OMICS insights into cancer histology; Metabolomics and proteomics approach

Extracellular vesicles and cancer stem cells: a deadly duo in tumor progression

The global incidence of cancer is increasing, with estimates suggesting that there will be 26 million new cases and 17 million deaths per year by 2030. Cancer stem cells (CSCs) and extracellular vesicles (EVs) are key to the resistance and advancement of cancer. They play a crucial role in tumor dynamics and resistance to therapy. CSCs, initially discovered in acute myeloid leukemia, are well-known for their involvement in tumor initiation, progression, and relapse, mostly because of their distinct characteristics, such as resistance to drugs and the ability to self-renew. EVs, which include exosomes, microvesicles, and apoptotic bodies, play a vital role in facilitating communication between cells within the tumor microenvironment (TME). They have a significant impact on cellular behaviors and contribute to genetic and epigenetic changes. This paper analyzes the mutually beneficial association between CSCs and EVs, emphasizing their role in promoting tumor spread and developing resistance mechanisms. This review aims to investigate the interaction between these entities in order to discover new approaches for attacking the complex machinery of cancer cells. It highlights the significance of CSCs and EVs as crucial targets in the advancement of novel cancer treatments, which helps stimulate additional research, promote progress in ideas for cancer treatment, and provide renewed optimism in the effort to reduce the burden of cancer.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer

Introduction

The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers.

Methods

We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions.

Results

Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95).

Discussion

In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

Multi-omics technologies and molecular biomarkers in brain tumor-related epilepsy

BACKGROUND

Brain tumors are one of the leading causes of epilepsy, and brain tumor-related epilepsy (BTRE) is recognized as the major cause of intractable epilepsy, resulting in huge treatment cost and burden to patients, their families, and society. Although optimal treatment regimens are available, the majority of patients with BTRE show poor resolution of symptoms. BTRE has a very complex and multifactorial etiology, which includes several influencing factors such as genetic and molecular biomarkers. Advances in multi-omics technologies have enabled to elucidate the pathophysiological mechanisms and related biomarkers of BTRE. Here, we reviewed multi-omics technology-based research studies on BTRE published in the last few decades and discussed the present status, development, opportunities, challenges, and prospects in treating BTRE.

METHODS

First, we provided a general review of epilepsy, BTRE, and multi-omics techniques. Next, we described the specific multi-omics (including genomics, transcriptomics, epigenomics, proteomics, and metabolomics) techniques and related molecular biomarkers for BTRE. We then presented the associated pathogenetic mechanisms of BTRE. Finally, we discussed the development and application of novel omics techniques for diagnosing and treating BTRE.

RESULTS

Genomics studies have shown that the BRAF gene plays a role in BTRE development. Furthermore, the BRAF V600E variant was found to induce epileptogenesis in the neuronal cell lineage and tumorigenesis in the glial cell lineage. Several genomics studies have linked IDH variants with glioma-related epilepsy, and the overproduction of D2HG is considered to play a role in neuronal excitation that leads to seizure occurrence. The high expression level of Forkhead Box O4 (FOXO4) was associated with a reduced risk of epilepsy occurrence. In transcriptomics studies, VLGR1 was noted as a biomarker of epileptic onset in patients. Several miRNAs such as miR-128 and miRNA-196b participate in BTRE development. miR-128 might be negatively associated with the possibility of tumor-related epilepsy development. The lncRNA UBE2R2-AS1 inhibits the growth and invasion of glioma cells and promotes apoptosis. Quantitative proteomics has been used to determine dynamic changes of protein acetylation in epileptic and non-epileptic gliomas. In another proteomics study, a high expression of AQP-4 was detected in the brain of GBM patients with seizures. By using quantitative RT-PCR and immunohistochemistry assay, a study revealed that patients with astrocytomas and oligoastrocytomas showed high BCL2A1 expression and poor seizure control. By performing immunohistochemistry, several studies have reported the relationship between D2HG overproduction and seizure occurrence. Ki-67 overexpression in WHO grade II gliomas was found to be associated with poor postoperative seizure control. According to metabolomics research, the PI3K/AKT/mTOR pathway is associated with the development of glioma-related epileptogenesis. Another metabolomics study found that SV2A, P-gb, and CAD65/67 have the potential to function as biomarkers for BTRE.

CONCLUSIONS

Based on the synthesized information, this review provided new research perspectives and insights into the early diagnosis, etiological factors, and personalized treatment of BTRE.

DNA Damage Responses, the Trump Card of Stem Cells in the Survival Game

Stem cells, as a group of undifferentiated cells, are enriched with self-renewal and high proliferative capacity, which have attracted the attention of many researchers as a promising approach in the treatment of many diseases over the past years. However, from the cellular and molecular point of view, the DNA repair system is one of the biggest challenges in achieving therapeutic goals through stem cell technology. DNA repair mechanisms are an advantage for stem cells that are constantly multiplying to deal with various types of DNA damage. However, this mechanism can be considered a trump card in the game of cell survival and treatment resistance in cancer stem cells, which can hinder the curability of various types of cancer. Therefore, getting a deep insight into the DNA repair system can bring researchers one step closer to achieving major therapeutic goals. The remarkable thing about the DNA repair system is that this system is not only under the control of genetic factors, but also under the control of epigenetic factors. Therefore, it is necessary to investigate the role of the DNA repair system in maintaining the survival of cancer stem cells from both aspects.

Analysis of the Inhibitory Effect of hsa-miR-145-5p and hsa-miR-203a-5p on Imatinib-Resistant K562 Cells by GC/MS Metabolomics Method

Imatinib (IM) resistance is considered to be a significant challenge in the management of chronic myeloid leukemia (CML). Previous studies have reported that hsa-miR-145-5p and hsa-miR-203a-5p can overcome IM resistance and hsa-miR-203a-5p can alter glutathione metabolism in IM-resistant cells. The purpose of this study was to examine whether hsa-miR-145-5p or hsa-miR-203a-5p counters IM resistance by targeting the overall metabolic profile of IM-resistant K562 cells. The metablic profiling of cell lysates obtained from IM-sensitive, IM-resistant, and miR-transfected IM-resistant K562 cells was carried out using the GC-MS technique. Overall, 75 major metabolites were detected, of which 32 were present in all samples. The pathway analysis of MetaboAnalyst 5.0 revealed that the majorly enriched pathways included glucose metabolism, fatty acid biosynthesis, lipogenesis, and nucleotide metabolism. Eleven of identified metabolites, l-glutamine, l-glutamic acid, l-lactic acid, phosphoric acid, 9,12-octadecadienoic acid, 9-octadecenoic acid, myristic acid, palmitic acid, cholesterol, and β-alanine, appeared in enriched pathways. IM-resistant cells had comparatively higher concentrations of all of these metabolites. Notably, the introduction of hsa-miR-145-5p or hsa-miR-203a-5p into resistant cells resulted in a decrease in levels of these metabolites. The efficacy of miR-203a-5p was particularly remarkable in comparison with miR-145-5p, as evidenced by partial least-squares-discriminant analysis (PLS-DA), which showed a high level of similarity in metabolic profile between IM-sensitive K562 cells and IM-resistant cells transfected with hsa-miR-203a-5p. The results indicate that GC-MS-based metabolic profiling has the potential to distinguish between drug-resistant and -sensitive cells. This approach can also be used to routinely monitor therapeutic response in drug-resistant patients, thus, enabling personalized therapy.

Plasma metabolomics identifies metabolic alterations associated with the growth and development of cat

BACKGROUND

The purpose of our study was to study the composition and content of the feline plasma metabolome revealing the critical metabolites and metabolic pathways associated with age during growth and development.

METHODS

Blood samples were collected from juvenile and adult groups for blood routine tests and serum biochemistry tests. Non-targeted metabolomics analyses of plasma were also performed to investigate changes in metabolites and metabolic pathways.

RESULTS

In this study, we found that the red blood cell counts, liver function indexes (albumin and gamma-glutamyl transpeptidase), and the concentration of triglyceride and glucose changed significant with growth and development. The metabolomics results revealed that 1427 metabolites were identified in the plasma of young and adult cats. Most of these metabolites belong to major classes of lipids and lipid-like molecules. The most obvious age-related metabolites include reduced levels of chenodeoxycholate, taurocholate, cholate, and taurochenodeoxycholate but increased levels of L-cysteine and taurocyamine in the adult cat's serum. These metabolites are mainly involved in the primary bile acid biosynthesis pathway, the bile secretion pathway, and the taurine and hypotaurine metabolism pathway.

CONCLUSION

This study revealed many age-related metabolite alterations in the feline plasma. These age-varying metabolites, especially in the bile acid biosynthesis and secretion metabolism pathways, indicate that the regulation of these pathways is involved in the growth and development of cats. This study promotes our understanding of the mechanism of feline growth and provides new insights into nutrition and medicine for cats of different ages.

Combination of metabolomics and network pharmacology analysis to decipher the mechanisms of total flavonoids of Litchi seed against prostate cancer

OBJECTIVES

To explore the underlying mechanism of total flavonoids of Litchi seed (TFLS) in treating prostate cancer (PCa).

METHODS

Cell Counting Kit-8 (CCK-8), EdU incorporation assay, trypan blue dye assay and colony formation assay were employed to evaluate the effect of TFLS on PCa in vitro. The xenograft mouse model was established to explore the anti-tumour effect of TFLS in vivo. Alterations in the metabolic profiles of the PC3 cells and mouse serum were obtained by untargeted metabolomics. Combination with metabolomics analysis and network pharmacology strategies, the potential targets were predicted and further validated by RT-qPCR.

KEY FINDINGS

TFLS attenuated PCa progression both in vitro and in vivo. Metabolomics results yielded from cells and serum indicated that the anti-cancer effect of TFLS was correlated with synergistic modulation of five common metabolic pathways including glycerophospholipid metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism, tryptophan metabolism and steroid biosynthesis. Using in silico prediction and RT-qPCR analysis, we further revealed that TFLS exerted anti-PCa activities via regulating the expressions of nine genes, including MAOA, ACHE, ALDH2, AMD1, ARG1, PLA2G10, PLA2G1B, FDFT1 and SQLE.

CONCLUSIONS

TFLS suppressed tumour proliferation in PCa, which may be associated with regulating lipid and amino acid metabolisms.

Advancement of Organoid Technology in Regenerative Medicine

Purpose

Organoids are three-dimensional cultures of stem cells in an environment similar to the body's extracellular matrix. This is also a novel development in the realm of regenerative medicine. Stem cells can begin to develop into 3D structures by modifying signaling pathways. To form organoids, stem cells are transplanted into the extracellular matrix. Organoids have provided the required technologies to reproduce human tissues. As a result, it might be used in place of animal models in scientific study. The key goals of these investigations are research into viral and genetic illnesses, malignancies, and extracellular vesicles, pharmaceutical discovery, and organ transplantation. Organoids can help pave the road for precision medicine through genetic editing, pharmaceutical development, and cell therapy.

Methods

PubMed, Google Scholar, and Scopus were used to search for all relevant papers written in English (1907-2021). The study abstracts were scrutinized. Studies on the use of stem-cell-derived organoids in regenerative medicine, organoids as 3D culture models for EVs analysis, and organoids for precision medicine were included. Articles with other irrelevant aims, meetings, letters, commentaries, congress and conference abstracts, and articles with no available full texts were excluded.

Results

According to the included studies, organoids have various origins, types, and applications in regenerative and precision medicine, as well as an important role in studying extracellular vesicles.

Conclusion

Organoids are considered a bridge that connects preclinical studies to clinical ones. However, the lack of a standardized protocol and other barriers addressed in this review, hinder the vast use of this technology.

Lay Summary

Organoids are 3D stem cell propagations in biological or synthetic scaffolds that mimic ECM to allow intercellular or matrix-cellular crosstalk. Because these structures are similar to organs in the body, they can be used as research models. Organoids are medicine's future hope for organ transplantation, tumor biobank formation, and the development of precision medicine. Organoid models can be used to study cell-to-cell interactions as well as effective factors like inflammation and aging. Bioengineering technologies are also used to define the size, shape, and composition of organoids before transforming them into precise structures. Finally, the importance of organoid applications in regenerative medicine has opened a new window for a better understanding of biological research, as discussed in this study.

Comprehensive serum metabolomics and network analysis to reveal the mechanism of gypenosides in treating lung cancer and enhancing the pharmacological effects of cisplatin

Gypenosides (GYP) exerted anticancer activity against various cancers. However, the mechanism of GYP against lung cancer (LC) in vivo remains unclear. This study aims to reveal the potential mechanism of GYP against LC and enhancing cisplatin efficacy using a comprehensive analysis of metabolomics, network analysis. Pharmacodynamic results showed that GYP inhibited tumor growth, reduced tumor volume and tumor weight, and alleviated pathological symptoms in Lewis tumor-bearing mice, and GYP could enhance the anti-LC effects of cisplatin. Using serum metabolomics methods, 53 metabolites were found to be significantly altered in the model group, and the levels of 23 biomarkers were significantly restored after GYP treatment. GYP-related metabolic pathways involved six pathways, including alpha-linolenic acid metabolism, glutathione metabolism, sphingolipid metabolism, glycerophospholipid metabolism, tryptophan metabolism, and primary bile acid biosynthesis. 57 genes associated with differential metabolites of GYP recovery and 7 genes of 11 saponins of GYP against LC were screened by network analysis, the STRING database was used to find the association between 57 genes and 7 genes, and a compound-intersection gene-metabolite related gene-metabolite-pathway network was constructed, and STAT3, MAPK14, EGFR and TYMS might be the crucial targets of GYP against LC. Western blot results showed that GYP restored the levels of STA3, MAPK14, EGFR, and TYMS in the model group, and GYP also restored the levels of STAT3 and MAPK14 in the cisplatin group, indicating that GYP might exert anti-LC effects and enhance the pharmacological effects of cisplatin through MAPK14/STAT3 signaling pathway. Our method revealed the effect and mechanism of GYP on LC and the pharmacological effects of GYP-enhanced chemotherapeutic agent cisplatin, which provided some reference for the development of anti-cancer drugs.

Marsdenia tenacissima (Roxb.) Moon injection exerts a potential anti-tumor effect in prostate cancer through inhibiting ErbB2-GSK3β-HIF1α signaling axis

ETHNOPHARMACOLOGICAL RELEVANCE

Marsdenia tenacissima injection (MTE), a traditional Chinese medical injection extracted from the rattan of Marsdenia tenacissima (Roxb.) Moon, has been approved for clinical use in China as an adjuvant therapeutic agent in multiple cancers, including esophageal cancer, gastric cancer, lung cancer, and liver cancer. However, the activity and mechanism of MTE on prostate cancer (PCa) remain to be defined.

AIM OF THE STUDY

To investigate the activity and the underlying mechanism of MTE in the treatment of PCa.

MATERIALS AND METHODS

The component characterization of MTE was analyzed by HPLC-CAD-QTOF-MS/MS technology. Cell Counting Kit-8 (CCK-8) assay was used to assess PCa cell proliferation. Colony formation assay was applied to detect the clonogenic ability of the cells. MetaboAnalyst5.0 database was employed to analyze the altered metabolites of PC3 cells treated with MTE obtained by UPLC-QTOF-MS/MS. Combined with metabolomics analysis and network pharmacology, we predicted the potential targets, which further were verified by Western Blot, RT-qPCR, and Immunohistochemistry assays. Finally, SeeSAR software was applied to predict the potential active components of MTE against PCa.

RESULTS

A total of 21 components in MTE were confirmed by HPLC-CAD-QTOF-MS/MS analysis. MTE inhibited the proliferation and colony formation of PCa cells. A total of 20 metabolites closely related to glycerophospholipid metabolism, glycolysis/gluconeogenesis, and tricarboxylic acid (TCA) cycle were significantly changed in PC3 cells treated with MTE. The network pharmacology analysis revealed that MTE suppressed the growth of PC3 cells might by regulating the ErbB2-GSK3β-HIF1α signaling axis. Furthermore, we also confirmed that stimulation of MTE significantly inhibited the phosphorylation of ErbB2 at Tyr877 and the activities of its downstream signal transducers (GSK3β and HIF1α) in PCa, as well as the mRNA levels of critical factors (IDH2, LDHA, and HIF1A) in the tricarboxylic acid (TCA) cycle. Molecular docking further suggested that Tenacissimoside E, cryptochlorogenic acid, and scopoletin might be the active ingredients of MTE for PCa treatment.

CONCLUSION

This study proposed that MTE exerts a potential anti-tumor effect in PCa through inhibiting ErbB2-GSK3β-HIF1α signaling axis, which may be related to the TCA cycle.

Molecular Docking as a Therapeutic Approach for Targeting Cancer Stem Cell Metabolic Processes

Cancer stem cells (CSCs) are subpopulation of cells which have been demonstrated in a variety of cancer models and involved in cancer initiation, progression, and development. Indeed, CSCs which seem to form a small percentage of tumor cells, display resembling characteristics to natural stem cells such as self-renewal, survival, differentiation, proliferation, and quiescence. Moreover, they have some characteristics that eventually can demonstrate the heterogeneity of cancer cells and tumor progression. On the other hand, another aspect of CSCs that has been recognized as a central concern facing cancer patients is resistance to mainstays of cancer treatment such as chemotherapy and radiation. Owing to these details and the stated stemness capabilities, these immature progenitors of cancerous cells can constantly persist after different therapies and cause tumor regrowth or metastasis. Further, in both normal development and malignancy, cellular metabolism and stemness are intricately linked and CSCs dominant metabolic phenotype changes across tumor entities, patients, and tumor subclones. Hence, CSCs can be determined as one of the factors that correlate to the failure of common therapeutic approaches in cancer treatment. In this context, researchers are searching out new alternative or complementary therapies such as targeted methods to fight against cancer. Molecular docking is one of the computational modeling methods that has a new promise in cancer cell targeting through drug designing and discovering programs. In a simple definition, molecular docking methods are used to determine the metabolic interaction between two molecules and find the best orientation of a ligand to its molecular target with minimal free energy in the formation of a stable complex. As a comprehensive approach, this computational drug design method can be thought more cost-effective and time-saving compare to other conventional methods in cancer treatment. In addition, increasing productivity and quality in pharmaceutical research can be another advantage of this molecular modeling method. Therefore, in recent years, it can be concluded that molecular docking can be considered as one of the novel strategies at the forefront of the cancer battle via targeting cancer stem cell metabolic processes.

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Potential Metabolite Biomarkers for Early Detection of Stage-I Pancreatic Ductal Adenocarcinoma

Background & Objectives

Pancreatic ductal adenocarcinoma remains an extremely malignant tumor having a poor prognosis. The 5-year survival rate of PDAC is related to its stage (about 80% for stage I vs 20% for other stages). However, detection of PDAC in an early stage is difficult due to the lack of effective screening methods. In this study, we aimed to construct a novel metabolic model for stage-I PDAC detection, using both serum and tissue samples.

Methods

We employed an untargeted technique, UHPLC-Q-TOF-MS, to identify the potential metabolite, and then used a targeted technique, GC-TOF-MS, to quantitatively validate. Multivariate and univariate statistics were performed to analyze the metabolomic profiles between stage-I PDAC and healthy controls, including 90 serum and 53 tissue samples. 28 patients with stage-I PDAC and 62 healthy controls were included in this study.

Results

A total of 10 potential metabolites presented the same expression levels both in serum and in tissue. Among them, a 2-metabolites-model (isoleucine and adrenic acid) for stage-I PDAC was constructed. The area under the curve (AUC) value was 0.93 in the discovery set and 0.90 in the independent validation set. Especially, the serum metabolite model had a better diagnostic performance than CA19-9 (AUC = 0.79). Pathway analysis revealed 11 altered pathways in both serum and tissue of stage-I PDAC.

Conclusions

This study developed a novel serum metabolites model that could early separate stage-I PDAC from healthy controls.

NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.

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NMR analysis of various bio-samples can explore disease course and vaccine interaction.

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Immunogenicity and reactogenicity caused by COVID-19 vaccination can be studied by NMR.

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Vaccine interaction alters metabolic pathway(s) at a certain stage, and this mechanism can be probed at the metabolic level.

Metabolomics Signatures of SARS-CoV-2 Infection

For a very long time, viral infections have been considered as one of the most important causes of death and disability around the world. Through the viral infection, viruses as small pathogens enter the host cells and use hosts' biosynthesis machinery to replicate and collect infectious lineages. Moreover, they can modify hosts' metabolic pathways in order to their own purposes. Nowadays (in 2019-2020), the most famous type of viral infection which was caused by a novel type of coronavirus is called COVID-19 disease. It has claimed the lives of many people around the world and is a very serious threat to health. Since investigations of the effects of viruses on host metabolism using metabolomics tools may have given focuses on novel appropriate treatments, in the current review the authors highlighted the virus-host metabolic interactions and metabolomics perspective in COVID-19.

Identification of Novel Serum Metabolic Biomarkers as Indicators in the Progression of Intravenous Leiomyomatosis: A High Performance Liquid Chromatography-Tandem Mass Spectrometry-Based Study

Background

Intravenous leiomyomatosis (IVL) is a rare estrogen-dependent neoplasm. However, identifiable and reliable biomarkers are still not available for clinical application, especially for the diagnosis and prognosis of the disease.

Methods

In the present study, 30 patients with IVL and 30 healthy controls were recruited. Serum samples were isolated from these participants for further high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis to study metabolomics alterations and identify differentially expressed metabolites based on orthogonal partial least-squares discriminant analysis (OPLS-DA). Subsequently, lasso regression analysis and a generalized linear regression model were applied to screen out hub metabolites associated with the progression of IVL.

Results

First, 16 metabolites in the positive ion mode were determined from the 240 identifiable metabolites at the superclass level, with ten metabolites upregulated in the IVL group and the remaining six metabolites downregulated. Our data further proved that four metabolites [hypoxanthine, acetylcarnitine, glycerophosphocholine, and hydrocortisone (cortisol)] were closely related to the oncogenesis of IVL. Hypoxanthine and glycerophosphocholine might function as protective factors in the development of IVL (OR = 0.19 or 0.02, respectively). Nevertheless, acetylcarnitine and hydrocortisone (cortisol), especially the former, might serve as risk indicators for the disease to promote the development or recurrence of IVL (OR = 18.16 or 2.10, respectively). The predictive accuracy of these hub metabolites was further validated by the multi-class receiver operator characteristic curve analysis (ROC) with the Scikit-learn algorithms.

Conclusion

Four hub metabolites were finally determined via comprehensive bioinformatics analysis, and these substances could potentially serve as novel biomarkers in predicting the prognosis or progression of IVL.

Valproic Acid and Breast Cancer: State of the Art in 2021

Valproic acid (2-propylpentanoic acid, VPA) is a short-chain fatty acid, a member of the group of histone deacetylase inhibitors (HDIs). VPA has been successfully used in the treatment of epilepsy, bipolar disorders, and schizophrenia for over 50 years. Numerous in vitro and in vivo pre-clinical studies suggest that this well-known anticonvulsant drug significantly inhibits cancer cell proliferation by modulating multiple signaling pathways. Breast cancer (BC) is the most common malignancy affecting women worldwide. Despite significant progress in the treatment of BC, serious adverse effects, high toxicity to normal cells, and the occurrence of multi-drug resistance (MDR) still limit the effective therapy of BC patients. Thus, new agents which improve the effectiveness of currently used methods, decrease the emergence of MDR, and increase disease-free survival are highly needed. This review focuses on in vitro and in vivo experimental data on VPA, applied individually or in combination with other anti-cancer agents, in the treatment of different histological subtypes of BC.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Bioinformatic analysis identifying FGF1 gene as a new prognostic indicator in clear cell Renal Cell Carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current clinical treatment situation is still challenging considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease METHODS: Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein-protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function of the gene and signaling mechanism of gene regulating ccRCC development was preliminary explored.

RESULTS

Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed with ccRCC tissue microarray which contained 104 local samples and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan-Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data.

CONCLUSIONS

Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which statistical significantly correlated with patients overall survival, and the gene's clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use.

Druggable genome and precision medicine in cancer: current challenges

The past decades have seen tremendous developments with respect to "specific" therapeutics that target key signaling molecules to conquer cancer. The key advancements with multiomics technologies, especially genomics, have allowed physicians and molecular oncologists to design "tailor-made" solutions to the specific oncogenes that are deregulated in individual patients, a strategy which has turned out to be successful though the patients quickly develop resistance. The swift integration of multidisciplinary approaches has led to the development of "next generation" therapeutics and, with synergistic therapeutic regimes combined with immune checkpoint inhibitors to reactivate the dampened immune response, has provided the much-needed promise for cancer patients. Despite these advances, a large portion of the druggable genome remains understudied, and the role of druggable genome in the immune system needs further attention. Establishment of patient-derived organoid models has fastened the preclinical validation of novel therapeutics for swift clinical translation. We summarized the current advances and challenges and also stress the importance of biobanking and collection of longitudinal data sets with structured clinical information, as well as the critical role these "high content data sets" will play in designing new therapeutic regimes in a tailor-made fashion.

Robust Data Integration Method for Classification of Biomedical Data

We present a protocol for integrating two types of biological data - clinical and molecular - for more effective classification of patients with cancer. The proposed approach is a hybrid between early and late data integration strategy. In this hybrid protocol, the set of informative clinical features is extended by the classification results based on molecular data sets. The results are then treated as new synthetic variables. The hybrid protocol was applied to METABRIC breast cancer samples and TCGA urothelial bladder carcinoma samples. Various data types were used for clinical endpoint prediction: clinical data, gene expression, somatic copy number aberrations, RNA-Seq, methylation, and reverse phase protein array. The performance of the hybrid data integration was evaluated with a repeated cross validation procedure and compared with other methods of data integration: early integration and late integration via super learning. The hybrid method gave similar results to those obtained by the best of the tested variants of super learning. What is more, the hybrid method allowed for further sensitivity analysis and recursive feature elimination, which led to compact predictive models for cancer clinical endpoints. For breast cancer, the final model consists of eight clinical variables and two synthetic features obtained from molecular data. For urothelial bladder carcinoma, only two clinical features and one synthetic variable were necessary to build the best predictive model. We have shown that the inclusion of the synthetic variables based on the RNA expression levels and copy number alterations can lead to improved quality of prognostic tests. Thus, it should be considered for inclusion in wider medical practice.

Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS

This study examined metabolite profile differences between serum samples of thyroid papillary carcinoma (PTC) patients and healthy controls, aiming to identify candidate biomarkers and pathogenesis pathways in this cancer type. Serum samples were collected from PTC patients (n = 80) and healthy controls (n = 80). Using principal component analysis (PCA), partial least squares discrimination analysis(PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), t-tests, and the volcano plot, a model of abnormal metabolic pathways in PTC was constructed. PCA, PLS-DA, and OPLS-DA analysis revealed differences in serum metabolic profiles between the PTC and control group. OPLS-Loading plot analysis, combined with Variable importance in the projection (VIP)>1, Fold change (FC) > 1.5, and p < 0.05 were used to screen 64 candidate metabolites. Among them, 22 metabolites, including proline betaine, taurocholic acid, L-phenylalanine, retinyl beta-glucuronide, alpha-tocotrienol, and threonine acid were upregulated in the PTC group; meanwhile, L-tyrosine, L-tryptophan, 2-arachidonylglycerol, citric acid, and other 42 metabolites were downregulated in this group. There were eight abnormal metabolic pathways related to the differential metabolites, which may be involved in the pathophysiology of PTC. Six metabolites yielded an area under the receiver operating curve of >0.75, specifically, 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanylphenylalanine, and l-glutamic acid. The Warburg effect was observed in PTC. The levels of 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanine, and L-glutamic acid may help distinguish PTC patients from healthy controls. Aspartic acid metabolism, glutamic acid metabolism, urea cycle, and tricarboxylic acid cycle are involved in the mechanism of PTC.

Salivary metabolomics for cancer detection

Introduction: Saliva is an ideal biofluid that can be collected in a noninvasive manner, enabling safe and frequent screening of various diseases. Recent studies have revealed that salivary metabolomics analysis has the potential to detect both oral and systemic cancers. Area covered: We reviewed the technical aspects, as well as applications, of salivary metabolomics for cancer detection. The topics include the effects of preconditioning and the method of sample collection, sample storage, processing, measurement, data analysis, and validation of the results. We also examined the rational relationship between salivary biomarkers and tumors distant from the oral cavity. A strategy to establish standard operating protocols for obtaining reproducible quantification data is also discussed Expert opinion: Salivary metabolomics reflects oral and systematic health status, which potently enables cancer detection. The sensitivity and specificity of each marker and their combinations have been well evaluated, but a validation study is required. Further, the standard operating protocol for each procedure should be established to obtain reproducible data before clinical usage.

Omics research in vascular calcification

Vascular calcification (VC), the pathological process of hydroxyapatite mineral deposition in the vascular system, is closely associated with aging, atherosclerotic plaque formation, cardiovascular disease (CVD) and diabetes mellitus (DM). Studies have shown that VC is related to cellular phenotypic changes, extracellular vesicles, disordered calcium and phosphate homeostasis, and an imbalance between inducers and inhibitors of VC. Unfortunately, there is currently no effective preventive or targeted treatment for pathologic condition. The rapid evolution of omics technology (genomics, epigenomics, transcriptomics, proteomics and metabolomics) has provided a novel approach for elucidation of pathophysiologic mechanisms in general and those associated with VC specifically. Here, we review articles published over the last twenty years and focus on the current state, challenges, limitations and future of omics in VC research and clinical practice. Highlighting potential targets based on omics technology will improve our understanding of this pathologic condition and assist in the development of potential treatment options for VC related disease.

Omics research in vascular calcification

Vascular calcification (VC), the pathological process of hydroxyapatite mineral deposition in the vascular system, is closely associated with aging, atherosclerotic plaque formation, cardiovascular disease (CVD) and diabetes mellitus (DM). Studies have shown that VC is related to cellular phenotypic changes, extracellular vesicles, disordered calcium phosphate homeostasis and an imbalance between inducers and inhibitors of VC. Unfortunately, there is currently no effective preventive or targeted treatment for this disorder. Recently, the evolution of omics technology (genomics, epigenomics, transcriptomics, proteomics and metabolomics) has paved the way for elucidation of complex biochemical processes and, as such, may provide new insight on VC. Accordingly, we conducted a review of articles published over the last twenty years and herein focus on current and future potential of omics technology in clarifying mechanisms of this disease process. Identification of new biomarkers will provide additional tools in characterizing this pathology and will further assist in the development of potential therapeutic targets.