Analysis of Saliva Lipids in Breast and Prostate Cancer by IR Spectroscopy

Dynamic response antibodies SARS-CoV-2 human saliva studied using two-dimensional correlation (2DCOS) infrared spectral analysis coupled with receiver operation characteristics analysis

COVID-19 has affected the entire world due to the rapid spread of SARS-CoV-2, mainly through airborne particles from saliva, which, being easily obtained, help monitor the progression of the disease. Fourier transform infrared (FTIR) spectra combined with chemometric analysis could increase the diagnostic efficiency of the disease. However, two-dimensional correlation spectroscopy (2DCOS) is superior to conventional spectra as it helps to resolve the minute overlapped peaks. In this work, we aimed to use 2DCOS and receiver operating characteristic (ROC) analyses to compare the immune response in saliva associated with COVID-19, which could be important in biomedical diagnosis. FTIR spectra of human saliva samples from male (575) and female (366) patients ranging from 20 to 82 ± 2 years of age were used for the study. Age groups were segregated as G1 (25-40 ± 2 years), G2 (45-60 ± 2 years), and G3 (65-80 ± 2 years). The results of the 2DCOS analysis showed biomolecular changes in response to SARS-CoV-2. 2DCOS analyses of the male G1 + (1579,1644) and -(1531,1598) crossover peaks evidenced changes such as amide I > IgG. Female G1 crossover peaks -(1504,1645), (1504,1545) and -(1391,1645) resulted in amide I > IgG > IgM. The asynchronous spectra in 1300-900 cm-1 of the G2 male group showed that IgM is more important in diagnosing infections than IgA. Female G2 asynchronous spectra -(1027,1242) and + (1068,1176) showed that IgA > IgM is produced against SARS-CoV-2. The G3 male group evidenced antibody changes in IgG > IgM. The absence of IgM in the female G3 population diagnoses a specifically targeted immunoglobulin associated with sex. Moreover, ROC analysis showed sensitivity (85-89 % men; 81-88 % women) and specificity (90-93 % men; 78-92 % women) for the samples studied. The general classification performance (F1 score) of the studied samples is high for the male (88-91 %) and female (80-90 %) populations. This high PPV (positive predictive value) and NPV (negative predictive value) verify our segregation of COVID-19 positive and negative sample groups. Therefore, 2DCOS with ROC analysis using FTIR spectra have the potential for a non-invasive approach to monitoring COVID-19.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Drying of bio-colloidal sessile droplets: Advances, applications, and perspectives

Drying of biologically-relevant sessile droplets, including passive systems such as DNA, proteins, plasma, and blood, as well as active microbial systems comprising bacterial and algal dispersions, has garnered considerable attention over the last decades. Distinct morphological patterns emerge when bio-colloids undergo evaporative drying, with significant potential in a wide range of biomedical applications, spanning bio-sensing, medical diagnostics, drug delivery, and antimicrobial resistance. Consequently, the prospects of novel and thrifty bio-medical toolkits based on drying bio-colloids have driven tremendous progress in the science of morphological patterns and advanced quantitative image-based analysis. This review presents a comprehensive overview of bio-colloidal droplets drying on solid substrates, focusing on the experimental progress during the last ten years. We provide a summary of the physical and material properties of relevant bio-colloids and link their native composition (constituent particles, solvent, and concentrations) to the patterns emerging due to drying. We specifically examined the drying patterns generated by passive bio-colloids (e.g., DNA, globular, fibrous, composite proteins, plasma, serum, blood, urine, tears, and saliva). This article highlights how the emerging morphological patterns are influenced by the nature of the biological entities and the solvent, micro- and global environmental conditions (temperature and relative humidity), and substrate attributes like wettability. Crucially, correlations between emergent patterns and the initial droplet compositions enable the detection of potential clinical abnormalities when compared with the patterns of drying droplets of healthy control samples, offering a blueprint for the diagnosis of the type and stage of a specific disease (or disorder). Recent experimental investigations of pattern formation in the bio-mimetic and salivary drying droplets in the context of COVID-19 are also presented. We further summarized the role of biologically active agents in the drying process, including bacteria, algae, spermatozoa, and nematodes, and discussed the coupling between self-propulsion and hydrodynamics during the drying process. We wrap up the review by highlighting the role of cross-scale in situ experimental techniques for quantifying sub-micron to micro-scale features and the critical role of cross-disciplinary approaches (e.g., experimental and image processing techniques with machine learning algorithms) to quantify and predict the drying-induced features. We conclude the review with a perspective on the next generation of research and applications based on drying droplets, ultimately enabling innovative solutions and quantitative tools to investigate this exciting interface of physics, biology, data sciences, and machine learning.

[Developments in Research on Salivary Biomarkers in the Diagnosis of Systemic Diseases]

Saliva, an important biological fluid secreted by oral glands, serves multiple functions. It performs cleaning and protective functions for oral tissues, safeguarding against biological, mechanical and chemical stimuli, while allowing for the sensory perception of taste and temperature. It is also responsible for the preliminary digestion of food. These functions and properties of saliva are attributed to the presence of electrolytes, buffers, proteins, glycoproteins, and lipids in saliva. Recent studies have found that saliva contains biomarkers that are closely connected with the pathophysiological status of the human body, suggesting that saliva makes an ideal biological fluid for drug monitoring and biomarker screening. Therefore, salivary biomarkers can be used as an instrument for physical monitoring and localization of the occurrence of diseases, thereby accomplishing early diagnosis of diseases and assessment of the overall health status of patients. However, the actual application of salivary biomarkers in the diagnosis and treatment of systemic diseases is still not widely available, and the establishment of evaluation criteria and the exploration of its mechanism are not sufficiently investigated. Herein, we reviewed the latest research findings on applying the salivary biomarkers in the diagnosis of systemic diseases.