Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

Identification of circulating metabolites linked to the risk of breast cancer: a mendelian randomization study

Objective

This study aimed to investigate potential causal relationships between circulating metabolites and breast cancer risk using Mendelian randomization (MR) analysis.

Materials and Methods

Summary-level genome-wide association study (GWAS) datasets for 249 circulating metabolites were obtained from the UK Biobank. GWAS datasets for estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancer were acquired from previous studies based on the Combined Oncoarray. Instrumental variables (IVs) were selected from single nucleotide polymorphisms (SNPs) associated with circulating metabolites, and MR analyses were conducted using the inverse-variance weighted (IVW) method as the primary analysis, with additional sensitivity analyses using other MR methods. Odds ratios (OR) and 95% confidence interval (CI) were used to estimate the association of circulating metabolites with breast cancer risk.

Results

The IVW analysis revealed significant causal relationships between 79 circulating metabolites and ER + breast cancer risk, and 10 metabolites were significantly associated with ER-breast cancer risk. Notably, acetate (OR = 1.12, P = 0.03), HDL cholesterol (OR = 1.09, P < 0.001), ration of omega-6 fatty acids to total fatty acids ratio (OR = 1.09, P = 0.01), and phospholipids in large LDL (OR = 1.09, P < 0.001) were linked to an increased risk of ER + breast cancer, while linoleic acid (OR = 0.91, P < 0.001) monounsaturated fatty acids (OR = 0.91, P < 0.001), and total lipids in LDL (OR = 0.91, P < 0.001) were associated with a decreased risk. In ER-breast cancer, glycine, citrate, HDL cholesterol, cholesteryl esters in HDL, cholesterol to total lipids ratio in very large HDL, and cholesterol in large LDL were associated with an increased risk, while the free cholesterol to total lipids in very large HDL was linked to a decreased risk.

Conclusion

This MR approach underscores aberrant lipid metabolism as a key process in breast tumorigenesis, and may inform future prevention and treatment strategies. To further elucidate the underlying mechanisms and explore the potential clinical implications, additional research is warranted to validate the observed associations in this study.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

A Multiomics, Molecular Atlas of Breast Cancer Survivors

Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.

A critical appraisal of the role of metabolomics in breast cancer research and diagnostics

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.

Serum Carbohydrate Antigen 19-9 and Metabolite Hypotaurine Are Predictive Markers for Early Recurrence of Pancreatic Ductal Adenocarcinoma

OBJECTIVE

A significant number of patients experience early recurrence after surgical resection for pancreatic ductal adenocarcinoma (PDAC), negating the benefit of surgery. The present study conducted clinicopathologic and metabolomic analyses to explore the factors associated with the early recurrence of PDAC.

MATERIALS AND METHODS

Patients who underwent pancreatectomy for PDAC at Kagawa University Hospital between 2011 and 2020 were enrolled. Tissue samples of PDAC and nonneoplastic pancreas were collected and frozen immediately after resection. Charged metabolites were quantified by capillary electrophoresis-mass spectrometry. Patients who relapsed within 1 year were defined as the early recurrence group.

RESULTS

Frozen tumor tissue and nonneoplastic pancreas were collected from 79 patients. The clinicopathologic analysis identified 11 predictive factors, including preoperative carbohydrate antigen 19-9 levels. The metabolomic analysis revealed that only hypotaurine was a significant risk factor for early recurrence. A multivariate analysis, including clinical and metabolic factors, showed that carbohydrate antigen 19-9 and hypotaurine were independent risk factors for early recurrence ( P = 0.045 and P = 0.049, respectively). The recurrence-free survival rate 1 year after surgery with both risk factors was only 25%.

CONCLUSIONS

Our results suggested that tumor hypotaurine is a potential metabolite associated with early recurrence. Carbohydrate antigen 19-9 and hypotaurine showed a vital utility for predicting early recurrence.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Spatholobus suberectus inhibits lipogenesis and tumorigenesis in triple-negative breast cancer via activation of AMPK-ACC and K-Ras-ERK signaling pathway

Background and aim

Triple-negative breast cancer (TNBC) is a highly invasive type of breast cancer with a poor prognosis. Currently, there are no effective management strategies for TNBC. Earlier, our lab reported the percolation of Spatholobus suberectus for the treatment of breast cancer. Lipid metabolic reprogramming is a hallmark of cancer. However, the anti-TNBC efficiency of S. suberectus extract and its causal mechanism for preventing lipogenesis have not been fully recognized. Hence, the present study aimed to investigate the inhibitory role of S. suberectus extract on lipogenesis and tumorigenesis in TNBC in vitro and in vivo by activating AMPK-ACC and K-Ras-ERK signaling pathways using lipidomic and metabolomic techniques.

Experimental procedure

Dried stems of S. suberectus extract inhibited lipogenesis and tumorigenesis and promoted fatty acid oxidation as demonstrated by the identification of the metabolites and fatty acid markers using proteomic and metabolomic analysis, qPCR, and Western blot.

Results and conclusion

The results indicated that S. suberectus extract promotes fatty acid oxidation and suppresses lipogenic metabolites and biomarkers, thereby preventing tumorigenesis via the AMPK-ACC and K-Ras-ERK signaling pathways. On the basis of this preclinical evidence, we suggest that this study represents a milestone and complements Chinese medicine. Further studies remain underway in our laboratory to elucidate the active principles of S. suberectus extract. This study suggests that S. suberectus extract could be a promising therapy for TNBC.

The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review

This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Common alterations in plasma free amino acid profiles and gut microbiota-derived tryptophan metabolites of five types of cancer patients

Amino acids not only play a vital role in the synthesis of biological molecules such as proteins in cancer malignant cells, they are also essential metabolites for immune cell activation and antitumor effects in the tumor microenvironment. The abnormal changes in amino acid metabolism are closely related to the occurrence and development of tumors and immunity. Intestinal microorganisms play an essential role in amino acid metabolism, and tryptophan and its intestinal microbial metabolites are typical representatives. However, it is known that the cyclic amino acid profile is affected by specific cancer types, so relevant studies mainly focus on one type of cancer and rarely study different cancer forms at the same time. The objective of this study was to examine the PFAA profile of five cancer patients and the characteristics of tryptophan intestinal microbial metabolites to determine whether there are general amino acid changes across tumors. Plasma samples were collected from esophageal (n = 53), lung (n = 73), colorectal (n = 94), gastric (n = 55), breast cancer (n = 25), and healthy control (HC) (n = 139) subjects. PFAA profile and tryptophan metabolites were measured, and their perioperative changes were examined using high-performance liquid chromatography. Univariate analysis revealed significant differences between cancer patients and HC. Furthermore, multivariate analysis discriminated cancer patients from HC. Regression diagnosis models were established for each cancer group using differential amino acids from univariate analysis. Receiver-operating characteristic analysis was applied to evaluate these diagnosis models. Finally, GABA, arginine, tryptophan, taurine, glutamic acid, and melatonin showed common alterations across all types of cancer patients. Metabolic pathway analysis shows that the most significant enrichment pathways were tryptophan, arginine, and proline metabolism. This study provides evidence that common alterations of the metabolites mentioned above suggest their role in the pathogenesis of each cancer patient. It was suggested that multivariate models based on PFAA profiles and tryptophan metabolites might be applicable in the screening of cancer patients.

Overexpression of C19orf48 correlates with poor prognosis in breast cancer

As one of the most commonly diagnosed cancers in women around the world, breast cancer has been detailed studied. This study aimed to identify the expression of c19orf48 in several kinds of cancers including liver, lung and breast cancers etc. The driving factors behind it were analysed and it found that the amplification of c19orf48 may relate with the elevated expression. At the same time, the correlation between the expression of it and the survival time in breast cancer patients was explored. It was found that the c19orf48 expression at transcriptional level elevated in breast cancer tissue samples compared with the normal. It was inferred that the c19orf48 play its oncogenic role in development of breast cancer by involving in cell-cycle related biological process. In conclusion, c19orf48 may be a useful and predictive biomarker for the prognosis of breast cancer patients. To the best of our knowledge, this is the first report describing the expression of c19orf48, the potential driving factor led to this and its effect.

Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolite analyses in 10 CRC (TCRC) and normal paired tissues (THC) along with 10 matched fecal samples (FCRC) and 10 healthy controls (FHC). The highest microbial phyla abundance from THC and TCRC were Firmicutes, while the dominant phyla from FHC and FCRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in THC than in TCRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in FHC than in FCRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between THC and TCRC groups and 700 metabolites different between FHC and FCRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning

We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.

Multi-Omics Analysis of Transcriptomic and Metabolomics Profiles Reveal the Molecular Regulatory Network of Marbling in Early Castrated Holstein Steers

The intramuscular fat (IMF), or so-called marbling, is known as potential determinant of the high quality beef in China, Korea, and Japan. Of the methods that affect IMF content in cattle, castration is markedly regarded as an effective and economical way to improve the deposition of IMF but with little attention to its multi-omics in early-castrated cattle. The aim of this study was to investigate the liver transcriptome and metabolome of early-castrated Holstein cattle and conduct a comprehensive analysis of two omics associated with the IMF deposition using transcriptomics and untargeted metabolomics under different treatments: non−castrated and slaughtered at 16 months of age (GL16), castrated at birth and slaughtered at 16 months of age (YL16), and castrated at birth and slaughtered at 26 months of age (YL26). The untargeted metabolome was analyzed using ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The transcriptome of the hepatic genes was analyzed to identify marbling-related genes. Using untargeted metabolomics, the main altered metabolic pathways in the liver of cattle, including those for lipid and amino acid metabolism, were detected in the YL16 group relative to the GL16 and YL26 groups. Significant increases in the presence of betaine, alanine, and glycerol 3-phosphate were observed in the YL16 group (p < 0.05), which might have contributed to the improved beef-marbling production. Compared to the GL16 and YL26 groups, significant increases in the presence of glutathione, acetylcarnitine, and riboflavin but decreases in diethanolamine and 2-hydroxyglutarate were identified in YL16 group (p < 0.05), which might have been beneficial to the beef’s enhanced functional quality. The gene expressions of GLI1 and NUF2 were downregulated and that of CYP3A4 was upregulated in the YL16 group; these results were strongly correlated with the alanine, betaine, and leucine, respectively, in the liver of the cattle. In conclusion, implementation of early castration modified the hepatic metabolites and the related biological pathways by regulating the relevant gene expressions, which could represent a better rearing method for production of high marbled and healthier beef products.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures

Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics-AI systems, limitations thereof and recent tools were also discussed.

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

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

Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis.

METHODS

This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning.

RESULTS

Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), L-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879).

CONCLUSIONS

This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer.

Metabolomics of Breast Cancer: A Review

Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.

Blood-derived lncRNAs as biomarkers for cancer diagnosis: the Good, the Bad and the Beauty

Cancer ranks as one of the deadliest diseases worldwide. The high mortality rate associated with cancer is partially due to the lack of reliable early detection methods and/or inaccurate diagnostic tools such as certain protein biomarkers. Cell-free nucleic acids (cfNA) such as circulating long noncoding RNAs (lncRNAs) have been proposed as a new class of potential biomarkers for cancer diagnosis. The reported correlation between the presence of tumors and abnormal levels of lncRNAs in the blood of cancer patients has notably triggered a worldwide interest among clinicians and oncologists who have been actively investigating their potentials as reliable cancer biomarkers. In this report, we review the progress achieved (“the Good”) and challenges encountered (“the Bad”) in the development of circulating lncRNAs as potential biomarkers for early cancer diagnosis. We report and discuss the diagnostic performance of more than 50 different circulating lncRNAs and emphasize their numerous potential clinical applications (“the Beauty”) including therapeutic targets and agents, on top of diagnostic and prognostic capabilities. This review also summarizes the best methods of investigation and provides useful guidelines for clinicians and scientists who desire conducting their own clinical studies on circulating lncRNAs in cancer patients via RT-qPCR or Next Generation Sequencing (NGS).

How previous treatment changes the metabolomic profile in patients with metastatic breast cancer

Purpose

Metabolites are in the spotlight of attention as promising novel breast cancer biomarkers. However, no study has been conducted concerning changes in the metabolomics profile of metastatic breast cancer patients according to previous therapy.

Methods

We performed a retrospective, single-center, nonrandomized, partially blinded, treatment-based study. Metastatic breast cancer (MBC) patients were enrolled between 03/2010 and 09/2016 at the beginning of a new systemic therapy. The endogenous metabolites in the plasma samples were analyzed using the AbsoluteIDQ^® p180 Kit (Biocrates Life Sciences AG, Innsbruck) a targeted, quality and quantitative-controlled metabolomics approach. The statistical analysis was performed using R package, version 3.3.1. ANOVA was used to statistically assess age differences within groups. Furthermore, we analyzed the CTC status of the patients using the CellSearch^™ assay.

Results

We included 178 patients in our study. Upon dividing the study population according to therapy before study inclusion, we found the following: 4 patients had received no therapy, 165 chemotherapy, and 135 anti-hormonal therapy, 30 with anti-Her2 therapy and 38 had received treatment with bevacizumab. Two metabolites were found to be significantly different, depending on the further therapy of the patients: methionine and serine. Whereas methionine levels were higher in the blood of patients who received an anti-Her2-therapy, serine was lower in patients with endocrine therapy only.

Conclusion

We identified two metabolites for which concentrations differed significantly depending on previous therapies, which could help to choose the next therapy in patients who have already received numerous different treatments.

SMSPL: Robust Multimodal Approach to Integrative Analysis of Multiomics Data

With the recent advancement of technologies, it is progressively easier to collect diverse types of genome-wide data. It is commonly expected that by analyzing these data in an integrated way, one can improve the understanding of a complex biological system. Current methods, however, are prone to overfitting heavy noise such that their applications are limited. High noise is one of the major challenges for multiomics data integration. This may be the main cause of overfitting and poor performance in generalization. A sample reweighting strategy is typically used to cope with this problem. In this article, we propose a robust multimodal data integration method, called SMSPL, which can simultaneously predict subtypes of cancers and identify potentially significant multiomics signatures. Especially, the proposed method leverages the linkages between different types of data to interactively recommend high-confidence samples, adopts a new soft weighting scheme to assign weights to the training samples of each type, and then iterates between weights recalculating and classifiers updating. Simulation and five real experiments substantiate the capability of the proposed method for classification and identification of significant multiomics signatures with heavy noise. We expect SMSPL to take a small step in the multiomics data integration and help researchers comprehensively understand the biological process.

3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis

MOTIVATION

The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders.

RESULTS

Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world dataset was used to demonstrate its simple and flexible operation, comprehensive outputs and distinctive contribution to dual-omics studies.

AVAILABILITYAND IMPLEMENTATION

3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Plasma Metabolite Signature Classifies Male LRRK2 Parkinson’s Disease Patients

Parkinson’s disease (PD) is a progressive neurodegenerative disease, causing loss of motor and nonmotor function. Diagnosis is based on clinical symptoms that do not develop until late in the disease progression, at which point the majority of the patients’ dopaminergic neurons are already destroyed. While many PD cases are idiopathic, hereditable genetic risks have been identified, including mutations in the gene for LRRK2, a multidomain kinase with roles in autophagy, mitochondrial function, transcription, molecular structural integrity, the endo-lysosomal system, and the immune response. A definitive PD diagnosis can only be made post-mortem, and no noninvasive or blood-based disease biomarkers are currently available. Alterations in metabolites have been identified in PD patients, suggesting that metabolomics may hold promise for PD diagnostic tools. In this study, we sought to identify metabolic markers of PD in plasma. Using a 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) NMR spectroscopy metabolomics platform coupled with machine learning (ML), we measured plasma metabolites from approximately age/sex-matched PD patients with G2019S LRRK2 mutations and non-PD controls. Based on the differential level of known and unknown metabolites, we were able to build a ML model and develop a Biomarker of Response (BoR) score, which classified male LRRK2 PD patients with 79.7% accuracy, 81.3% sensitivity, and 78.6% specificity. The high accuracy of the BoR score suggests that the metabolomics/ML workflow described here could be further utilized in the development of a confirmatory diagnostic for PD in larger patient cohorts. A diagnostic assay for PD will aid clinicians and their patients to quickly move toward a definitive diagnosis, and ultimately empower future clinical trials and treatment options.

Altered Plasma Mitochondrial Metabolites in Persistently Symptomatic Individuals after a GBCA-Assisted MRI

Despite the impressive safety of gadolinium (Gd)-based contrast agents (GBCAs), a small number of patients report the onset of new, severe, ongoing symptoms after even a single exposure-a syndrome termed Gadolinium Deposition Disease (GDD). Mitochondrial dysfunction and oxidative stress have been repeatedly implicated by animal and in vitro studies as mechanisms of Gd/GBCA-related toxicity, and as pathogenic in other diseases with similarities in presentation. Here, we aimed to molecularly characterize and explore potential metabolic associations with GDD symptoms. Detailed clinical phenotypes were systematically obtained for a small cohort of individuals (n = 15) with persistent symptoms attributed to a GBCA-enhanced MRI and consistent with provisional diagnostic criteria for GDD. Global untargeted mass spectroscopy-based metabolomics analyses were performed on plasma samples and examined for relevance with both single marker and pathways approaches. In addition to GDD criteria, frequently reported symptoms resembled those of patients with known mitochondrial-related diseases. Plasma differences compared to a healthy, asymptomatic reference cohort were suggested for 45 of 813 biochemicals. A notable proportion of these are associated with mitochondrial function and related disorders, including nucleotide and energy superpathways, which were over-represented. Although early evidence, coincident clinical and biochemical indications of potential mitochondrial involvement in GDD are remarkable in light of preclinical models showing adverse Gd/GBCA effects on multiple aspects of mitochondrial function. Further research on the potential contributory role of these markers and pathways in persistent symptoms attributed to GBCA exposure is recommended.

Molecular Subclassification Based on Crosstalk Analysis Improves Prediction of Prognosis in Colorectal Cancer

The poor performance of single-gene lists for prognostic predictions in independent cohorts has limited their clinical use. Here, we employed a pathway-based approach using embedded biological features to identify reproducible prognostic markers as an alternative. We used pathway activity score, sure independence screening, and K-means clustering analyses to identify and cluster colorectal cancer patients into two distinct subgroups, G2 (aggressive) and G1 (moderate). The differences between these two groups with respect to survival, somatic mutation, pathway activity, and tumor-infiltration by immunocytes were compared. These comparisons revealed that the survival rates in the G2 subgroup were significantly reduced compared to that in the G1 subgroup; further, the mutational burden rates in several oncogenes, including KRAS, DCLK1, and EPHA5, were significantly higher in the G2 subgroup than in the G1 subgroup. The enhanced activity of the critical pathways such as MYC and epithelial-mesenchymal transition may also lead to the progression of colorectal cancer. Taken together, we established a novel prognostic classification system that offers meritorious insights into the hallmarks of colorectal cancer.

Potential Significance and Clinical Value Explorations of Calmin (CLMN) in Breast Invasive Carcinoma

Objective

Function of calmin (CLMN) was rarely reported in human diseases, especially in tumor. Present study initially assessed the significance of CLMN in breast invasive carcinoma (BRCA).

Methods

Expressions of CLMN containing mRNA and protein in BRCA was firstly assessed, and association of CLMN mRNA expression with clinical phenotypes of BRCA patients was analyzed as well. Prognostic value of CLMN in BRCA was subsequently predicted based on the clinical characteristics of patients. Finally, the potential biological function associated with CLMN involved in BRCA was revealed.

Results

(1) The mRNA expression of CLMN was lower in BRCA compared with that in normal patients (P<0.001). However, result of CLMN total protein expression was opposite (P<0.05). (2) The mRNA expression of CLMN was statistically associated with BRCA patient’s age, gender, PR status, ER status, histological type, tumor stage, copy number, and methylation level (all P<0.05). (3) Compared with low expression group, high expression of CLMN was conducive to the overall survival of BRCA patients (P=0.0011). Detailed, survival difference between CLMN high and low expression groups was observed in patients with stage 1 (P=0.0250), positive ER status (P=0.0042), negative HER status (P=0.0433), luminal A (P=0.0065), luminal B (P=0.0123) and positive lymph node status (P=0.0069). Pathway analysis suggested that CLMN mainly participated in cell cycle process (P<0.05) and exerted inhibition effect on the cell cycle involved in BRCA (P<0.05).

Conclusion

CLMN mRNA high expression prolonged the survival time of patients and caused a favorable prognosis. The positive function of CLMN in BRCA required further investigation in future work.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Glycolysis Define Two Prognostic Subgroups of Lung Adenocarcinoma With Different Mutation Characteristics and Immune Infiltration Signatures

Increasing studies have proved that malignant tumors are associated with energy metabolism. This study was aimed to explore biological variables that impact the prognosis of patients in the glycolysis-related subgroups of lung adenocarcinoma (LUAD). The mRNA expression profiling and mutation data in large LUAD samples were collected from the Cancer Genome Atlas (TCGA) database. Then, we identified the expression level and prognostic value of glycolysis-related genes, as well as the fractions of 22 immune cells in the tumor microenvironment. The differences between glycolysis activity, mutation, and immune infiltrates were discussed in these groups, respectively. Two hundred fifty-five glycolysis-related genes were identified from gene set enrichment analysis (GSEA), of which 43 genes had prognostic values (p < 0.05). Next, we constructed a glycolysis-related competing endogenous RNA (ceRNA) network which related to the survival of LUAD. Then, two subgroups of LUAD (clusters 1 and 2) were identified by applying unsupervised consensus clustering to 43 glycolysis-related genes. The survival analysis showed that the cluster 1 patients had a worse prognosis (p < 0.001), and upregulated differentially expressed genes (DEGs) are interestingly enriched in malignancy-related biological processes. The differences between the two subgroups are SPTA1, KEAP1, USH2A, and KRAS among top 10 mutated signatures, which may be the underlying mechanism of grouping. Combined high tumor mutational burden (TMB) with tumor subgroups preferably predicts the prognosis of LUAD patients. The CIBERSORT algorithm results revealed that low TMB samples were concerned with increased infiltration level of memory resting CD4+ T cell (p = 0.03), resting mast cells (p = 0.044), and neutrophils (p = 0.002) in cluster 1 and high TMB samples were concerned with increased infiltration level of memory B cells, plasma cells, CD4 memory-activated T cells, macrophages M1, and activated mast cells in cluster 2, while reduced infiltration of monocytes, resting dendritic cells, and resting mast cells was captured in cluster 2. In conclusion, significant different gene expression characteristics were pooled according to the two subgroups of LUAD. The combination of subgroups, TMB and tumor-infiltrating immune cell signature, might be a novel prognostic biomarker in LUAD.

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

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

Identification of a 15 DNA Damage Repair-Related Gene Signature as a Prognostic Predictor for Lung Adenocarcinoma

BACKGROUND

Lung Adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to the lack of predictive markers. DNA Damage Repair (DDR)-related genes are closely related to cancer progression and treatment.

INTRODUCTION

To identify a reliable DDR-related gene signature as an independent predictor of LUAD.

METHODS

DDR-related genes were obtained using combined analysis of TCGA-LUAD data and literature information, followed by the identification of DDR-related prognostic genes. The DDR-related molecular subtypes were then screened, followed by Kaplan-Meier analysis, feature gene identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO regression analyses were performed for the feature genes of each subtype to construct a prognostic model. The clinical utility of the prognostic model was confirmed using the validation dataset GSE72094 and nomogram analysis.

RESULTS

Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using consensus cluster analysis, three molecular subtypes were screened. Cluster 2 had the best prognosis, while cluster 3 had the worst. Compared to cluster 2, clusters 1 and 3 consisted of more stage 3 - 4, T2-T4, male, and older samples. The feature genes of clusters 1, 2, and 3 were mainly enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature gene signature was identified for improving the prognosis of LUAD patients.

CONCLUSION

The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment.

Identification and Functional Characterization of Metabolites for Bone Mass in Peri- and Postmenopausal Chinese Women

CONTEXT

Although metabolic profiles appear to play an important role in menopausal bone loss, the functional mechanisms by which metabolites influence bone mineral density (BMD) during menopause are largely unknown.

OBJECTIVE

We aimed to systematically identify metabolites associated with BMD variation and their potential functional mechanisms in peri- and postmenopausal women.

DESIGN AND METHODS

We performed serum metabolomic profiling and whole-genome sequencing for 517 perimenopausal (16%) and early postmenopausal (84%) women aged 41 to 64 years in this cross-sectional study. Partial least squares regression and general linear regression analysis were applied to identify BMD-associated metabolites, and weighted gene co-expression network analysis was performed to construct co-functional metabolite modules. Furthermore, we performed Mendelian randomization analysis to identify causal relationships between BMD-associated metabolites and BMD variation. Finally, we explored the effects of a novel prominent BMD-associated metabolite on bone metabolism through both in vivo/in vitro experiments.

RESULTS

Twenty metabolites and a co-functional metabolite module (consisting of fatty acids) were significantly associated with BMD variation. We found dodecanoic acid (DA), within the identified module causally decreased total hip BMD. Subsequently, the in vivo experiments might support that dietary supplementation with DA could promote bone loss, as well as increase the osteoblast and osteoclast numbers in normal/ovariectomized mice. Dodecanoic acid treatment differentially promoted osteoblast and osteoclast differentiation, especially for osteoclast differentiation at higher concentrations in vitro (eg,10, 100 μM).

CONCLUSIONS

This study sheds light on metabolomic profiles associated with postmenopausal osteoporosis risk, highlighting the potential importance of fatty acids, as exemplified by DA, in regulating BMD.

Plasma Metabolomics for Discovery of Early Metabolic Markers of Prostate Cancer Based on Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry

BACKGROUND

The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa.

METHODS

Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS).

RESULTS

Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower.

CONCLUSION

Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa.

Cancer metabolomics: A tool of clinical utility for early diagnosis of gynaecological cancers

Gynaecological cancers are the major cause of cancer-related deaths in Indian women. The poor prognosis and lack of symptoms in the early stages make early cancer diagnosis difficult. The absence of mandatory screening programmes and the lack of awareness pose to be a real challenge in a developing economy as India. Prompt intervention is required to enhance cancer patient survival statistics and to lessen the social and financial burden. Conventional screening and cytological techniques employed currently have helped to reduce the incidence of cancers considerably. However, these tests offer low sensitivity and specificity and are not widely used for risk assessment, leading to inadequate early-stage cancer diagnosis. The accomplishment of Human Genome Project (HGP) has opened doors to exciting 'omics' platforms. Promising research in genomics and proteomics has revolutionized cancer detection and screening methodologies by providing more insights in the gene expression, protein function and how specific mutation in specific genes corresponds to a particular phenotype. However, these are incompetent to translate the information into clinical applicability. Various factors such as low sensitivity, diurnal variation in protein, poor reproducibility and analytical variables are prime hurdles. Thus the focus has been shifted to metabolomics, which is a much younger platform compared to genomics and proteomics. Metabolomics focuses on endpoint metabolites, which are final products sustained in the response to genetic or environmental changes by a living system. As a result, the metabolome indicates the cell's functional condition, which is directly linked to its phenotype. Metabolic profiling aims to study the changes occurred in metabolic pathways. This metabolite profile is capable of differentiating the healthy individuals from those having cancer. The pathways that a cell takes in turning malignant are exceedingly different, owing to the fact that transformation of healthy cells to abnormal cells is linked with significant metabolic abnormalities. This review is aimed to discuss metabolomics and its potential role in early diagnosis of gynaecological cancers, viz. breast, ovarian and cervical cancer.

Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26-37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895-0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).

Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis

Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.

Personalized Metabolic Analysis of Diseases

The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm called Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for a detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with a dynamically built objective function based on biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds supervised classification models to discriminate between patients and healthy subjects based on the computed metabolic network changes. The use of Metabolitics is demonstrated for three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully differentiate patients from healthy individuals by an average f1-score of 88 percent. Besides, in addition to the confirmation of previously reported breast cancer-associated pathways, we discovered that Biotin Metabolism along with Arginine and Proline Metabolism is subject to a significant increase in flux capacity, which have not been reported before.

Abnormal saccharides affecting cancer multi-drug resistance (MDR) and the reversal strategies

Clinically, chemotherapy is the mainstay in the treatment of multiple cancers. However, highly adaptable and activated survival signaling pathways of cancer cells readily emerge after long exposure to chemotherapeutics drugs, resulting in multi-drug resistance (MDR) and treatment failure. Recently, growing evidences indicate that the molecular action mechanisms of cancer MDR are closely associated with abnormalities in saccharides. In this review, saccharides affecting cancer MDR development are elaborated and analyzed in terms of aberrant aerobic glycolysis and its related enzymes, abnormal glycan structures and their associated enzymes, and glycoproteins. The reversal strategies including depletion of ATP, circumventing the original MDR pathway, activation by or inhibition of sugar-related enzymes, combination therapy with traditional cytotoxic agents, and direct modification on the sugar moiety, are ultimately proposed. It follows that abnormal saccharides have a significant effect on cancer MDR development, providing a new perspective for overcoming MDR and improving the outcome of chemotherapy.

Topic Evolution Analysis for Omics Data Integration in Cancers

One of the vital challenges for cancer diseases is efficient biomarkers monitoring formation and development are limited. Omics data integration plays a crucial role in the mining of biomarkers in the human condition. As the link between omics study on biomarkers discovery and cancer diseases is deepened, defining the principal technologies applied in the field is a must not only for the current period but also for the future. We utilize topic modeling to extract topics (or themes) as a probabilistic distribution of latent topics from the dataset. To predict the future trend of related cases, we utilize the Prophet neural network to perform a prediction correction model for existing topics. A total of 2,318 pieces of literature (from 2006 to 2020) were retrieved from MEDLINE with the query on “omics” and “cancer.” Our study found 20 topics covering current research types. The topic extraction results indicate that, with the rapid development of omics data integration research, multi-omics analysis (Topic 11) and genomics of colorectal cancer (Topic 10) have more studies reported last 15 years. From the topic prediction view, research findings in multi-omics data processing and novel biomarker discovery for cancer prediction (Topic 2, 3, 10, 11) will be heavily focused in the future. From the topic visuallization and evolution trends, metabolomics of breast cancer (Topic 9), pharmacogenomics (Topic 15), genome-guided therapy regimens (Topic 16), and microRNAs target genes (Topic 17) could have more rapidly developed in the study of cancer treatment effect and recurrence prediction.

Integration of transcriptomics and metabolomics reveals anlotinib-induced cytotoxicity in colon cancer cells

BACKGROUND

Mounting evidences suggested that anlotinib exhibits effective anti-tumor activity in various cancer types, such as lung cancer, glioblastoma and medullary thyroid cancer. However, its function in colon cancer remains to be further revealed.

METHODS

Colon cancer cells (HCT-116) were treated with or without anlotinib. Transcript and metabolite data were generated through RNA sequencing and liquid chromatography-tandem mass spectrometry, respectively. The integrated analysis transcriptomics and metabolomics was conducted using R programs and online tools, including ClusterProfiler R program, GSEA, Prognoscan and Cytoscape.

RESULTS

We found that differentially expressed genes (DEGs) were mainly involved in metabolic pathways and ribosome pathway. Structural maintenance of chromosome 3 (SMC3), Topoisomerase II alpha (TOP2A) and Glycogen phosphorylase B (PYGB) are the most significant DEGs which bring poor clinical prognosis in colon cancer. The analysis of metabolomics presented that most of the differentially accumulated metabolites (DAMs) were amino acids, such as L-glutamine, DL-serine and aspartic acid. The joint analysis of DEGs and DAMs showed that they were mainly involved in protein digestion and absorption, ABC transporters, central carbon metabolism, choline metabolism and Gap junction. Anlotinib affected protein synthesis and energy supporting of colon cancer cells by regulating amino acid metabolism.

CONCLUSIONS

Anlotinib has a significant effect on colon cancer in both transcriptome and metabolome. Our research will provide possible targets for colon cancer treatment using anlotinib.

Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data

Pathological images are easily accessible data with the potential of prognostic biomarkers. Moreover, integration of heterogeneous data types from multi-modality, such as pathological image and gene expression data, is invaluable to help predicting cancer patient survival. However, the analytical challenges are significant. Here, we take the hepatocellular carcinoma (HCC) pathological image features extracted by CellProfiler, and apply them as the input for Cox-nnet, a neural network-based prognosis prediction model. We compare this model with the conventional Cox proportional hazards (Cox-PH) model, CoxBoost, Random Survival Forests and DeepSurv, using C-index and log-rank P-values. The results show that Cox-nnet is significantly more accurate than Cox-PH and Random Survival Forests models and comparable with CoxBoost and DeepSurv models, on pathological image features. Further, to integrate pathological image and gene expression data of the same patients, we innovatively construct a two-stage Cox-nnet model, and compare it with another complex neural-network model called PAGE-Net. The two-stage Cox-nnet complex model combining histopathology image and transcriptomic RNA-seq data achieves much better prognosis prediction, with a median C-index of 0.75 and log-rank P-value of 6e-7 in the testing datasets, compared to PAGE-Net (median C-index of 0.68 and log-rank P-value of 0.03). Imaging features present additional predictive information to gene expression features, as the combined model is more accurate than the model with gene expression alone (median C-index 0.70). Pathological image features are correlated with gene expression, as genes correlated to top imaging features present known associations with HCC patient survival and morphogenesis of liver tissue. This work proposes two-stage Cox-nnet, a new class of biologically relevant and interpretable models, to integrate multiple types of heterogenous data for survival prediction.

Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

BACKGROUND

previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software.

RESULTS

here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning-based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression.

CONCULSION

Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

One of the major hallmarks of cancer is the derailment of a cell's metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

PURPOSE

The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure.

METHODS

Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.

RESULTS

We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85.

CONCLUSION

Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

The Heterogeneity of Breast Cancer Metabolism

Despite advances in screening, therapy, and surveillance that have improved patient survival rates, breast cancer is still the most commonly diagnosed cancer and the second leading cause of cancer mortality among women [1]. Breast cancer is a highly heterogeneous disease rooted in a genetic basis, influenced by extrinsic stimuli, and reflected in clinical behavior. The diversity of breast cancer hormone receptor status and the expression of surface molecules have guided therapy decisions for decades; however, subtype-specific treatment often yields diverse responses due to varying tumor evolution and malignant potential. Although the mechanisms behind breast cancer heterogeneity is not well understood, available evidence suggests that studying breast cancer metabolism has the potential to provide valuable insights into the causes of these variations as well as viable targets for intervention.

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US

OBJECTIVES

The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.

STUDY DESIGN

A retrospective cohort study.

SETTING

Two medical centres from the USA.

PARTICIPANTS

Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.

OUTCOME MEASURES

Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.

RESULTS

A model to determine gestational age was developed (R²=0.98) and validated (R²=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R²=-0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.

CONCLUSIONS

In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.