Toward noncontact macroscopic imaging of multiple cancers using multi-spectral inelastic scattering detection.

Here we introduce a Raman spectroscopy approach combining multi-spectral imaging and a new fluorescence background subtraction technique to image individual Raman peaks in less than 5 seconds over a square field-of-view of 1-centimeter sides with 350 micrometers resolution. First, human data is presented supporting the feasibility of achieving cancer detection with high sensitivity and specificity - in brain, breast, lung, and ovarian/endometrium tissue - using no more than three biochemically interpretable biomarkers associated with the inelastic scattering signal from specific Raman peaks. Second, a proof-of-principle study in biological tissue is presented demonstrating the feasibility of detecting a single Raman band - here the CH2/CH3 deformation bands from proteins and lipids - using a conventional multi-spectral imaging system in combination with the new background removal method. This study paves the way for the development of a new Raman imaging technique that is rapid, label-free, and wide field.

Establishment of a multi-line immunochromatography based on magnetic nanoparticles for simultaneous screening of multiple biomarkers.

Biomarkers screening is a benefit approach for early diagnosis of major diseases. In this study, magnetic nanoparticles (MNPs) have been utilized as labels to establish a multi-line immunochromatography (MNP-MLIC) for simultaneous detection of carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA 19-9), and alpha-fetoprotein (AFP) in a single serum sample. Under the optimal parameters, the three biomarkers can be rapidly and simultaneously qualitative screening within 15 min by naked eye. As for quantitative detection, the MNP-MLIC test strips were precisely positioned and captured by a smartphone, and signals on the test and control lines were extracted by ImageJ software. The signal ratio of test and control lines has been calculated and used to plot quantitative standard curves with the logarithmic concentration, of which the correlation coefficients are more than 0.99, and the limit of detection for CEA, CA 19-9, and AFP were 0.60 ng/mL, 1.21 U/mL, and 0.93 ng/mL, respectively. The recoveries of blank serum were 75.0 ~ 112.5% with the relative standard deviation ranging from 2.5 to 15.3%, and the specificity investigation demonstrated that the MNP-MLIC is highly specific to the three biomarkers. In conclusion, the developed MNP-MLIC offers a rapid, simple, accurate, and highly specific method for simultaneously detecting multiple biomarkers in serum samples, which provides an efficient and accurate approach for the early diagnosis of diseases.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Comparison of primary and metastatic FH-deficient renal cell carcinomas documents morphologic divergence and potential diagnostic pitfall with peritoneal mesothelioma.

Fumarate hydratase (FH)-deficient renal cell carcinomas are rare neoplasms characterized by a wide morphological heterogeneity and pathogenetic mutations in the FH gene. They often show aggressive behavior with rapid diffusion to distant organs, so novel therapeutic scenarios have been explored, including EGFR inhibitors and PD-L1 expression for targeted immunotherapy. Herein, we investigated a series of eleven primary FH-deficient renal cell carcinomas and seven distant metastases to evaluate tumor heterogeneity even in metastatic sites and estimate the specific spread rates to various organs. Furthermore, the tumors were tested for immunohistochemical PD-L1 expression and EGFR mutations. Most metastatic cases involved the abdominal lymph nodes (4/7, 57%), followed by the peritoneum (3/7, 42%), the liver (2/7, 29%), and the lungs (1/7, 14%). Six metastatic localizations were histologically documented, revealing morphological heterogeneous architecture often differing from the corresponding primary renal tumor. Peritoneal involvement morphologically resembled a benign reactive mesothelial process or primary peritoneal mesothelioma, thus advocating to perform an accurate immunohistochemical panel, including PAX 8 and FH, to reach the proper diagnosis. A pure low-grade SDH-looking primary FH-deficient renal cell carcinoma was also recorded. As for therapy, significant PD-L1 labeling was found in 60% of primary renal tumors, while none of them carried pathogenetic EGFR mutations. Our data show that FH-deficient renal cell carcinoma may be morphologically heterogeneous in metastases as well, which involve the lymph nodes, the liver, and the peritoneum more frequently than other renal tumors. Due to the high frequency of this latter (42%), pathologists should always be concerned about ruling out mesothelial-derived mimickers, and the occurrence of rarer primary low-grade-looking types. Finally, contrary to EGFR mutations, PD-L1 expression could be a possible predictive biomarker for the therapy of these tumors.

A Case for Caution: Patient Use of Artificial Intelligence.

Artificial intelligence use is increasing exponentially, including by patients in medical decision- making. Because of the limitations of chatbots and the possibility of receiving erroneous or incomplete information, patient.

Oxidative Stress in the Pathogenesis of Oral Cancer.

Oxidative stress, arising from an imbalance between reactive oxygen species (ROS) and antioxidants, contributes significantly to oral cancer such as oral squamous cell carcinoma (OSCC) initiation, promotion, and progression. ROS, generated both internally and externally, induce cellular damage including DNA mutations and lipid peroxidation, fostering oncogene activation and carcinogenesis. The objective of this review was to cover and analyze the interplay between ROS and antioxidants, influencing the key processes such as cell proliferation, apoptosis, and angiogenesis, shaping the trajectory of OSCC development. Despite the promise of antioxidants to halt cancer progression and mitigate oxidative damage, their therapeutic efficacy remains debated. The conducted literature search highlighted potential biomarkers that indicate levels of oxidative stress, showing promise for the early detection and monitoring of OSCC. Furthermore, melatonin has emerged as a promising adjunct therapy for OSCC, exerting antioxidant and oncostatic effects by modulating tumor-associated neutrophils and inhibiting cancer cell survival and migration. In addition, this review aims to shed light on developing personalized therapeutic strategies for patients with OSCC such as melatonin therapy, which will be discussed. Research is needed to elucidate the underlying mechanisms and clinical implications of oxidative stress modulation in the context of oral cancer.

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

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

Recent advances in the applications of DNA frameworks in liquid biopsy: A review.

Cancer is one of the serious threats to public life and health. Early diagnosis, real-time monitoring, and individualized treatment are the keys to improve the survival rate and prolong the survival time of cancer patients. Liquid biopsy is a potential technique for cancer early diagnosis due to its non-invasive and continuous monitoring properties. However, most current liquid biopsy techniques lack the ability to detect cancers at the early stage. Therefore, effective detection of a variety of cancers is expected through the combination of various techniques. Recently, DNA frameworks with tailorable functionality and precise addressability have attracted wide spread attention in biomedical applications, especially in detecting cancer biomarkers such as circulating tumor cells (CTCs), exosomes and circulating tumor nucleic acid (ctNA). Encouragingly, DNA frameworks perform outstanding in detecting these cancer markers, but also face some challenges and opportunities. In this review, we first briefly introduced the development of DNA frameworks and its typical structural characteristics and advantages. Then, we mainly focus on the recent progress of DNA frameworks in detecting commonly used cancer markers in liquid-biopsy. We summarize the advantages and applications of DNA frameworks for detecting CTCs, exosomes and ctNA. Furthermore, we provide an outlook on the possible opportunities and challenges for exploiting the structural advantages of DNA frameworks in the field of cancer diagnosis. Finally, we envision the marriage of DNA frameworks with other emerging materials and technologies to develop the next generation of disease diagnostic biosensors.

Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping.

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

Pan-Cancer Molecular Biomarkers: A Paradigm Shift in Diagnostic Pathology.

The rapid adoption of next-generation sequencing in clinical oncology has enabled the detection of molecular biomarkers shared between multiple tumor types. These pan-cancer biomarkers include sequence-altering mutations, copy number changes, gene rearrangements, and mutational signatures and have been demonstrated to predict response to targeted therapy. This article reviews issues surrounding current and emerging pan-cancer molecular biomarkers in clinical oncology: technological advances that enable the broad detection of cancer mutations across hundreds of genes, the spectrum of driver and passenger mutations derived from human cancer genomes, and implications for patient care now and in the near future.

Identification of epigenetic silencing of the SFRP2 gene in colorectal cancer as a clinical biomarker and molecular significance.

BACKGROUND: Several studies have suggested secreted frizzled-related protein 2 (SFRP2) gene as a potential clinical biomarker in colorectal cancer (CRC). However, its diagnostic role remains unclear. In this study, we aimed to investigate the significance of SFRP2 methylation levels in a large cohort of biological specimens (including blood, adipose and colonic tissues) from patients with CRC, thereby potentially identifying new biomarker utility. METHODS: We examined the expression (by qPCR) and methylation status (by 450 K DNA array and DNA pyrosequencing) of the SFRP2 gene in healthy participants (N = 110, aged as 53.7 (14.2), 48/62 males/females) and patients with CRC (N = 85, aged 67.7 (10.5), 61/24 males/females), across different biological tissues, and assessing its potential as a biomarker for CRC. Additionally, we investigated the effect of recombinant human SFRP2 (rhSFRP2) as a therapeutic target, on cell proliferation, migration, and the expression of key genes related to carcinogenesis and the Wnt pathway. RESULTS: Our findings revealed that SFRP2 promoter methylation in whole blood could predict cancer stage (I + II vs. III + IV) (AUC = 0.653), lymph node invasion (AUC = 0.692), and CRC recurrence (AUC = 0.699) in patients with CRC (all with p < 0.05). Furthermore, we observed a global hypomethylation of SFRP2 in tumors compared to the adjacent area (p < 0.001). This observation was validated in the TCGA-COAD and TCGA-READ cohorts, demonstrating overall hypermethylation (both with p < 0.001) and low expression (p < 0.001), as shown in publicly available scRNA-Seq data. Notably, neoadjuvant-treated CRC patients exhibited lower SFRP2 methylation levels compared to untreated patients (p < 0.05) and low promoter SFRP2 methylation in untreated patients was associated with poor overall survival (p < 0.05), when compared to high methylation. Finally, treatment with 5 µg of rhSFRP2 treatment in CRC cells (HCT116 cells) inhibited cell proliferation (p < 0.001) and migration (p < 0.05), and downregulated the expression of AXIN2 (p < 0.01), a gene involved in Wnt signaling pathway. CONCLUSIONS: These findings establish promoter methylation of the SFRP2 gene as a prognostic candidate in CRC when assessed in blood, and as a therapeutic prognostic candidate in tumors, potentially valuable in clinical practice. SFRP2 also emerges as a therapeutic option, providing new clinical and therapeutical avenues.

An Innovative Approach for Tailoring Molecularly Imprinted Polymers for Biosensors-Application to Cancer Antigen 15-3.

This work presents a novel approach for tailoring molecularly imprinted polymers (MIPs) with a preliminary stage of atom transfer radical polymerization (ATRP), for a more precise definition of the imprinted cavity. A well-defined copolymer of acrylamide and N,N'-methylenebisacrylamide (PAAm-co-PMBAm) was synthesized by ATRP and applied to gold electrodes with the template, followed by a crosslinking reaction. The template was removed from the polymer matrix by enzymatic/chemical action. The surface modifications were monitored via electrochemical impedance spectroscopy (EIS), having the MIP polymer as a non-conducting film designed with affinity sites for CA15-3. The resulting biosensor exhibited a linear response to CA15-3 log concentrations from 0.001 to 100 U/mL in PBS or in diluted fetal bovine serum (1000×) in PBS. Compared to the polyacrylamide (PAAm) MIP from conventional free-radical polymerization, the ATRP-based MIP extended the biosensor's dynamic linear range 10-fold, improving low concentration detection, and enhanced the signal reproducibility across units. The biosensor demonstrated good sensitivity and selectivity. Overall, the work described confirmed that the process of radical polymerization to build an MIP material influences the detection capacity for the target substance and the reproducibility among different biosensor units. Extending this approach to other cancer biomarkers, the methodology presented could open doors to a new generation of MIP-based biosensors for point-of-care disease diagnosis.

Comprehensive molecular profiling identifies actionable biomarkers for patients from Thailand and the United Arab Emirates with advanced malignancies.

Background: Comprehensive molecular profiling of tissue samples that can help guide therapy management is not widely available across the globe. Methods: Comprehensive molecular profiling through Caris Molecular Intelligence involves the analysis of DNA through next-generation sequencing, chromogenic or fluorescent in situ hybridization, pyrosequencing, and copy number alterations; RNA through whole-transcriptome sequencing and multiplex PCR of RNA; and protein through immunohistochemistry. Results: Here we describe the experience of molecular profiling of tumor tissue samples from patients diagnosed with advanced solid tumors and treated in two countries, the United Arab Emirates and Thailand. Tumor cancer cases submitted to Caris Life Sciences (Phoenix, Arizona, USA) for molecular profiling from the UAE and Thailand were retrospectively analyzed (data accessed between 2019 and 2020) for their molecular alterations and clinical biomarkers, without regard to ethnicity. A total of 451 samples from 35 distinct types of advanced cancers were examined for mutations, amplifications, overexpression, exon copy number alterations, microsatellite instability, deficient mismatch repair, tumor mutational burden, and fusions. Interrogating each step of the biological pathway, from DNA to RNA to distinct protein, identified an alteration with an associated therapy for 75% of these tumor samples. The most common alterations identified included elevated PDL-1 that can be targeted with an immune checkpoint inhibitors and amplification of HER2 for which a variety of anti HER2 therapies are available. Conclusion: Comprehensive molecular profiling in patients with advanced malignancies can help optimize therapeutic management allowing for improved prognostic outcome.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives.

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Prognostic value of CDKN2A in head and neck squamous cell carcinoma via pathomics and machine learning.

This study aims to enhance the prognosis prediction of Head and Neck Squamous Cell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2A gene expression from pathology images, directly correlating with patient outcomes. Our approach introduces a novel AI-driven pathomics framework, delineating a more precise relationship between CDKN2A expression and survival rates compared to previous studies. Utilizing 475 HNSCC cases from the TCGA database, we stratified patients into high-risk and low-risk groups based on CDKN2A expression thresholds. Through pathomics analysis of 271 cases with available slides, we extracted 465 distinctive features to construct a Gradient Boosting Machine (GBM) model. This model was then employed to compute Pathomics scores (PS), predicting CDKN2A expression levels with validation for accuracy and pathway association analysis. Our study demonstrates a significant correlation between higher CDKN2A expression and improved median overall survival (66.73 months for high expression vs. 42.97 months for low expression, p = 0.013), establishing CDKN2A's prognostic value. The pathomic model exhibited exceptional predictive accuracy (training AUC: 0.806; validation AUC: 0.710) and identified a strong link between higher Pathomics scores and cell cycle activation pathways. Validation through tissue microarray corroborated the predictive capacity of our model. Confirming CDKN2A as a crucial prognostic marker in HNSCC, this study advances the existing literature by implementing an AI-driven pathomics analysis for gene expression evaluation. This innovative methodology offers a cost-efficient and non-invasive alternative to traditional diagnostic procedures, potentially revolutionizing personalized medicine in oncology.

Raman Imaging-A Valuable Tool for Tracking Fatty Acid Metabolism-Normal and Cancer Human Colon Single-Cell Study.

Altered metabolism of lipids is a key factor in many diseases including cancer. Therefore, investigations into the impact of unsaturated and saturated fatty acids (FAs) on human body homeostasis are crucial for understanding the development of lifestyle diseases. In this paper, we focus on the impact of palmitic (PA), linoleic (LA), and eicosapentaenoic (EPA) acids on human colon normal (CCD-18 Co) and cancer (Caco-2) single cells using Raman imaging and spectroscopy. The label-free nature of Raman imaging allowed us to evaluate FAs dynamics without modifying endogenous cellular metabolism. Thanks to the ability of Raman imaging to visualize single-cell substructures, we have analyzed the changes in chemical composition of endoplasmic reticulum (ER), mitochondria, lipid droplets (LDs), and nucleus upon FA supplementation. Analysis of Raman band intensity ratios typical for lipids, proteins, and nucleic acids (I1656/I1444, I1444/I1256, I1444/I750, I1304/I1256) proved that, using Raman mapping, we can observe the metabolic pathways of FAs in ER, which is responsible for the uptake of exogenous FAs, de novo synthesis, elongation, and desaturation of FAs, in mitochondria responsible for energy production via FA oxidation, in LDs specialized in cellular fat storage, and in the nucleus, where FAs are transported via fatty-acid-binding proteins, biomarkers of human colon cancerogenesis. Analysis for membranes showed that the uptake of FAs effectively changed the chemical composition of this organelle, and the strongest effect was noticed for LA. The spectroscopy studies have been completed using XTT tests, which showed that the addition of LA or EPA for Caco-2 cells decreases their viability with a stronger effect observed for LA and the opposite effect observed for PA. For normal cells, CCD-18 Co supplementation using LA or EPA stimulated cells for growing, while PA had the opposite impact.

Plasma Thioredoxin Reductase as a Potential Diagnostic Biomarker for Breast Cancer.

BACKGROUND: Early diagnosis of breast cancer is critical to the treatment and prognosis of breast cancer patients. Our aim is to explore more practical and effective diagnostic methods to facilitate early treatment and improve prognosis for breast cancer patients. MATERIALS AND METHODS: The Mann-Whitney U test, receiver operating characteristic curve, Youden index, Chi-square test, and Fisher's exact test were used to determine whether plasma thioredoxin reductase (TrxR) could be used for the clinical diagnosis of breast cancer. The Wilcoxon signed-rank test was used to validate the prognostic potential of plasma TrxR activity assessment. RESULTS: A total of 761 patients were included, including 537 cases of breast cancer and 224 cases of benign breast diseases. Plasma TrxR activity in the breast cancer group [8.0 (6.0, 9.45) U/mL] was significantly higher than that in the benign group [3.05 (1.20, 6.275) U/mL]. The diagnostic efficiency of TrxR for breast cancer was higher than that of other conventional breast cancer biomarkers, with an area under the curve of 0.821 (95% CI = 0.791-0.852). In addition, TrxR can be used in combination with conventional tumor markers to further improve the diagnostic efficiency. The optimal TrxR threshold for identifying benign and malignant diseases is 7.45 U/mL. We detected plasma TrxR activity and serum tumor markers before and after antitumor therapies in 333 breast cancer patients and found that their trends were basically the same, with a significant decrease in plasma TrxR activity after treatment. CONCLUSION: Plasma TrxR activity can be used as a suitable biomarker for breast cancer diagnosis and efficacy assessment.

Leveraging electrochemical sensors to improve efficiency of cancer detection.

Electrochemical biosensors have emerged as a promising technology for cancer detection due to their high sensitivity, rapid response, low cost, and capability for non-invasive detection. Recent advances in nanomaterials like nanoparticles, graphene, and nanowires have enhanced sensor performance to allow for cancer biomarker detection, like circulating tumor cells, nucleic acids, proteins and metabolites, at ultra-low concentrations. However, several challenges need to be addressed before electrochemical biosensors can be clinically implemented. These include improving sensor selectivity in complex biological media, device miniaturization for implantable applications, integration with data analytics, handling biomarker variability, and navigating regulatory approval. This editorial critically examines the prospects of electrochemical biosensors for efficient, low-cost and minimally invasive cancer screening. We discuss recent developments in nanotechnology, microfabrication, electronics integration, multiplexing, and machine learning that can help realize the potential of these sensors. However, significant interdisciplinary efforts among researchers, clinicians, regulators and the healthcare industry are still needed to tackle limitations in selectivity, size constraints, data interpretation, biomarker validation, toxicity and commercial translation. With committed resources and pragmatic strategies, electrochemical biosensors could enable routine early cancer detection and dramatically reduce the global cancer burden.