Cancer diagnostics based on plasma protein biomarkers: hard times but great expectations

Efficient isolation of encoded microparticles in a degassed micromold for highly sensitive and multiplex immunoassay with signal amplification

Multiplex detection of low-abundance protein biomarkers in biofluids can contribute to diverse biomedical fields such as early diagnosis and precision medicine. However, conventional techniques such as digital ELISA, microarray, and hydrogel-based assay still face limitations in terms of efficient protein detection due to issues with multiplexing capability, sensitivity, or complicated assay procedures. In this study, we present the degassed micromold-based particle isolation technique for highly sensitive and multiplex immunoassay with enzymatic signal amplification. Using degassing treatment of nanoporous polydimethylsiloxane (PDMS) micromold, the encoded particles are isolated in the mold within 5 min absorbing trapped air bubbles into the mold by air suction capability. Through 10 min of signal amplification in the isolated spaces by fluorogenic substrate and horseradish peroxidase labeled in the particle, the assay signal is amplified with one order of magnitude compared to that of the standard hydrogel-based assay. Using the signal amplification assay, vascular endothelial growth factor (VEGF) and chorionic gonadotropin beta (CG beta), the preeclampsia-related protein biomarkers, are quantitatively detected with a limit of detection (LoD) of 249 fg/mL and 476 fg/mL in phosphate buffer saline. The multiplex immunoassay is conducted to validate negligible non-specific detection signals and robust recovery rates in the multiplex assay. Finally, the VEGF and CG beta in real urine samples are simultaneously and quantitatively detected by the developed assay. Given the high sensitivity, multiplexing capability, and process simplicity, the presented particle isolation-based signal amplification assay holds significant potential in biomedical and proteomic fields.

Liquid biopsy: An arsenal for tumour screening and early diagnosis

Cancer has become the second leading cause of death in the world, and more than 50% of cancer patients are diagnosed at an advanced stage. Early diagnosis of tumours is the key to improving patient quality of life and survival time and reducing the socioeconomic burden. However, there is still a lack of reliable early diagnosis methods in clinical practice. In recent years, liquid biopsy technology has developed rapidly. It has the advantages of noninvasiveness, easy access to sample sources, and reproducibility. It has become the main focus of research on the early diagnosis methods of tumours. This review summarises the research progress of existing liquid biopsy markers, such as circulating tumour DNA, circulating viral DNA, DNA methylation, circulating tumour cells, circulating RNA, exosomes, and tumour education platelets in early diagnosis of tumours, and analyses the current advantages and limitations of various markers, providing a direction for the application and transformation of liquid biopsy research in early diagnosis of clinical tumours.

HIST1H2BK predicts neoadjuvant-chemotherapy response and mediates 5-fluorouracil resistance of gastric cancer cells

BACKGROUND

Neoadjuvant chemotherapy (NACT) is routinely used to treat patients with advanced gastric cancer (AGC). However, the identification of reliable markers to determine which AGC patients would benefit from NACT remains challenging.

METHODS

A systematic screening of plasma proteins between NACT-sensitive and NACT-resistant AGC patients was performed by a mass spectrometer (n = 6). The effect of the most differential plasma protein was validated in two independent cohorts with AGC patients undergoing NACT (ELISA cohort: n = 155; Validated cohort: n = 203). The expression of this candidate was examined in a cohort of AGC tissues using immunohistochemistry (n = 34). The mechanism of this candidate on 5-Fluorouracil (5-FU) resistance was explored by cell-biology experiments in vitro and vivo.

RESULTS

A series of differential plasma proteins between NACT-sensitive and NACT-resistant AGC patients was identified. Among them, plasma HIST1H2BK was validated as a significant biomarker for predicting NACT response and prognosis. Moreover, HIST1H2BK was over-expression in NACT-resistant tissues compared to NACT-sensitive tissues in AGC. Mechanistically, HIST1H2BK inhibited 5-FU-induced apoptosis by upregulating A2M transcription and then activating LRP/PI3K/Akt pathway, thereby promoting 5-FU resistance in GC cells. Intriguingly, HIST1H2BK-overexpressing 5-FU-resistant GC cells propagated resistance to 5-FU-sensitive GC cells through the secretion of HIST1H2BK.

CONCLUSION

This study highlights significant differences in plasma protein profiles between NACT-resistant and NACT-sensitive AGC patients. Plasma HIST1H2BK emerged as an effective biomarker for achieving more accurate NACT in AGC. The mechanism of intracellular and secreted HIST1H2BK on 5-FU resistance provided a novel insight into chemoresistance in AGC.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication

BACKGROUND

Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.

OBJECTIVES

This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.

METHODS

A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation.

RESULTS

The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability.

CONCLUSION

By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

Unveiling Colorectal Cancer Biomarkers: Harnessing Biosensor Technology for Volatile Organic Compound Detection

Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such as computed tomography colonography and stool tests are either too costly or less reliable than invasive ones. On the other hand, volatile organic compounds (VOCs) are potentially ideal non-invasive biomarkers for CRC detection and monitoring. The present review is a comprehensive presentation of the current state-of-the-art VOC-based CRC diagnostics, with a specific focus on recent advancements in biosensor design and application. Among them, breath-based chromatography pattern analysis and sampling techniques are overviewed, along with nanoparticle-based optical and electrochemical biosensor approaches. Limitations of the currently available technologies are also discussed with an outlook for improvement in combination with big data analytics and advanced instrumentation, as well as expanding the scope and specificity of CRC-related volatile biomarkers.

Advances in circulating tumor cells for early detection, prognosis and metastasis reduction in lung cancer

Globally, lung cancer stands as the leading type of cancer in terms of incidence and is the major source of mortality attributed to cancer. We have outlined the molecular biomarkers for lung cancer that are available clinically. Circulating tumor cells (CTCs) spread from the original location, circulate in the bloodstream, extravasate, and metastasize, forming secondary tumors by invading and establishing a favorable environment. CTC analysis is considered a common liquid biopsy method for lung cancer. We have enumerated both in vivo and ex vivo techniques for CTC separation and enrichment, examined the advantages and limitations of these methods, and also discussed the detection of CTCs in other bodily fluids. We have evaluated the value of CTCs, as well as CTCs in conjunction with other biomarkers, for their utility in the early detection and prognostic assessment of patients with lung cancer. CTCs engage with diverse cells of the metastatic process, interfering with the interaction between CTCs and various cells in metastasis, potentially halting metastasis and enhancing patient prognosis.

Integrating plasma protein-centric multi-omics to identify potential therapeutic targets for pancreatic cancer

BACKGROUND

Deciphering the role of plasma proteins in pancreatic cancer (PC) susceptibility can aid in identifying novel targets for diagnosis and treatment.

METHODS

We examined the relationship between genetically determined levels of plasma proteins and PC through a systemic proteome-wide Mendelian randomization (MR) analysis utilizing cis-pQTLs from multiple centers. Rigorous sensitivity analyses, colocalization, reverse MR, replications with varying instrumental variable selections and additional datasets, as well as subsequent meta-analysis, were utilized to confirm the robustness of significant findings. The causative effect of corresponding protein-coding genes' expression and their expression pattern in single-cell types were then investigated. Enrichment analysis, between-protein interaction and causation, knock-out mice models, and mediation analysis with established PC risk factors were applied to indicate the pathogenetic pathways. These candidate targets were ultimately prioritized upon druggability and potential side effects predicted by a phenome-wide MR.

RESULTS

Twenty-one PC-related circulating proteins were identified in the exploratory phase with no evidence for horizontal pleiotropy or reverse causation. Of these, 11 were confirmed in a meta-analysis integrating external validations. The causality at a transcription level was repeated for neutrophil elastase, hydroxyacylglutathione hydrolase, lipase member N, protein disulfide-isomerase A5, xyloside xylosyltransferase 1. The carbohydrate sulfotransferase 11 and histo-blood group ABO system transferase exhibited high-support genetic colocalization evidence and were found to affect PC carcinogenesis partially through modulating body mass index and type 2 diabetes, respectively. Approved drugs have been established for eight candidate targets, which could potentially be repurposed for PC therapies. The phenome-wide investigation revealed 12 proteins associated with 51 non-PC traits, and interference on protein disulfide-isomerase A5 and cystatin-D would increase the risk of other malignancies.

CONCLUSIONS

By employing comprehensive methodologies, this study demonstrated a genetic predisposition linking 21 circulating proteins to PC risk. Our findings shed new light on the PC etiology and highlighted potential targets as priorities for future efforts in early diagnosis and therapeutic strategies of PC.

Optical nanomaterial-based detection of biomarkers in liquid biopsy

Liquid biopsy, which is a minimally invasive procedure as an alternative to tissue biopsy, has been introduced as a new diagnostic/prognostic measure. By screening disease-related markers from the blood or other biofluids, it promises early diagnosis, timely prognostication, and effective treatment of the diseases. However, there will be a long way until its realization due to its conceptual and practical challenges. The biomarkers detected by liquid biopsy, such as circulating tumor cell (CTC) and circulating tumor DNA (ctDNA), are extraordinarily rare and often obscured by an abundance of normal cellular components, necessitating ultra-sensitive and accurate detection methods for the advancement of liquid biopsy techniques. Optical biosensors based on nanomaterials open an important opportunity in liquid biopsy because of their enhanced sensing performance with simple and practical properties. In this review article, we summarized recent innovations in optical nanomaterials to demonstrate the sensitive detection of protein, peptide, ctDNA, miRNA, exosome, and CTCs. Each study prepares the optical nanomaterials with a tailored design to enhance the sensing performance and to meet the requirements of each biomarker. The unique optical characteristics of metallic nanoparticles (NPs), quantum dots, upconversion NPs, silica NPs, polymeric NPs, and carbon nanomaterials are exploited for sensitive detection mechanisms. These recent advances in liquid biopsy using optical nanomaterials give us an opportunity to overcome challenging issues and provide a resource for understanding the unknown characteristics of the biomarkers as well as the mechanism of the disease.

Theranostic Fluorescent Probes

The ability to gain spatiotemporal information, and in some cases achieve spatiotemporal control, in the context of drug delivery makes theranostic fluorescent probes an attractive and intensely investigated research topic. This interest is reflected in the steep rise in publications on the topic that have appeared over the past decade. Theranostic fluorescent probes, in their various incarnations, generally comprise a fluorophore linked to a masked drug, in which the drug is released as the result of certain stimuli, with both intrinsic and extrinsic stimuli being reported. This release is then signaled by the emergence of a fluorescent signal. Importantly, the use of appropriate fluorophores has enabled not only this emerging fluorescence as a spatiotemporal marker for drug delivery but also has provided modalities useful in photodynamic, photothermal, and sonodynamic therapeutic applications. In this review we highlight recent work on theranostic fluorescent probes with a particular focus on probes that are activated in tumor microenvironments. We also summarize efforts to develop probes for other applications, such as neurodegenerative diseases and antibacterials. This review celebrates the diversity of designs reported to date, from discrete small-molecule systems to nanomaterials. Our aim is to provide insights into the potential clinical impact of this still-emerging research direction.

Paper-Based Analytical Devices for Cancer Liquid Biopsy

Liquid biopsies have caused a significant revolution in cancer diagnosis, and the use of point of care (PoC) platforms has the potential to bring liquid biopsy-based cancer detection closer to patients. These platforms provide rapid and on-site analysis by reducing the time between sample collection and results output. The aim of this tutorial content is to provide readers an in-depth understanding regarding the choice of the ideal sensing platform suitable for specific cancer-related biomarkers.

An Atlas of Dietary Intakes and Medication Uses on Risk of Bladder Cancer: A Wide-Angle Mendelian Randomization Analysis

BACKGROUND

Observational studies suggests that diets and medications affect bladder cancer (BC) development, which are subject to confounding and difficult to make causal inference. Here we aimed to investigate whether those observational associations are causal and determining the potential directions and pathways.

METHODS

We used 2-sample Mendelian randomization (MR) analysis to assess associations of dietary intakes, medication uses and molecules with BC risk. Genetic summary data were derived from participants of predominantly European ancestry with rigorous instruments selection, where univariable MR, mediation MR and multivariable MR were performed.

RESULTS

The results of univariable MR showed 4 dietary intakes and 4 medication uses having a protective effect on BC, while 4 circulating metabolites, 440 circulating proteins and 2 gut microbes were observed to be causally associated with BC risk. Through mediation MR, we found 572 analytes showing consistent mediating effects between dietary intakes or medication uses and BC risk. Furthermore, 9 out of 16 diet-medication pairs showed significant interactions and alterations on BC when consumed jointly.

CONCLUSION

In summary, the findings obtained from the current study have important implications for informing prevention strategies that point to potential lifestyle interventions or medication prescriptions to reduce the risk of developing BC. HighlightsThe current study extends observational literature in showing the importance of diets and medications on bladder cancer prevention.The associations of diets and medications on bladder cancer prevention might be through circulating metabolites, circulating proteins and gut microbiotaOur results provide a new understanding of interactions in certain diet-medication pairs which should be taken into account by both physicians and patients during the development of a treatment strategy.

timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Peptide absent sequences emerging in human cancers

Early diagnosis of cancer can significantly improve survival of cancer patients; however sensitive and highly specific biomarkers for cancer detection are currently lacking for most cancer types. Nullpeptides are short peptides that are absent from the human proteome. Here, we examined the emergence of nullpeptides during cancer development. We analyzed 3,600,964 somatic mutations across 10,064 whole exome sequencing tumor samples spanning 32 cancer types. We analyze RNA-seq data from primary tumor samples to identify the subset of nullpeptides that emerge in highly expresed genes. We show that nullpeptides, and particularly the subset that is highly recurrent across cancer patients, can be identified in tumor biopsy samples. We find that cancer genes show an excess of nullpeptides and detect nullpeptide hotspots in specific loci of oncogenes and tumor suppressors. We also observe that recurrent nullpeptides are more likely to be found in neoantigens, which have been shown to be effective targets for immunotherapy, suggesting that they can be used to prioritize candidates. Our findings provide evidence for the utility of nullpeptides as cancer detection and therapeutic biomarkers.

Applications and advancements of CRISPR-Cas in the treatment of lung cancer

Lung cancer is one of the most malignant diseases and a major contributor to cancer-related deaths worldwide due to the deficiency of early diagnosis and effective therapy that are of great importance for patient prognosis and quality of life. Over the past decade, the advent of clustered regularly interspaced short palindromic repeats/CRISPR associated protein (CRISPR/Cas) system has significantly propelled the progress of both fundamental research and clinical trials of lung cancer. In this review, we review the current applications of the CRISPR/Cas system in diagnosis, target identification, and treatment resistance of lung cancer. Furthermore, we summarize the development of lung cancer animal models and delivery methods based on CRISPR system, providing novel insights into clinical diagnosis and treatment strategies of lung cancer.

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

BACKGROUND

Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without.

METHODS

The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression.

RESULTS

The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve.

CONCLUSION

These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.

Proteomics evaluation of five economical commercial abundant protein depletion kits for enrichment of diseases-specific biomarkers from blood serum

Blood serum is arguably the most analyzed biofluid for disease prediction and diagnosis. Herein, we benchmarked five different serum abundant protein depletion (SAPD) kits with regard to the identification of disease-specific biomarkers in human serum using bottom-up proteomics. As expected, the IgG removal efficiency among the SAPD kits is highly variable, ranging from 70% to 93%. A pairwise comparison of database search results showed a 10%-19% variation in protein identification among the kits. Immunocapturing-based SAPD kits against IgG and albumin outperformed the others in the removal of these two abundant proteins. Conversely, non-antibody-based methods (i.e., kits using ion exchange resins) and kits leveraging a multi-antibody approach were proven to be less efficient in depleting IgG/albumin from samples but led to the highest number of identified peptides. Notably, our results indicate that different cancer biomarkers could be enriched up to 10% depending on the utilized SAPD kit compared with the undepleted sample. Additionally, functional analysis of the bottom-up proteomic results revealed that different SAPD kits enrich distinct disease- and pathway-specific protein sets. Overall, our study emphasizes that a careful selection of the appropriate commercial SAPD kit is crucial for the analysis of disease biomarkers in serum by shotgun proteomics.

Proteome-wide mendelian randomization study implicates therapeutic targets in common cancers

BACKGROUND

The interest in targeted cancer therapies has been growing rapidly. While numerous cancer biomarkers and targeted treatment strategies have been developed and employed, there are still significant limitations and challenges in the early diagnosis and targeted treatment of cancers. Accordingly, there is an urgent need to identify novel targets and develop new targeted drugs.

METHODS

The study was conducted using combined cis-Mendelian randomization (cis-MR) and colocalization analysis. We analyzed data from 732 plasma proteins to identify potential drug targets associated with eight site-specific cancers. These findings were further validated using the UK Biobank dataset. Then, a protein-protein interaction network was also constructed to examine the interplay between the identified proteins and the targets of existing cancer medications.

RESULTS

This MR analysis revealed associations between five plasma proteins and prostate cancer, five with breast cancer, and three with lung cancer. Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. Our study indicatied that genetically predicted KDELC2 (OR: 0.89, 95% CI 0.86-0.93) and TNFRSF10B (OR: 0.74, 95% CI 0.65-0.83) are inversely associated with prostate cancer. Furthermore, we observed an inverse association between CPNE1 (OR: 0.96, 95% CI 0.94-0.98) and breast cancer, while PDIA3 (OR: 1.19, 95% CI 1.10-1.30) were found to be associated with the risk of breast cancer. In addition, we also propose that SPINT2 (OR: 1.05, 95% CI 1.03-1.06), GSTP1 (OR: 0.82, 95% CI 0.74-0.90), and CTSS (OR: 0.91, 95% CI 0.88-0.95) may serve as potential therapeutic targets in prostate cancer. Similarly, GDI2 (OR: 0.85, 95% CI 0.80-0.91), ISLR2 (OR: 0.87, 95% CI 0.82-0.93), and CTSF (OR: 1.14, 95% CI 1.08-1.21) could potentially be targets for breast cancer. Additionally, we identified SFTPB (OR: 0.93, 95% CI 0.91-0.95), ICAM5 (OR: 0.95, 95% CI 0.93-0.97), and FLRT3 (OR: 1.10, 95% CI 1.05-1.15) as potential targets for lung cancer. Notably, TNFRSF10B, GSTP1, and PDIA3 were found to interact with the target proteins of current medications used in prostate or breast cancer treatment.

CONCLUSIONS

This comprehensive analysis has highlighted thirteen plasma proteins with potential roles in three site-specific cancers. Continued research in this area may reveal their therapeutic potential, particularly KDELC2, TNFRSF10B, CPNE1, and PDIA3, paving the way for more effective cancer treatments.

Circulating tumour cells for early detection of clinically relevant cancer

Given that cancer mortality is usually a result of late diagnosis, efforts in the field of early detection are paramount to reducing cancer-related deaths and improving patient outcomes. Increasing evidence indicates that metastasis is an early event in patients with aggressive cancers, often occurring even before primary lesions are clinically detectable. Metastases are usually formed from cancer cells that spread to distant non-malignant tissues via the blood circulation, termed circulating tumour cells (CTCs). CTCs have been detected in patients with early stage cancers and, owing to their association with metastasis, might indicate the presence of aggressive disease, thus providing a possible means to expedite diagnosis and treatment initiation for such patients while avoiding overdiagnosis and overtreatment of those with slow-growing, indolent tumours. The utility of CTCs as an early diagnostic tool has been investigated, although further improvements in the efficiency of CTC detection are required. In this Perspective, we discuss the clinical significance of early haematogenous dissemination of cancer cells, the potential of CTCs to facilitate early detection of clinically relevant cancers, and the technological advances that might improve CTC capture and, thus, diagnostic performance in this setting.

Liquid Biopsies, Novel Approaches and Future Directions

Cancer is among the leading causes of death worldwide. Early diagnosis and prognosis are vital to improve patients' outcomes. The gold standard of tumor characterization leading to tumor diagnosis and prognosis is tissue biopsy. Amongst the constraints of tissue biopsy collection is the sampling frequency and the incomplete representation of the entire tumor bulk. Liquid biopsy approaches, including the analysis of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating miRNAs, and tumor-derived extracellular vesicles (EVs), as well as certain protein signatures that are released in the circulation from primary tumors and their metastatic sites, present a promising and more potent candidate for patient diagnosis and follow up monitoring. The minimally invasive nature of liquid biopsies, allowing frequent collection, can be used in the monitoring of therapy response in real time, allowing the development of novel approaches in the therapeutic management of cancer patients. In this review we will describe recent advances in the field of liquid biopsy markers focusing on their advantages and disadvantages.

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography-tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073-2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Liquid biopsy at the frontier in renal cell carcinoma: recent analysis of techniques and clinical application

Renal cell carcinoma (RCC) is a major pathological type of kidney cancer and is one of the most common malignancies worldwide. The unremarkable symptoms of early stages, proneness to postoperative metastasis or recurrence, and low sensitivity to radiotherapy and chemotherapy pose a challenge for the diagnosis and treatment of RCC. Liquid biopsy is an emerging test that measures patient biomarkers, including circulating tumor cells, cell-free DNA/cell-free tumor DNA, cell-free RNA, exosomes, and tumor-derived metabolites and proteins. Owing to its non-invasiveness, liquid biopsy enables continuous and real-time collection of patient information for diagnosis, prognostic assessment, treatment monitoring, and response evaluation. Therefore, the selection of appropriate biomarkers for liquid biopsy is crucial for identifying high-risk patients, developing personalized therapeutic plans, and practicing precision medicine. In recent years, owing to the rapid development and iteration of extraction and analysis technologies, liquid biopsy has emerged as a low cost, high efficiency, and high accuracy clinical detection method. Here, we comprehensively review liquid biopsy components and their clinical applications over the past 5 years. Additionally, we discuss its limitations and predict its future prospects.

Liquid biopsies: the future of cancer early detection

Cancer is a worldwide pandemic. The burden it imposes grows steadily on a global scale causing emotional, physical, and financial strains on individuals, families, and health care systems. Despite being the second leading cause of death worldwide, many cancers do not have screening programs and many people with a high risk of developing cancer fail to follow the advised medical screening regime due to the nature of the available screening tests and other challenges with compliance. Moreover, many liquid biopsy strategies being developed for early detection of cancer lack the sensitivity required to detect early-stage cancers. Early detection is key for improved quality of life, survival, and to reduce the financial burden of cancer treatments which are greater at later stage detection. This review examines the current liquid biopsy market, focusing in particular on the strengths and drawbacks of techniques in achieving early cancer detection. We explore the clinical utility of liquid biopsy technologies for the earlier detection of solid cancers, with a focus on how a combination of various spectroscopic and -omic methodologies may pave the way for more efficient cancer diagnostics.

Metal-Organic Framework-Mediated Bioorthogonal Reaction to Immobilize Bacteria for Ultrasensitive Fluorescence Counting Immunoassays

Ultrasensitive quantification of protein biomarkers has significant implications in disease diagnosis. Herein, we report a fluorescent bacteria counting immunoassay (FBCIA) strategy for protein biomarker detection based on a cascade signal conversion and amplification strategy including the copper metal-organic framework (Cu-MOF)-mediated Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) for fluorescent bacteria immobilization that converted the concentration of target protein to countable bacterial number and the further self-proliferation of bacteria to amplify the detectable bacterial number. The developed low-background and enzyme-free cascade methodology achieved highly sensitive detection of carcinoembryonic antigen (CEA) and prostate-specific antigen (PSA) with detection limits down to 0.8 pg/mL and 64.5 fg/mL, respectively. On top of that, we also developed a smartphone device for visualizing individual bacteria and point-of-care counting of the resulting bacteria for the detection of clinical samples. The good consistency between FBCIA and clinical enzyme-linked immunosorbent assay (ELISA) validated the high reliability and promising potential of our developed platform in clinical applications.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

Diagnostic role of plasma ORM2 in differentiating prostate cancer from benign prostatic hyperplasia

PURPOSE

Markers are needed to increase the diagnostic accuracy of prostate-specific antigen (PSA) in prostate cancer (PCa) screening. Mounting evidence has shown that plasma proteins can be hopeful biomarkers for cancer diagnosis.

METHODS

Tandem mass tag (TMT)-based proteomics and parallel reaction monitoring (PRM) analysis were used to screen the differential proteins and further validated in other independent studies (n = 539). Receiver-operating characteristic (ROC), decision curves and nomograms were applied to assess the diagnostic accuracy of biomarkers.

RESULTS

Three candidate proteins (DBP, LCAT and ORM2) were preliminarily screened. Subsequent validation studies revealed significant upregulation of ORM2 in PCa patients across other independent cohorts. ORM2 yielded excellent discriminative power for PCa from benign prostatic hyperplasia (BPH) patients (AUC = 0.861 and 0.814 in validation phases 2a and 2b, respectively). Importantly, the combination of ORM2 and PSA gave better predictive accuracy than PSA alone. We incorporated age, PSA and ORM2 into a nomogram, which yielded C-index of 0.883 in validation phase 2a. A similar C-index of 0.879 was obtained in external validation phase 2b.

CONCLUSIONS

In summary, our study suggests that ORM2 could be treated as a complementary biomarker for PSA in distinguishing PCa from BPH.

Recent Advances on the Metal-Organic Frameworks-Based Biosensing Methods for Cancer Biomarkers Detection

Sensitive and selective detection of cancer biomarkers is crucial for early diagnosis and treatment of cancer, one of the most dangerous diseases in the world. Metal-organic frameworks (MOFs), a class of hybrid porous materials fabricated through the assembly of metal ions/clusters and organic ligands, have attracted increasing attention in the sensing of cancer biomarkers, due to the advantages of adjustable size, high porosity, large surface area and ease of modification. MOFs have been utilized to not only fabricate active sensing interfaces but also arouse a variety of measurable signals. Several representative analytical technologies have been applied in MOF-based biosensing strategies to ensure high detection sensitivity toward cancer biomarkers, such as fluorescence, electrochemistry, electrochemiluminescence, photochemistry and colorimetric methods. In this review, we summarized recent advances on MOFs-based biosensing strategies for the detection of cancer biomarkers in recent three years based on the categories of metal nodes, and aimed to provide valuable references for the development of innovative biosensing platform for the purpose of clinical diagnosis.

Use of Cysteamine and Glutaraldehyde Chemicals for Robust Functionalization of Substrates with Protein Biomarkers—An Overview on the Construction of Biosensors with Different Transductions

Currently, several biosensors are reported to confirm the absence/presence of an abnormal level of specific human biomarkers in research laboratories. Unfortunately, public marketing and/or pharmacy accessibility are not yet possible for many bodily fluid biomarkers. The questions are numerous, starting from the preparation of the substrates, the wet/dry form of recognizing the (bio)ligands, the exposure time, and the choice of the running buffers. In this context, for the first time, the present overview summarizes the pre-functionalization of standard and nanostructured solid/flexible supports with cysteamine (Cys) and glutaraldehyde (GA) chemicals for robust protein immobilization and detection of biomarkers in body fluids (serum, saliva, and urine) using three transductions: piezoelectrical, electrochemical, and optical, respectively. Thus, the reader can easily access and compare step-by-step conjugate protocols published over the past 10 years. In conclusion, Cys/GA chemistry seems widely used for electrochemical sensing applications with different types of recorded signals, either current, potential, or impedance. On the other hand, piezoelectric detection via quartz crystal microbalance (QCM) and optical detection by surface plasmon resonance (LSPR)/surface-enhanced Raman spectroscopy (SERS) are ultrasensitive platforms and very good candidates for the miniaturization of medical devices in the near future.

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R² > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Novel Field-Based Protein Detection Method Using Light Transmission Spectroscopy and Antibody Functionalized Gold Nanoparticles

Protein detection is a universal tool critical to many applications in medicine, agriculture, and biotechnology. We developed a novel protein detection method combining light transmission spectroscopy and particle-size analysis of gold nanospheres monovalently functionalized with polyclonal antibodies and applied it to an emerging challenge for such technologies─the monitoring of environmental proteins (eProteins) present in natural aquatic systems. These are an underreported source of pollution and include the pseudopersistent Cry toxins that enter aquatic ecosystems from surrounding genetically engineered crops. The assay is capable of detecting proteins in complex matrices, such as water samples collected in the field, making it a competitive assay for eProtein detection. It is sensitive, reaching 1.25 ng mL^-1, and we demonstrate its application to the detection of Cry1Ab from subsurface tile-drain and streamwater samples from agricultural waterways. The assay can also be quickly adapted for other protein detection applications in the future.

Spatially hierarchical nano-architecture for real time detection of Interleukin-8 cancer biomarker

In the present work we have developed two hierarchical nano-architectures based electrochemical immunosensors for the detection of interleukin-8 (IL-8) cytokine tumor biomarker. A comparative study has been performed for spatial nano-architectures and their relative sensing to establish the model for real time monitoring. With the first platform, the recognition layer consisted with immobilised IL-8 on aminothiol modified gold electrodes. In the second approach, the activated multi walled carbon nanotubes (MWCNT-COOH) were added in the functionalisation process by covalent attachment between the functionalities NH₂ of aminothiol and the functionalities COOH of carbon nanotubes. The surface topology of the recognition layer has been characterised by atomic force spectroscopy (AFM) and contact angle (CA) measurements. The electrochemical response of the developed sensor was measured by electrochemical impedance spectroscopy (EIS). A side-by-side comparison showed that aminothiol/activated MWCNTs/anti-IL-8 based impedimetric immunosensor exhibits high reproducibility (The relative standard deviation (R.S.D) = 3.2%, n = 3) with high stability. The present sensor allows evaluating a lower detection limit of 0.1 pg mL^-1 with a large dynamic sensitivity range from 1 pg mL^-1to 1000 pg mL^-1 covering the entire clinical therapeutic window. The developed MWCNTs based immunosensor has been calibrated by determining IL-8 in artificial plasma and showed a selective response to IL-8 even in the interfering environment of other cytokines such as Interleukin-1 (IL-1) and Interleukin-6 (IL-6).

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Engineering an Au nanostar-based liquid phase interfacial ratiometric SERS platform with programmable entropy-driven DNA circuits to detect protein biomarkers in clinical samples

Developing sensing platforms that simultaneously integrate high sensitivity and accuracy has been a promising but challenging task for the detection of protein biomarkers in clinical samples. Herein, we engineered an Au nanostar-based liquid phase interfacial ratiometric SERS platform with programmable entropy-driven DNA circuits to detect the protein biomarker Mucin 1 (MUC1) in clinical samples.

Variation in Plasma Levels of TRAF2 Protein During Development of Squamous Cell Carcinoma of the Oral Tongue

As early detection is crucial for improvement of cancer prognosis, we searched for biomarkers in plasma from individuals who later developed squamous cell carcinoma of the oral tongue (SCCOT) as well as in patients with an already established SCCOT. Levels of 261 proteins related to inflammation and/or tumor processes were measured using the proximity extension assay (PEA) in 179 plasma samples (42 collected before diagnosis of SCCOT with 81 matched controls; 28 collected at diagnosis of SCCOT with 28 matched controls). Statistical modeling tools principal component analysis (PCA) and orthogonal partial least square - discriminant analysis (OPLS-DA) were applied to provide insights into separations between groups. PCA models failed to achieve group separation of SCCOT patients from controls based on protein levels in samples taken prior to diagnosis or at the time of diagnosis. For pre-diagnostic samples and their controls, no significant OPLS-DA model was identified. Potentials for separating pre-diagnostic samples collected up to five years before diagnosis (n = 15) from matched controls (n = 28) were seen in four proteins. For diagnostic samples and controls, the OPLS-DA model indicated that 21 proteins were important for group separation. TNF receptor associated factor 2 (TRAF2), decreased in pre-diagnostic plasma (< 5 years) but increased at diagnosis, was the only protein showing altered levels before and at diagnosis of SCCOT (p-value < 0.05). Taken together, changes in plasma protein profiles at diagnosis were evident, but not reliably detectable in pre-diagnostic samples taken before clinical signs of tumor development. Variation in protein levels during cancer development poses a challenge for the identification of biomarkers that could predict SCCOT development.

Uncovering the Depths of the Human Proteome: Antibody-based Technologies for Ultrasensitive Multiplexed Protein Detection and Quantification

Probing the human proteome in tissues and biofluids such as plasma is attractive for biomarker and drug target discovery. Recent breakthroughs in multiplex, antibody-based, proteomics technologies now enable the simultaneous quantification of thousands of proteins at as low as sub fg/ml concentrations with remarkable dynamic ranges of up to 10-log. We herein provide a comprehensive guide to the methodologies, performance, technical comparisons, advantages, and disadvantages of established and emerging technologies for the multiplexed ultrasensitive measurement of proteins. Gaining holistic knowledge on these innovations is crucial for choosing the right multiplexed proteomics tool for applications at hand to critically complement traditional proteomics methods. This can bring researchers closer than ever before to elucidating the intricate inner workings and cross talk that spans multitude of proteins in disease mechanisms.