Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BACKGROUND

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Influence of Dietary Polyunsaturated Fatty Acid Intake on Potential Lipid Metabolite Diagnostic Markers in Renal Cell Carcinoma: A Case-Control Study

Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes.

Lipidomic profiles in serum and urine in children with steroid sensitive nephrotic syndrome

BACKGROUND

Steroid-sensitive nephrotic syndrome (SSNS) accounts for approximately 80% of cases of nephrotic syndrome. The involvement of aberrant lipid metabolism in early SSNS is poorly understood, warranting further investigation. This study aimed to explore alterations in lipid metabolism associated with SSNS pathogenesis.

METHODS

A screening cohort containing serum (50 SSNS, 37 controls) and urine samples (27 SSNS, 26 controls) was analyzed by untargeted lipidomic profiling using UHPLC-QTOF-MS. Then, a validation cohort (20 SSNS, 56 controls) underwent further analysis to check the potential clinical application by ROC curve analysis.

RESULTS

Lipidomic profiling of serum and urine samples revealed significant lipid alterations in SSNS patients, with the alterations in the serum samples being more significant. An elevated concentration of PE and PG and downregulated concentration of FA were observed in SSNS serum. A total of 38 dysregulated lipids and 5 lipid metabolic pathways were identified in the serum samples in SSNS patients. Validation in the second cohort confirmed differential regulation of nine kinds of lipids, including 5 up-regulated substances [SM d33:2 (m/z = 686.5361), SHexCer d34:1 (m/z = 779.521), PI 20:4_22:4 (m/z = 934.5558), Cer_NS d18:1_23:0 (m/z = 635.6216), and GM3 d36:1 (m/z = 1180.7431)], as well as 4 down-regulated substances: [CE 18:1 (m/z = 650.601), PE 38:6 (m/z = 763.5205), PC 17:0_20:4 (m/z = 795.5868) and EtherPC 16:2e_20:4 (m/z = 763.5498)].

CONCLUSIONS

Untargeted lipidomic analysis successfully identified specific lipid class changes in patients with SSNS, providing a deeper understanding of lipid alterations and underlying mechanisms associated with SSNS.

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma

The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma

The ultimate goal of surgical treatment in cancer is to remove the tumor mass for restoring a healthy state. A 16-lipid panel that discriminated healthy controls from clear cell renal cell carcinoma (ccRCC) patients in a prior study was evaluated in the present work in paired-serum samples collected from patients (n = 41) before and after nephrectomy. Changes in the lipid and metabolite fingerprints from ccRCC patients were investigated and compared with fingerprints from healthy individuals obtained by means of ultra-performance liquid chromatography-high-resolution mass spectrometry. The lipid panel differentiated phenotypes associated with metabolic restoration after surgery, representing a serum signature of phenoreversion to a healthy metabolic state. In particular, PC 16:0/0:0, PC 18:2/18:2, and linoleic acid allowed discriminating serum samples from ccRCC patients with poor prognosis from those with an improved outcome during the follow-up period. Ratios of PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid levels may contribute as prognostic tools to support decision-making during the patient follow-up care. The preliminary character of these results should be validated with larger cohorts, including subjects with different ethnicities, life style, and diets. MetaboLights study references: MTBLS1839, MTBLS3838, and MTBLS4629.

Fatty acid metabolism reprogramming in ccRCC: mechanisms and potential targets

Lipid droplet formation is a defining histological feature in clear-cell renal cell carcinoma (ccRCC) but the underlying mechanisms and importance of this biological behaviour have remained enigmatic. De novo fatty acid (FA) synthesis, uptake and suppression of FA oxidation have all been shown to contribute to lipid storage, which is a necessary tumour adaptation rather than a bystander effect. Clinical studies and mechanistic investigations into the roles of different enzymes in FA metabolism pathways have revealed new metabolic vulnerabilities that hold promise for clinical effect. Several metabolic alterations are associated with worse clinical outcomes in patients with ccRCC, as lipogenic genes drive tumorigenesis. Enzymes involved in the intrinsic FA metabolism pathway include FA synthase, acetyl-CoA carboxylase, ATP citrate lyase, stearoyl-CoA desaturase 1, cluster of differentiation 36, carnitine palmitoyltransferase 1A and the perilipin family, and each might be potential therapeutic targets in ccRCC owing to the link between lipid deposition and ccRCC risk. Adipokines and lipid species are potential biomarkers for diagnosis and treatment monitoring in patients with ccRCC. FA metabolism could potentially be targeted for therapeutic intervention in ccRCC as small-molecule inhibitors targeting the pathway have shown promising results in preclinical models.

Epidemiology and Prevention of Renal Cell Carcinoma

With 400,000 diagnosed and 180,000 deaths in 2020, renal cell carcinoma (RCC) accounts for 2.4% of all cancer diagnoses worldwide. The highest disease burden developed countries, primarily in Europe and North America. Incidence is projected to increase in the future as more countries shift to Western lifestyles. Risk factors for RCC include fixed factors such as gender, age, and hereditary diseases, as well as intervening factors such as smoking, obesity, hypertension, diabetes, diet and alcohol, and occupational exposure. Intervening factors in primary prevention, understanding of congenital risk factors and the establishment of early diagnostic tools are important for RCC. This review will discuss RCC epidemiology, risk factors, and biomarkers involved in reducing incidence and improving survival.

The Diagnostic Value of Serum Ang, VEGF, and CRP Combined with the Chinese Medicine Antitumor Formula in the Treatment of Advanced Renal Carcinoma

Objective

To explore the diagnostic value of serum angiopoietin (Ang), vascular endothelial growth factor (VEGF), and C-reactive protein (CRP) combined with the Chinese medicine antitumor formula in the treatment of advanced renal carcinoma.

Methods

Retrospective analysis was performed for the data of 60 patients with advanced renal cancer admitted at Yantaishan Hospital from February 2019 to February 2020. All patients were treated with Chinese medicine antitumor formula. The serum Ang, VEGF, and CRP levels in venous blood samples were detected before and after treatment. Sensitivity, specificity, and AUC of combined serum Ang, VEGF, and CRP were analyzed utilizing the receiver operating characteristic curve (ROC) (95% CI).

Results

There were 52 cases of clear-cell carcinoma (86.7%), 7 cases of papillary carcinoma (11.7%), and 1 case of chromophobe renal cell carcinoma (1.7%). The average tumor diameter was (9.67 ± 0.65) cm, and the KPS score was (74.68 ± 1.52). About 75% of the patients had metastasis. After treatment, the level of serum Ang, VEGF, and CRP was immensely lower compared to that before treatment (P < 0.001). The sensitivity, specificity, and AUC (95%CI) of the combined detection of Ang, VEGF, and CRP before treatment were 86.7%, 90.0%, and 0.883 (0.817-0.950), while the sensitivity, specificity, and AUC (95%CI) of the combined detection of Ang, VEGF, and CRP were 83.3%, 86.7%, and 0.850 (0.776-0.9524), respectively.

Conclusion

The combined detection of serum Ang, VEGF, and CRP has high diagnostic value for patients with advanced renal cancer treated with Chinese medicine antitumor formula.

Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage

Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R² value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.

Development of computational models using omics data for the identification of effective cancer metabolic biomarkers

Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several orders of magnitude in abundance. Machine learning tools that effectively leverage these data to accurately identify disease states are in high demand. While mass spectrometry data sets are rich with potentially useful information, using the data effectively can be challenging because of missing entries in the data sets and because the number of samples is typically much smaller than the number of features, two challenges that make machine learning difficult. To address this problem, we have modified a new supervised classification tool, the Aristotle Classifier, so that omics data sets can be better leveraged for identifying disease states. The optimized classifier, AC.2021, is benchmarked on multiple data sets against its predecessor and two leading supervised classification tools, Support Vector Machine (SVM) and XGBoost. The new classifier, AC.2021, outperformed existing tools on multiple tests using proteomics data. The underlying code for the classifier, provided herein, would be useful for researchers who desire improved classification accuracy when using their omics data sets to identify disease states.