology and Future of Multi-Cancer Early Detection

Cancer remains a significant global health challenge due to its high morbidity and mortality rates. Early detection is essential for improving patient outcomes, yet current diagnostic methods lack the sensitivity and specificity needed for identifying early-stage cancers. Here, we explore the potential of multi-omics approaches, which integrate genomic, transcriptomic, proteomic, and metabolomic data, to enhance early cancer detection. We highlight the challenges and benefits of data integration from these diverse sources and discuss successful examples of multi-omics applications in other fields. By leveraging these advanced technologies, multi-omics can significantly improve the sensitivity and specificity of early cancer diagnostics, leading to better patient outcomes and more personalized cancer care. We underscore the transformative potential of multi-omics approaches in revolutionizing early cancer detection and the need for continued research and clinical integration.

Multi-omic analysis of dysregulated pathways in triple negative breast cancer

The aggressive characteristics of triple-negative breast cancer (TNBC) and the absence of targeted medicines make TNBC a challenging clinical case. The molecular landscape of TNBC has been well-understood thanks to recent developments in multi-omic analysis, which have also revealed dysregulated pathways and possible treatment targets. This review summarizes the utilization of multi-omic approaches in elucidating TNBC's complex biology and therapeutic avenues. Dysregulated pathways including cell cycle progression, immunological modulation, and DNA damage response have been uncovered in TNBC by multi-omic investigations that integrate genomes, transcriptomics, proteomics, and metabolomics data. Methods like this pave the door for the discovery of new therapeutic targets, such as the EGFR, PARP, and mTOR pathways, which in turn direct the creation of more precise treatments. Recent developments in TNBC treatment strategies, including immunotherapy, PARP inhibitors, and antibody-drug conjugates, show promise in clinical trials. Emerging biomarkers like MUC1, YB-1, and immune-related markers offer insights into personalized treatment approaches and prognosis prediction. Despite the strengths of multi-omic analysis in offering a more comprehensive view and personalized treatment strategies, challenges exist. Large sample sizes and ensuring high-quality data remain crucial for reliable findings. Multi-omic analysis has revolutionized TNBC research, shedding light on dysregulated pathways, potential targets, and emerging biomarkers. Continued research efforts are imperative to translate these insights into improved outcomes for TNBC patients.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

Drug repurposing for neurodegenerative diseases

Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.

Paradigm shift required for translational research on the brain

Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Development of gene panel for predicting recurrence in early-stage cervical cancer patients

Cervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus-based gene panel using multi-omics data that could effectively predict recurrence in early-stage cervical cancer patients. We utilized the "Multi-Omics Consensus Integration Analysis (MOVICS)" package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early-stage CC. We identified the "resting and naive" tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning-driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan-Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early-stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.

Exploring protein profiles and hub genes in ameloblastoma

Ameloblastoma (AM) is a prominent benign odontogenic tumor characterized by aggressiveness, likely originating from tooth-generating tissue or the dental follicle (DF). However, proteomic distinctions between AM and DF remain unclear. In the present study, the aim was to identify the distinction between AM and DF in terms of their proteome and to determine the associated hub genes. Shotgun proteomics was used to compare the proteomes of seven fresh-frozen AM tissues and five DF tissues. Differentially expressed proteins (DEPs) were quantified and subsequently analyzed through Gene Ontology-based functional analysis, protein-protein interaction (PPI) analysis and hub gene identification. Among 7,550 DEPs, 520 and 216 were exclusive to AM and DF, respectively. Significant biological pathways included histone H2A monoubiquitination and actin filament-based movement in AM, as well as pro-B cell differentiation in DF. According to PPI analysis, the top-ranked upregulated hub genes were ubiquitin C (UBC), breast cancer gene 1 (BRCA1), lymphocyte cell-specific protein-tyrosine kinase (LCK), Janus kinase 1 and ATR serine/threonine kinase, whereas the top-ranked downregulated hub genes were UBC, protein kinase, DNA-activated, catalytic subunit (PRKDC), V-Myc avian myelocytomatosis viral oncogene homolog (MYC), tumor protein P53 and P21 (RAC1) activated kinase 1. When combining upregulated and downregulated genes, UBC exhibited the highest degree and betweenness values, followed by MYC, BRCA1, PRKDC, embryonic lethal, abnormal vision, Drosophila, homolog-like 1, myosin heavy chain 9, amyloid beta precursor protein, telomeric repeat binding factor 2, LCK and filamin A. In summary, these findings contributed to the knowledge on AM protein profiles, potentially aiding future research regarding AM etiopathogenesis and leading to AM prevention and treatment.

Construction and validation of classification models for predicting the response to concurrent chemo-radiotherapy of patients with esophageal squamous cell carcinoma based on multi-omics data

BACKGROUND

Concurrent chemo-radiotherapy (CCRT) is the preferred non-surgical treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Unfortunately, some patients respond poorly, which leads to inappropriate or excessive treatment and affects patient survival. To accurately predict the response of ESCC patients to CCRT, we developed classification models based on the clinical, serum proteomic and radiomic data.

METHODS

A total of 138 ESCC patients receiving CCRT were enrolled in this study and randomly split into a training cohort (n = 92) and a test cohort (n = 46). All patients were classified into either complete response (CR) or incomplete response (non-CR) groups according to RECIST1.1. Radiomic features were extracted by 3Dslicer. Serum proteomic data was obtained by Olink proximity extension assay. The logistic regression model with elastic-net penalty and the R-package "rms" v6.2-0 were applied to construct classification and nomogram models, respectively. The area under the receiver operating characteristic curves (AUC) was used to evaluate the predictive performance of the models.

RESULTS

Seven classification models based on multi-omics data were constructed, of which Model-COR, which integrates five clinical, five serum proteomic, and seven radiomic features, achieved the best predictive performance on the test cohort (AUC = 0.8357, 95 % CI: 0.7158-0.9556). Meanwhile, patients predicted to be CR by Model-COR showed significantly longer overall survival than those predicted to be non-CR in both cohorts (Log-rank P = 0.0014 and 0.027, respectively). Furthermore, two nomogram models based on multi-omics data also performed well in predicting response to CCRT (AUC = 0.8398 and 0.8483, respectively).

CONCLUSION

We developed and validated a multi-omics based classification model and two nomogram models for predicting the response of ESCC patients to CCRT, which achieved the best prediction performance by integrating clinical, serum Olink proteomic, and radiomic data. These models could be useful for personalized treatment decisions and more precise clinical radiotherapy and chemotherapy for ESCC patients.

dCas9 Tells Tales: Probing Gene Function and Transcription Regulation in Cancer

Clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing is evolving into an essential tool in the field of biological and medical research. Notably, the development of catalytically deactivated Cas9 (dCas9) enzyme has substantially broadened its traditional boundaries in gene editing or perturbation. The conjugation of dCas9 with various molecular effectors allows precise control over transcriptional processes, epigenetic modifications, visualization of chromosomal dynamics, and several other applications. This expanded repertoire of CRISPR-Cas9 applications has emerged as an invaluable molecular tool kit that empowers researchers to comprehensively interrogate and gain insights into health and diseases. This review delves into the advancements in Cas9 protein engineering, specifically on the generation of various dCas9 tools that have significantly enhanced the CRISPR-based technology capability and versatility. We subsequently discuss the multifaceted applications of dCas9, especially in interrogating the regulation and function of genes that involve in supporting cancer pathogenesis. In addition, we also delineate the designing and utilization of dCas9-based tools as well as highlighting its current constraints and transformative potentials in cancer research.

Purine salvage-associated metabolites as biomarkers for early diagnosis of esophageal squamous cell carcinoma: a diagnostic model-based study

Esophageal squamous cell carcinoma (ESCC) remains an important health concern in developing countries. Patients with advanced ESCC have a poor prognosis and survival rate, and achieving early diagnosis remains a challenge. Metabolic biomarkers are gradually gaining attention as early diagnostic biomarkers. Hence, this multicenter study comprehensively evaluated metabolism dysregulation in ESCC through an integrated research strategy to identify key metabolite biomarkers of ESCC. First, the metabolic profiles were examined in tissue and serum samples from the discovery cohort (n = 162; ESCC patients, n = 81; healthy volunteers, n = 81), and ESCC tissue-induced metabolite alterations were observed in the serum. Afterward, RNA sequencing of tissue samples (n = 46) was performed, followed by an integrated analysis of metabolomics and transcriptomics. The potential biomarkers for ESCC were further identified by censoring gene-metabolite regulatory networks. The diagnostic value of the identified biomarkers was validated in a validation cohort (n = 220), and the biological function was verified. A total of 457 dysregulated metabolites were identified in the serum, of which 36 were induced by tumor tissues. The integrated analyses revealed significant alterations in the purine salvage pathway, wherein the abundance of hypoxanthine/xanthine exhibited a positive correlation with HPRT1 expression and tumor size. A diagnostic model was developed using two purine salvage-associated metabolites. This model could accurately discriminate patients with ESCC from normal individuals, with an area under the curve (AUC) (95% confidence interval (CI): 0.680-0.843) of 0.765 in the external cohort. Hypoxanthine and HPRT1 exerted a synergistic effect in terms of promoting ESCC progression. These findings are anticipated to provide valuable support in developing novel diagnostic approaches for early ESCC and enhance our comprehension of the metabolic mechanisms underlying this disease.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Recent advances in micro-physiological systems for investigating tumor metastasis and organotropism

Tumor metastasis involves complex processes that traditional 2D cultures and animal models struggle to fully replicate. Metastatic tumors undergo a multitude of transformations, including genetic diversification, adaptation to diverse microenvironments, and modified drug responses, contributing significantly to cancer-related mortality. Micro-physiological systems (MPS) technology emerges as a promising approach to emulate the metastatic process by integrating critical biochemical, biomechanical, and geometrical cues at a microscale. These systems are particularly advantageous simulating metastasis organotropism, the phenomenon where tumors exhibit a preference for metastasizing to particular organs. Organotropism is influenced by various factors, such as tumor cell characteristics, unique organ microenvironments, and organ-specific vascular conditions, all of which can be effectively examined using MPS. This review surveys the recent developments in MPS research from the past five years, with a specific focus on their applications in replicating tumor metastasis and organotropism. Furthermore, we discuss the current limitations in MPS-based studies of organotropism and propose strategies for more accurately replicating and analyzing the intricate aspects of organ-specific metastasis, which is pivotal in the development of targeted therapeutic approaches against metastatic cancers.

Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification

BACKGROUND

Characterizing cancer molecular subtypes is crucial for improving prognosis and individualized treatment. Integrative analysis of multi-omics data has become an important approach for disease subtyping, yielding better understanding of the complex biology. Current multi-omics integration tools and methods for cancer subtyping often suffer challenges of high computational efficiency as well as the problem of weight assignment on data types.

RESULTS

Here, we present an efficient multi-omics integration via weighted affinity and self-diffusion (MOSD) to dissect cancer heterogeneity. MOSD first construct local scaling affinity on each data type and then integrate all affinities by weighted linear combination, followed by the self-diffusion to further improve the patients' similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness for cancer subtyping, we apply MOSD across ten cancer types with three measurements (Gene expression, DNA methylation, miRNA).

CONCLUSIONS

Our approach exhibits more significant differences in patient survival and computationally efficient benchmarking against several state-of-art integration methods and the identified molecular subtypes reveal strongly biological interpretability. The code as well as its implementation are available in GitHub: https://github.com/DXCODEE/MOSD .

Construction of an immune-related risk score signature for gastric cancer based on multi-omics data

Early identification of gastric cancer (GC) is associated with a superior survival rate compared to advanced GC. However, the poor specificity and sensitivity of traditional biomarkers suggest the importance of identifying more effective biomarkers. This study aimed to identify novel biomarkers for the prognosis of GC and construct a risk score (RS) signature based on these biomarkers, with to validation of its predictive performance. We used multi-omics data from The Cancer Genome Atlas to analyze the significance of differences in each omics data and combined the data using Fisher's method. Hub genes were subsequently subjected to univariate Cox and LASSO regression analyses and used to construct the RS signature. The RS of each patient was calculated, and the patients were divided into two subgroups according to the RS. The RS signature was validated in two independent datasets from the Gene Expression Omnibus and subsequent analyses were subsequently conducted. Five immune-related genes strongly linked to the prognosis of GC patients were obtained, namely CGB5, SLC10A2, THPO, PDGFRB, and APOD. The results revealed significant differences in overall survival between the two subgroups (p < 0.001) and indicated the high accuracy of the RS signature. When validated in two independent datasets, the results were consistent with those in the training dataset (p = 0.003 and p = 0.001). Subsequent analyses revealed that the RS signature is independent and has broad applicability among various GC subtypes. In conclusion, we used multi-omics data to obtain five immune-related genes comprising the RS signature, which can independently and effectively predict the prognosis of GC patients with high accuracy.

Overcoming Chemotherapy Resistance in Metastatic Cancer: A Comprehensive Review

The management of metastatic cancer is complicated by chemotherapy resistance. This manuscript provides a comprehensive academic review of strategies to overcome chemotherapy resistance in metastatic cancer. The manuscript presents background information on chemotherapy resistance in metastatic cancer cells, highlighting its clinical significance and the current challenges associated with using chemotherapy to treat metastatic cancer. The manuscript delves into the molecular mechanisms underlying chemotherapy resistance in subsequent sections. It discusses the genetic alterations, mutations, and epigenetic modifications that contribute to the development of resistance. Additionally, the role of altered drug metabolism and efflux mechanisms, as well as the activation of survival pathways and evasion of cell death, are explored in detail. The strategies to overcome chemotherapy resistance are thoroughly examined, covering various approaches that have shown promise. These include combination therapy approaches, targeted therapies, immunotherapeutic strategies, and the repurposing of existing drugs. Each strategy is discussed in terms of its rationale and potential effectiveness. Strategies for early detection and monitoring of chemotherapy drug resistance, rational drug design vis-a-vis personalized medicine approaches, the role of predictive biomarkers in guiding treatment decisions, and the importance of lifestyle modifications and supportive therapies in improving treatment outcomes are discussed. Lastly, the manuscript outlines the clinical implications of the discussed strategies. It provides insights into ongoing clinical trials and emerging therapies that address chemotherapy resistance in metastatic cancer cells. The manuscript also explores the challenges and opportunities in translating laboratory findings into clinical practice and identifies potential future directions and novel therapeutic avenues. This comprehensive review provides a detailed analysis of strategies to overcome chemotherapy resistance in metastatic cancer. It emphasizes the importance of understanding the molecular mechanisms underlying resistance and presents a range of approaches for addressing this critical issue in treating metastatic cancer.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

Multi-omics fusion with soft labeling for enhanced prediction of distant metastasis in nasopharyngeal carcinoma patients after radiotherapy

Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in data sources and medical imaging equipment. Due to these differences, the distribution of omics futures exhibits spatial heterogeneity, diminishing their capacity to enhance subsequent tasks. To overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology for nasopharyngeal carcinoma (NPC) distant metastasis prediction to mitigate the disparities inherent in omics data. The multi-kernel late-fusion method can reduce the impact of these differences by mapping the features using the most suiTable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. The proposed approach in this study employs a distinctive framework incorporating a label-softening technique alongside a multi-kernel-based Radial basis function (RBF) neural network to address these limitations. An efficient representation of the data may be achieved by utilizing the multi-kernel to map the inherent features and then merging them in a space with many dimensions. However, the inflexibility of label fitting poses a constraint on using multi-kernel late-fusion methods in complex NPC datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. The label softening increases the disparity between the two cohorts, providing a more flexible structure for allocating labels. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients.

Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis

BACKGROUND

Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods.

METHODS

On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas.

RESULTS

Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.

CONCLUSION

Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Integration of exome-seq and mRNA-seq using DawnRank, identified genes involved in innate immunity as drivers of breast cancer in the Indian cohort

Genetic heterogeneity influences the prognosis and therapy of breast cancer. The cause of disease progression varies and can be addressed individually. To identify the mutations and their impact on disease progression at an individual level, we sequenced exome and transcriptome from matched normal-tumor samples. We utilised DawnRank to prioritise driver genes and identify specific mutations in Indian patients. Mutations in the C3 and HLA genes were identified as drivers of disease progression, indicating the involvement of the innate immune system. We performed immune profiling on 16 matched normal/tumor samples using CIBERSORTx. We identified CD8+ve T cells, M2 macrophages, and neutrophils to be enriched in luminal A and T cells CD4+naïve, natural killer (NK) cells activated, T follicular helper (Tfh) cells, dendritic cells activated, and neutrophils in triple-negative breast cancer (TNBC) subtypes. Weighted gene co-expression network analysis (WGCNA) revealed activation of T cell-mediated response in ER positive samples and Interleukin and Interferons in ER negative samples. WGCNA analysis also identified unique pathways for each individual, suggesting that rare mutations/expression signatures can be used to design personalised treatment.

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

Medical gas plasma technology: Roadmap on cancer treatment and immunotherapy

Despite continuous therapeutic progress, cancer remains an often fatal disease. In the early 2010s, first evidence in rodent models suggested promising antitumor action of gas plasma technology. Medical gas plasma is a partially ionized gas depositing multiple physico-chemical effectors onto tissues, especially reactive oxygen and nitrogen species (ROS/RNS). Today, an evergrowing body of experimental evidence suggests multifaceted roles of medical gas plasma-derived therapeutic ROS/RNS in targeting cancer alone or in combination with oncological treatment schemes such as ionizing radiation, chemotherapy, and immunotherapy. Intriguingly, gas plasma technology was recently unraveled to have an immunological dimension by inducing immunogenic cell death, which could ultimately promote existing cancer immunotherapies via in situ or autologous tumor vaccine schemes. Together with first clinical evidence reporting beneficial effects in cancer patients following gas plasma therapy, it is time to summarize the main concepts along with the chances and limitations of medical gas plasma onco-therapy from a biological, immunological, clinical, and technological point of view.

Integration of gene expression and DNA methylation data across different experiments

Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the two omics by training on multi-omic data measured on the same set of samples. In comprehensive testing on 11 TCGA (The Cancer Genome Atlas) cancer datasets spanning 4329 patients, INTEND achieves significantly superior results compared with four state-of-the-art integration algorithms. We also demonstrate INTEND's ability to uncover connections between DNA methylation and the regulation of gene expression in the joint analysis of two lung adenocarcinoma single-omic datasets from different sources. INTEND's data-driven approach makes it a valuable multi-omic data integration tool. The code for INTEND is available at https://github.com/Shamir-Lab/INTEND.

Expanding the concept of serotoninomics: perspectives for serotonin studies in the 20's of the 21st century

Surely, Vittorio Erspamer, discoverer of Enteramine in 1935, and Irvine Page, Maurice M. Rapport and Arda Green, discoverers of Serotonin in 1948, never imagined the biological importance that this fundamental molecule has in the living beings of our planet; from its physiological, passing through endocrine, neural, developmental and reproductive functions and even its role in evolution. For this reason, our workgroup is commemorating these researchers and celebrating their great discovery, which deeply influenced science and medicine, in the present perspective article. As a consequence of their seminal work, and the work of many other researchers in the field of serotonin over the following years, now we stand in front of the practical concept of "Serotoninomics," which we think will contribute to find out precise answers regarding basic, clinical, and translational research related to serotonin, just as the emerging medical and "omics" sciences have done before.

Enhancing biohydrogen production from mono-substrates and co-substrates using a novel bacterial strains

The staggering increase in pollution associated with a sharp tightening in global energy demand is a major concern for organic substances. Renewable biofuel production through simultaneous waste reduction is a sustainable approach to meet this energy demand. This study co-fermentation of dairy whey and SCB was performed using mixed and pure bacterial cultures of Salmonella bongori, Escherichia coli, and Shewanella oneidensis by dark fermentation process for hydrogen production. The maximum H2 production was 202.7 ± 5.5 H2/mL/L, 237.3 ± 6.0 H2/mL/L, and 198 ± 9.9 H2/mL/L obtained in fermentation reactions containing dairy whey, solid and liquid hydrolysis of pretreated sugarcane bagasse as mono-substrates. The H2 production was greater in co-substrate by 347.3 ± 18.5 H2/mL/L under optimized conditions (pH 7.0, temperature 37 °C, substrate concentration 30:50 g/L) than expected in mono-substrate conditions, which confirms that co-fermentation of different substrates enhances the H2 potential. Fermentation medium during bio-H2 production under GC analysis has stated that using mixed cultures in dark fermentation favored acetic acid and butyric acid. Co-substrate degradation produces ethyl alcohol, benzoic acid, propionic acid, and butanol as metabolic by-products. The difference in the treated and untreated substrate and carbon enrichment in the substrates was evaluated by FT-IR analysis. The present study justifies that rather than the usage of mono-substrate for bio-H2 production, the co-substrate provided highly stable H2 production by mixed bacterial cultures. Fabricate the homemade single-chamber microbial fuel cell to generate electricity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03687-9.

Role of Surgical Pathologist for the Detection of Immuno-oncologic Predictive Factors in Non-small Cell Lung Cancers

Until very recently, surgery, chemotherapy, and radiation therapy have been the mainstay of treatment in non-small cell carcinomas (NSCLCs). However, recent advances in molecular immunology have unveiled some of the complexity of the mechanisms regulating cellular immune responses and led to the successful targeting of immune checkpoints in attempts to enhance antitumor T-cell responses. Immune checkpoint molecules such as cytotoxic T-lymphocyte associated protein-4, programmed cell death protein-1, and programmed death ligand (PD-L) 1 have been shown to play central roles in evading cancer immunity. Thus, these molecules have been targeted by inhibitors for the management of cancers forming the basis of immunotherapy. Advanced NSCLC has been the paradigm for the benefits of immunotherapy in any cancer. Treatment decisions are made based on the expression of PD-L1 on the tumor cells and the presence or absence of driver mutations. Patients with high PD-L1 expression (≥50%) and no driver mutations are treated with single-agent immunotherapy whereas, for all other patients with a lower level of PD-L1 expression, a combination of chemotherapy and immunotherapy is preferred. Thus, PD-L1 blockers are the only immunotherapeutic agents approved in advanced NSCLC without any oncogenic driver mutations. PD-L1 immunohistochemistry, however, may not be the best biomarker in view of its dynamic nature in time and space, and the benefits may be seen regardless of PD -L1 expression. Each immunotherapy molecule is prescribed based on the levels of PD-L1 expression as assessed by a Food and Drug Administration-approved companion diagnostic assay. Other biomarkers that have been studied include tumor mutational burden, the T-effector signature, tumor-infiltrating lymphocytes, radiomic assays, inflammation index, presence or absence of immune-related adverse events and specific driver mutations, and gut as well as local microbiome. At the current time, none of these biomarkers are routinely used in the clinical decision-making process for immunotherapy in NSCLC. However, in individual cases, they can be useful adjuncts to conventional therapy. This review describes our current understanding of the role of biomarkers as predictors of response to immune checkpoint molecules. To begin with a brief on cancer immunology in general and in NSCLC, in particular, is discussed. In the end, recent advancements in laboratory techniques for refining biomarker assays are described.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

An overview of glioblastoma multiforme and temozolomide resistance: can LC-MS-based proteomics reveal the fundamental mechanism of temozolomide resistance?

Glioblastoma multiforme (GBM) is a primary type of lethal brain tumor. Over the last two decades, temozolomide (TMZ) has remained the primary chemotherapy for GBM. However, TMZ resistance in GBM constitutes an underlying factor contributing to high rates of mortality. Despite intense efforts to understand the mechanisms of therapeutic resistance, there is currently a poor understanding of the molecular processes of drug resistance. For TMZ, several mechanisms linked to therapeutic resistance have been proposed. In the past decade, significant progress in the field of mass spectrometry-based proteomics has been made. This review article discusses the molecular drivers of GBM, within the context of TMZ resistance with a particular emphasis on the potential benefits and insights of using global proteomic techniques.

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

BACKGROUND

Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.

METHODS

We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.

RESULTS

Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.

CONCLUSION

Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.

Pulmonary arterial hypertension associated with congenital heart disease: An omics study

Pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) is a severely progressive condition with uncertain physiological course. Hence, it has become increasingly relevant to clarify the specific mechanisms of molecular modification, which is crucial to identify more treatment strategies. With the rapid development of high-throughput sequencing, omics technology gives access to massive experimental data and advanced techniques for systems biology, permitting comprehensive assessment of disease occurrence and progression. In recent years, significant progress has been made in the study of PAH-CHD and omics. To provide a comprehensive description and promote further in-depth investigation of PAH-CHD, this review attempts to summarize the latest developments in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and multi-omics integration.

Atomic force microscopy-based assessment of multimechanical cellular properties for classification of graded bladder cancer cells and cancer early diagnosis using machine learning analysis

Cellular mechanical properties (CMPs) have been frequently reported as biomarkers for cell cancerization to assist objective cytology, compared to the current subjective method dependent on cytomorphology. However, single or dual CMPs cannot always successfully distinguish every kind of malignant cell from its benign counterpart. In this work, we extract 4 CMPs of four different graded bladder cancer (BC) cell lines by AFM (atomic force microscopy)-based nanoindentation to generate a CMP database, which is used to train a cancerization-grade classifier by machine learning. The classifier is tested on 4 categories of BC cells at different cancer grades. The classification shows split-independent robustness and an accuracy of 91.25% with an AUC-ROC (ROC stands for receiver operating characteristic, and ROC curve is a graphical plot which illustrates the performance of a binary classifier system as its discrimination threshold is varied) value of 97.98%. Finally, we also compare our proposed method with traditional invasive diagnosis and noninvasive cancer diagnosis relying on cytomorphology, in terms of accuracy, sensitivity and specificity. Unlike former studies focusing on the discrimination between normal and cancerous cells, our study fulfills the classification of 4 graded cell lines at different cancerization stages, and thus provides a potential method for early detection of cancerization. STATEMENT OF SIGNIFICANCE: We measured four cellular mechanical properties (CMPs) of 4 graded bladder cancer (BC) cell lines using AFM (atomic force microscopy). We found that single or dual CMPs cannot fulfill the task of BC cell classification. Instead, we employ MLA (Machine Learning Algorithm)-based analysis whose inputs are BC CMPs. Compared with traditional cytomorphology-based prognoses, the non-invasive method proposed in this study has higher accuracy but with many fewer cellular properties as inputs. The proposed non-invasive prognosis is characterized with high sensitivity and specificity, and thus provides a potential tumor-grading means to identify cancer cells with different metastatic potential. Moreover, our study proposes an objective grading method based on quantitative characteristics desirable for avoiding misdiagnosis induced by ambiguous subjectivity.

Diagnostic value of liquid biopsy in the era of precision medicine: 10 years of clinical evidence in cancer

Liquid biopsy is a diagnostic repeatable test, which in last years has emerged as a powerful tool for profiling cancer genomes in real-time with minimal invasiveness and tailoring oncological decision-making. It analyzes different blood-circulating biomarkers and circulating tumor DNA (ctDNA) is the preferred one. Nevertheless, tissue biopsy remains the gold standard for molecular evaluation of solid tumors whereas liquid biopsy is a complementary tool in many different clinical settings, such as treatment selection, monitoring treatment response, cancer clonal evolution, prognostic evaluation, as well as the detection of early disease and minimal residual disease (MRD). A wide number of technologies have been developed with the aim of increasing their sensitivity and specificity with acceptable costs. Moreover, several preclinical and clinical studies have been conducted to better understand liquid biopsy clinical utility. Anyway, several issues are still a limitation of its use such as false positive and negative results, results interpretation, and standardization of the panel tests. Although there has been rapid development of the research in these fields and recent advances in the clinical setting, many clinical trials and studies are still needed to make liquid biopsy an instrument of clinical routine. This review provides an overview of the current and future clinical applications and opening questions of liquid biopsy in different oncological settings, with particular attention to ctDNA liquid biopsy.

Probable Treatment Targets for Diabetic Retinopathy Based on an Integrated Proteomic and Genomic Analysis

Purpose

Using previously approved medications for new indications can expedite the lengthy and expensive drug development process. We describe a bioinformatics pipeline that integrates genomics and proteomics platforms to identify already-approved drugs that might be useful to treat diabetic retinopathy (DR).

Methods

Proteomics analysis of vitreous humor samples from 12 patients undergoing pars plana vitrectomy for DR and a whole genome dataset (UKBiobank TOPMed-imputed) from 1330 individuals with DR and 395,155 controls were analyzed independently to identify biological pathways associated with DR. Common biological pathways shared between both datasets were further analyzed (STRING and REACTOME analyses) to identify target proteins for probable drug modulation. Curated target proteins were subsequently analyzed by the BindingDB database to identify chemical compounds they interact with. Identified chemical compounds were further curated through the Expasy SwissSimilarity database for already-approved drugs that interact with target proteins.

Results

The pathways in each dataset (proteomics and genomics) converged in the upregulation of a previously unknown pathway involved in DR (RUNX2 signaling; constituents MMP-13 and LGALS3), with an emphasis on its role in angiogenesis and blood-retina barrier. Bioinformatics analysis identified U.S. Food and Drug Administration (FDA)-approved medications (raltitrexed, pemetrexed, glyburide, probenecid, clindamycin hydrochloride, and ticagrelor) that, in theory, may modulate this pathway.

Conclusions

The bioinformatics pipeline described here identifies FDA-approved drugs that can be used for new alternative indications. These theoretical candidate drugs should be validated with experimental studies.

Translational Relevance

Our study suggests possible drugs for DR treatment based on an integrated proteomics and genomics pipeline. This approach can potentially expedite the drug discovery process by identifying already-approved drugs that might be used for new indications.

Mechanism-Driven and Clinically Focused Development of Botanical Foods as Multitarget Anticancer Medicine: Collective Perspectives and Insights from Preclinical Studies, IND Applications and Early-Phase Clinical Trials

Cancer progression and mortality remain challenging because of current obstacles and limitations in cancer treatment. Continuous efforts are being made to explore complementary and alternative approaches to alleviate the suffering of cancer patients. Epidemiological and nutritional studies have indicated that consuming botanical foods is linked to a lower risk of cancer incidence and/or improved cancer prognosis after diagnosis. From these observations, a variety of preclinical and clinical studies have been carried out to evaluate the potential of botanical food products as anticancer medicines. Unfortunately, many investigations have been poorly designed, and encouraging preclinical results have not been translated into clinical success. Botanical products contain a wide variety of chemicals, making them more difficult to study than traditional drugs. In this review, with the consideration of the regulatory framework of the USFDA, we share our collective experiences and lessons learned from 20 years of defining anticancer foods, focusing on the critical aspects of preclinical studies that are required for an IND application, as well as the checkpoints needed for early-phase clinical trials. We recommend a developmental pipeline that is based on mechanisms and clinical considerations.

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization

Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

PYY modulates the tumorigenesis and progression of colorectal cancer unveiled by proteomics

Despite decrease in mortality caused by colorectal cancer (CRC), there remains no effective therapeutic method for patients with CRC. We attempted to screen biomarkers with therapeutic values in CRC. Proteomic analysis was performed on tumor, tumor-adjacent, and normal tissues derived from five patients with colon adenocarcinoma (COAD) via label-free proteome profiling. Differentially expressed proteins (DEPs) were identified, and functional annotation was performed based on the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The effect of marker proteins on CRC was determined via cell function experiments and using tumor organoid models. The localization of the marker proteins was determined via immunofluorescence. A total of 126 DEPs were identified in COAD tissues compared with normal tissues, of which Peptide YY (PYY) overlapped among the tumor, adjacent, and normal groups. DEPs in the cancer group vs. normal group were enriched in the regulation of cell cycle checkpoint, developmental process, focal adhesion, and apoptosis-related pathways. The low expression of PYY in CRC tissues was verified via qRT-PCR, western blotting, and immunohistochemistry. Overexpression of PYY promoted apoptosis and inhibited the proliferation, migration, and invasion of HCT116 and HT29 cells. Furthermore, PYY was secreted by neurons and its supplementation suppressed tumor organoid growth in a dose-dependent manner. In conclusion, PYY exerted inhibitory action on CRC and could be a therapeutic target for CRC.

TET3 governs malignant behaviors and unfavorable prognosis of esophageal squamous cell carcinoma by activating the PI3K/AKT/GSK3β/β-catenin pathway

Ten–eleven translocation 3 (TET3) participates in tumorigenesis and malignant transformation by mediating DNA demethylation and specific gene activation in malignances. This study aims to elucidate its molecular function and regulatory mechanism in esophageal squamous cell carcinoma (ESCC). Stable ESCC cells that infected with TET3 overexpression (OE) and knockdown lentiviral vector had been established. The biological behaviors and molecular mechanism of TET3 were demonstrated by cell biology experiments in vitro and in vivo. Tissues from patients with ESCC were used to demonstrate the clinical value of TET3. Our findings revealed that TET3 is highly expressed in ESCC tissues and related to poor prognosis of patients with ESCC. OE of TET3 presented a significant effect on proliferation, metastatic potential, and spheroid formation of ESCC cells by activating the PI3K/AKT/GSK3β/β-catenin axis. Knockdown of TET3 could remarkably reverse these malignant phenotypes. Patients with ESCC with high TET3 expression resulted in a shorter overall survival (OS) and disease-free survival. Based on the multivariate analysis, TET3 could be an independent favorable factor for predicting OS and recurrence. The high expression of TET3 not only aggravates malignant behaviors in vitro and in vivo but also becomes a novel biomarker for clinical monitoring and individualized precision treatment for patients with ESCC.

The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Simultaneous metabolite MALDI-MSI, whole exome and transcriptome analysis from formalin-fixed paraffin-embedded tissue sections

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) allows spatial analysis of proteins, metabolites, or small molecules from tissue sections. Here, we present the simultaneous generation and analysis of MALDI-MSI, whole-exome sequencing (WES), and RNA-sequencing data from the same formalin-fixed paraffin-embedded (FFPE) tissue sections. Genomic DNA and total RNA were extracted from (i) untreated, (ii) hematoxylin-eosin (HE) stained, and (iii) MALDI-MSI-analyzed FFPE tissue sections from three head and neck squamous cell carcinomas. MALDI-MSI data were generated by a time-of-flight analyzer prior to preprocessing and visualization. WES data were generated using a low-input protocol followed by detection of single-nucleotide variants (SNVs), tumor mutational burden, and mutational signatures. The transcriptome was determined using 3'-RNA sequencing and was examined for similarities and differences between processing stages. All data met the commonly accepted quality criteria. Besides SNVs commonly identified between differently processed tissues, FFPE-typical artifactual variants were detected. Tumor mutational burden was in the same range for tissues from the same patient and mutational signatures were highly overlapping. Transcriptome profiles showed high levels of correlation. Our data demonstrate that simultaneous molecular profiling of MALDI-MSI-processed FFPE tissue sections at the transcriptome and exome levels is feasible and reliable.

Metabolomics Analysis Revealed Significant Metabolic Changes in Brain Cancer Cells Treated with Paclitaxel and/or Etoposide

Cancer of the central nervous system (CNS) is ranked as the 19th most prevalent form of the disease in 2020. This study aims to identify candidate biomarkers and metabolic pathways affected by paclitaxel and etoposide, which serve as potential treatments for glioblastoma, and are linked to the pathogenesis of glioblastoma. We utilized an untargeted metabolomics approach using the highly sensitive ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS) for identification. In this study, 92 and 94 metabolites in U87 and U373 cell lines were profiled, respectively. The produced metabolites were then analyzed utilizing t-tests, volcano plots, and enrichment analysis modules. Our analysis revealed distinct metabolites to be significantly dysregulated (nutriacholic acid, L-phenylalanine, L-arginine, guanosine, ADP, hypoxanthine, and guanine), and to a lesser extent, mevalonic acid in paclitaxel and/or etoposide treated cells. Furthermore, both urea and citric acid cycles, and metabolism of polyamines and amino acids (aspartate, arginine, and proline) were significantly enriched. These findings can be used to create a map that can be utilized to assess the antitumor effect of paclitaxel and/or etoposide within the studied cancer cells.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.