Using All Our Genomes: Blood-based Liquid Biopsies for the Early Detection of Cancer

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Mucosa‑associated lymphoid tissue lymphoma translocation protein 1 inhibitor, MI‑2, attenuates non‑small cell lung cancer cell proliferation, migration and invasion, and promotes apoptosis by suppressing the JNK/c‑JUN pathway

Mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) inhibitors are effective in attenuating the progression of several types of cancer. However, their role in lung cancer requires further investigation. Therefore, the present study aimed to explore the effect of the MALT1 inhibitor, MI-2, on the behavior of non-small cell lung cancer (NSCLC) cells and to uncover their possible underlying mechanism of action. The mRNA and protein expression levels of MALT1 were detected in the human normal lung epithelial cell line BEAS-2B, and the NSCLC cell lines, NCI-H1299, NCI-H1650, HCC827, A549 and NCI-H23. Subsequently, NCI-H1650 and A549 cells were treated with MI-2. Additionally, NCI-H1650 and A549 cells were co-treated with anisomycin, a c-JUN N-terminal kinase (JNK) pathway activator, with or without MI-2. The results illustrated that the mRNA and protein expression levels of MALT1 were significantly increased in NCI-H1299, NCI-H1650, A549 and NCI-H23 cells compared with those in BEAS-2B cells. Treatment of NCI-H1650 and A549 cells with MI-2 for 72 h reduced the optical density value as determined using the Cell Counting Kit-8 assay. Consistently, the 5-ethynyl-2'-deoxyuridine assay also showed that proliferation was reduced in MI-2-treated NSCLC cells. In addition, MI-2 downregulated B-cell lymphoma 2 (BCL2), and enhanced BCL2-associated X-protein expression and apoptotic rate in NCI-H1650 and A549 cells. These findings indicated that MI-2 could inhibit NCI-H1650 and A549 cell proliferation and promote apoptosis. Furthermore, treatment of cells with MI-2 only attenuated the migration and invasion of NCI-H1650 cells. Notably, MI-2 decreased the expression levels of phosphorylated (p)-JNK and p-c-JUN in NCI-H1650 and A549 cells, thus suggesting that MI-2 could suppress the JNK/c-JUN signaling pathway. However, NSCLC cell co-treatment with anisomycin (JNK pathway activator) reversed the effect of MI-2 on the proliferation, apoptosis and activation of the JNK/c-JUN pathway in NCI-H1650 and A549 cells. In conclusion, the present study demonstrated that the MALT1 inhibitor, MI-2, could suppress NSCLC cell proliferation, migration and invasion, and induce apoptosis via inactivating the JNK/c-JUN pathway.

An Inducible CRISPR-dCas9-Based Transcriptional Repression System for Cancer Therapy

Gene therapy has been adapted for improving malignant tumor treatment. However, pharmacotherapies targeting cancer remain limited and are generally inapplicable for rare disease patients. Oleanolic acid (OA) is a plant-derived triterpenoid that is frequently used in Chinese medicine as a safe but slow-acting treatment for many disorders. Here, the congruent pharmacological activities of OA and CRISPR-dCas9 in targeting AURKA or KDM1A and improving disease-specific prognosis and used a synthetic-biology-inspired design principle to engineer a therapeutic gene circuit that enables a concerted action of both drugs are utilized. In particular, the OA-triggered CRISPR-dCas9 transcriptional repression system rapidly and simultaneously attenuated lung and thyroid cancer. Collectively, this work shows that rationally engineered synthetic gene circuits are capable of treating multifactorial diseases in a synergistic manner by multiplexing the targeting efficiencies of single therapeutics.

Construction of lung cancer serum markers based on ReliefF feature selection

Serum miRNAs are available clinical samples for cancer screening. Identifying early serum markers in lung cancer (LC) is essential for patients' early diagnosis and clinical treatment. Expression data of serum miRNAs of lung adenocarcinoma (LUAD) patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO). These data were normalized and subjected to differential expression analysis to obtain differentially expressed miRNAs (DEmiRNAs). The DEmiRNAs were subsequently subjected to ReliefF feature selection, and subsets closely related to cancer were screened as candidate feature miRNAs. Thereafter, a Gaussian Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifier were constructed based on these candidate feature miRNAs. Then the best diagnostic signature was constructed through NB combined with incremental feature selection (IFS). Thereafter, these samples were subjected to principal component analysis (PCA) based on miRNAs with optimal predictive performance. Finally, the peripheral serum miRNAs of 64 LUAD patients and 59 normal individuals were extracted for qRT-PCR analysis to validate the performance of the diagnostic model in respect of clinical detection. Finally, according to area under the curve (AUC) and accuracy values, the NB classifier composed of miR-5100 and miR-663a manifested the most outstanding diagnostic performance. The PCA results also revealed that the 2-miRNA diagnostic signature could effectively distinguish cancer patients from healthy individuals. Finally, qRT-PCR results of clinical serum samples revealed that miR-5100 and miR-663a expression in tumor samples was remarkably higher than that in normal samples. The AUC of the 2-miRNA diagnostic signature was 0.968. In summary, we identified markers (miR-5100 and miR-663a) in serum for early LUAD screening, providing ideas for developing early LUAD diagnostic models.

Constraint Coupling of Redox Cascade and Electron Transfer Synchronization on Electrode-Nanosensor Interface for Repeatable Detection of Tumor Biomarkers

Quantitative analysis of up-regulated biomarkers in pathological tissues is helpful to tumor surgery yet the loss of biomarker extraction and time-consuming operation limited the accurate and quick judgement in preoperative or intraoperative diagnosis. Herein, an immobilization-free electrochemical sensing platform is developed by constraint coupling of electron transfer cascade on electrode-nanosensor interface. Specifically, electrochemical indicator (Ri)-labeled single-stranded DNA on electroactive nanodonor (polydopamine, PDA) can be responsively detached by formation of DNA complex through the recognition and binding with targets. By applying the oxidation potential of Ri, nanosensor collisions on electrode surface trigger a cascade redox cycling of PDA and Ri through synchronous electron transfer, which boost the amplification of current signal output. The developed nanosensor exhibit excellent linear response toward up-regulated biomarkers (miRNA-21, ATP, and VEGF) with low detection limits (32 fM, 386 pM, and 2.8 pM). Moreover, background influence from physiological interferent is greatly reduced by restricted electron transfer coupling on electrode. The practical applicability is illustrated in sensitive and highly repeatable profiling of miRNA-21 in lysate of tumor cells and tumor tissue, beneficial for more reliable diagnosis. This electrochemical platform by employing electron transfer cascades at heterogeneous interfaces offers a route to anti-interference detection of biomarkers in tumor tissues.

The Present and Future Landscapes of Molecular Diagnostics

Nucleic acid testing is the cornerstone of modern molecular diagnostics. This review describes the current status and future directions of molecular diagnostics, focusing on four major techniques: polymerase chain reaction (PCR), next-generation sequencing (NGS), isothermal amplification methods such as recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR)-based detection methods. We explore the advantages and limitations of each technique, describe how each overlaps with or complements other techniques, and examine current clinical offerings. This review provides a broad perspective into the landscape of molecular diagnostics and highlights potential future directions in this rapidly evolving field.

Entrectinib in ROS1-positive advanced non-small cell lung cancer: the phase 2/3 BFAST trial

Although comprehensive biomarker testing is recommended for all patients with advanced/metastatic non-small cell lung cancer (NSCLC) before initiation of first-line treatment, tissue availability can limit testing. Genomic testing in liquid biopsies can be utilized to overcome the inherent limitations of tissue sampling and identify the most appropriate biomarker-informed treatment option for patients. The Blood First Assay Screening Trial is a global, open-label, multicohort trial that evaluates the efficacy and safety of multiple therapies in patients with advanced/metastatic NSCLC and targetable alterations identified by liquid biopsy. We present data from Cohort D (ROS1-positive). Patients ≥18 years of age with stage IIIB/IV, ROS1-positive NSCLC detected by liquid biopsies received entrectinib 600 mg daily. At data cutoff (November 2021), 55 patients were enrolled and 54 had measurable disease. Cohort D met its primary endpoint: the confirmed objective response rate (ORR) by investigator was 81.5%, which was consistent with the ORR from the integrated analysis of entrectinib (investigator-assessed ORR, 73.4%; data cutoff May 2019, ≥12 months of follow-up). The safety profile of entrectinib was consistent with previous reports. These results demonstrate consistency with those from the integrated analysis of entrectinib in patients with ROS1-positive NSCLC identified by tissue-based testing, and support the clinical value of liquid biopsies to inform clinical decision-making. The integration of liquid biopsies into clinical practice provides patients with a less invasive diagnostic method than tissue-based testing and has faster turnaround times that may expedite the reaching of clinical decisions in the advanced/metastatic NSCLC setting. ClinicalTrials.gov registration: NCT03178552 .

Cell Membrane Perforation: Patterns, Mechanisms and Functions

Cell membrane is crucial for the cellular activities, and any disruption to it may affect the cells. It is demonstrated that cell membrane perforation is associated with some biological processes like programmed cell death (PCD) and infection of pathogens. Specific developments make it a promising technique to perforate the cell membrane controllably and precisely. The pores on the cell membrane provide direct pathways for the entry and exit of substances, and can also cause cell death, which means reasonable utilization of cell membrane perforation is able to assist intracellular delivery, eliminate diseased or cancerous cells, and bring about other benefits. This review classifies the patterns of cell membrane perforation based on the mechanisms into 1) physical patterns, 2) biological patterns, and 3) chemical patterns, introduces the characterization methods and then summarizes the functions according to the characteristics of reversible and irreversible pores, with the aim of providing a comprehensive summary of the knowledge related to cell membrane perforation and enlightening broad applications in biomedical science.

Partial remission with sintilimab monotherapy in a patient carrying a CD274 amplification in refractory diffuse large B‑cell lymphoma: A case report

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with varying characteristics, in terms of genomic variation, cell morphology and clinical presentation. At present, only ~66% of patients are cured with initial treatment and those with refractory DLBCL exhibit a poor prognosis. Thus, further investigations into novel effective treatment options for DLBCL are required. The present study reports the case of a patient resistant to multiple therapies, including rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) plus enzastaurin (trial no. CTR20171560), GemOx plus lenalidomide and selinexor (trial no. ATG-010-DLBCL-001). The patient harbored a CD274 amplification, as identified via next-generation sequencing (NGS), and exhibited a high programmed death-ligand 1 Tumor Proportion Score of up to 95%. Consequently, the patient was treated with sintilimab monotherapy and the response lasted for 12 months of follow-up without major immune-related adverse events. This case highlights the role of NGS technology in selecting treatment options for refractory DLBCL. Furthermore, the results of the present study suggest that sintilimab may have potential in the treatment of patients with refractory DLBCL.

Role of angiomotin family members in human diseases (Review)

Angiomotin (Amot) family members, including Amot, Amot-like protein 1 (Amotl1) and Amot-like protein 2 (Amotl2), have been found to interact with angiostatins. In addition, Amot family members are involved in various physiological and pathological functions such as embryonic development, angiogenesis and tumorigenesis. Some studies have also demonstrated its regulation in signaling pathways such as the Hippo signaling pathway, AMPK signaling pathway and mTOR signaling pathways. Amot family members play an important role in neural stem cell differentiation, dendritic formation and synaptic maturation. In addition, an increasing number of studies have focused on their function in promoting and/or suppressing cancer, but the underlying mechanisms remain to be elucidated. The present review integrated relevant studies on upstream regulation and downstream signals of Amot family members, as well as the latest progress in physiological and pathological functions and clinical applications, hoping to offer important ideas for further research.

Intrapleural perfusion hyperthermia improves the efficiency of anti‑PD1 antibody‑based therapy for lung adenocarcinoma: A case report

Chemotherapy based on intrapleural perfusion hyperthermia (IPH) can markedly improve the sensitivity of lung adenocarcinoma cells to anti-programmed cell death receptor 1 (PD1) antibody adjuvant chemotherapy and enhance the clinical response of a patient. In the present study, a unique case of a patient who failed to respond to immunotherapy combined with chemotherapy but achieved prolonged stable disease after treatment with IPH and subsequent sintilimab-based treatment, is reported. A 50-year-old Chinese female patient was admitted to a regional cancer hospital presenting with hemoptysis and persistent fever. The findings of computed tomography imaging and thoracic puncture tissue biopsy indicated a diagnosis of adenocarcinoma. The TNM and clinical stage were identified as cT2N3M0 and stage IIIB, respectively. Immunohistochemical tests showed the expression of programmed death-ligand 1 (PD-L1) with a tumor proportion score of 2%. No other classic genetic alterations were detected. Initially, sintilimab-based chemotherapy at 200 mg was administered, for three cycles from April 2020, and increased pleural effusion was observed on the left side. The best overall response (BOR) assessment of the local lesion was progressive disease. IPH combined with chemotherapy was then carried out from August to September 2020, after which the same course of sintilimab-based chemotherapy as aforementioned was provided from October 2020 to September 2023. The BOR evaluation results during the monotherapy courses were all judged as stable disease. Therefore, it was concluded that IPH can substantially improve the efficiency of anti-PD1 antibody-based therapy for lung adenocarcinoma.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Genetic Signature of Human Pancreatic Cancer and Personalized Targeting

Pancreatic cancer is a highly lethal disease with a 5-year survival rate of around 11-12%. Surgery, being the treatment of choice, is only possible in 20% of symptomatic patients. The main reason is that when it becomes symptomatic, IT IS the tumor is usually locally advanced and/or has metastasized to distant organs; thus, early diagnosis is infrequent. The lack of specific early symptoms is an important cause of late diagnosis. Unfortunately, diagnostic tumor markers become positive at a late stage, and there is a lack of early-stage markers. Surgical and non-surgical cases are treated with neoadjuvant and/or adjuvant chemotherapy, and the results are usually poor. However, personalized targeted therapy directed against tumor drivers may improve this situation. Until recently, many pancreatic tumor driver genes/proteins were considered untargetable. Chemical and physical characteristics of mutated KRAS are a formidable challenge to overcome. This situation is slowly changing. For the first time, there are candidate drugs that can target the main driver gene of pancreatic cancer: KRAS. Indeed, KRAS inhibition has been clinically achieved in lung cancer and, at the pre-clinical level, in pancreatic cancer as well. This will probably change the very poor outlook for this disease. This paper reviews the genetic characteristics of sporadic and hereditary predisposition to pancreatic cancer and the possibilities of a personalized treatment according to the genetic signature.

A Ratiometric Gene-Switch System for miRNA Sensing and Gene Regulation

microRNAs (miRNAs) are a class of non-coding, small RNAs that play an important role in diverse biological processes and diseases. By regulating the expression of eukaryotic genes post-transcriptionally in a sequence-specific manner, miRNAs are widely used to design synthetic RNA switches. However, most of the RNA switches are often dependent on the corresponding ligand molecules, whose specificity and concentration would affect the efficiency of synthetic RNA circuits. Here, a fused transcriptional repressor Gal4BD-Rluc based gene-switch system Gal-miR for miRNA visualization and gene regulation is described. By placing a luciferase downstream gene under the control of endogenous miRNA machinery, the Gal-miR system makes the conversion of miRNA-mediated gene silencing into a ratiometric bioluminescent signal, which quantitatively reflected miRNA-206 activity during myogenic differentiation. Moreover, it demonstrates that this gene-switch system can effectively inhibit breast cancer cell viability, migration and invasion under the control of specific miRNAs by replacing the downstream gene with melittin functional gene. The study proposes a powerful modular genetic design for achieving precise control of transgene expression in a miRNA responsive way, as well as visualizing the dynamics of miRNA activity.

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

AuNPs/CNC Nanocomposite with A "Dual Dispersion" Effect for LDI-TOF MS Analysis of Intact Proteins in NSCLC Serum Exosomes

Detecting exosomal markers using laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) is a novel approach for examining liquid biopsies of non-small cell lung cancer (NSCLC) samples. However, LDI-TOF MS is limited by low sensitivity and poor reproducibility when analyzing intact proteins directly. In this report, gold nanoparticles/cellulose nanocrystals (AuNPs/CNC) is introduced as the matrix for direct analysis of intact proteins in NSCLC serum exosomes. AuNPs/CNC with "dual dispersion" effects dispersed and stabilized AuNPs and improved ion inhibition effects caused by protein aggregation. These features increased the signal-to-noise ratio of [M+H]+ peaks by two orders of magnitude and lowered the detection limit of intact proteins to 0.01 mg mL-1. The coefficient of variation with or without AuNPs/CNC is measured as 10.2% and 32.5%, respectively. The excellent reproducibility yielded a linear relationship (y = 15.41x - 7.983, R2 = 0.989) over the protein concentration range of 0.01 to 20 mg mL-1. Finally, AuNPs/CNC-assisted LDI-TOF MS provides clinically relevant fingerprint information of exosomal proteins in NSCLC serum, and characteristic proteins S100 calcium-binding protein A10, Urokinase plasminogen activator surface receptor, Plasma protease C1 inhibitor, Tyrosine-protein kinase Fgr and Mannose-binding lectin associated serine protease 2 represented excellent predictive biomarkers of NSCLC risk.

NUF2 Expression in Cancer Tissues and Lymph Nodes Suggests Post-Surgery Recurrence of Non-Small Cell Lung Cancer

In non-small cell lung cancer (NSCLC) cases, detecting potential lymph node metastases is essential to determine the indications for sublobar resection or adjuvant therapy. NUF2 is a tumor-specific antigen that is highly expressed in lung cancer tissues. However, the significance of analyzing NUF2 expression in dissected lymph nodes has not yet been studied. Thus, we investigated the association between NUF2 expression in lung cancer tissues and dissected lymph nodes and early recurrence of NSCLC to determine its usefulness as a marker of lymph node micrometastasis. This retrospective study quantified NUF2 expression in the cancer tissues of 88 patients with NSCLC who underwent complete resection using real-time polymerase chain reaction and investigated its relationship with clinicopathological features and prognosis. We also quantified NUF2 RNA expression in mediastinal lymph nodes from 255 patients with pN0 NSCLC who underwent complete resection with lymph node dissection and analyzed its association with prognosis. NUF2 expression in primary tumors was correlated with lymph node metastasis and unfavorable outcomes in terms of poor recurrence-free and cancer-specific survival. In N0 NSCLC cases, high NUF2 expression in mediastinal lymph nodes indicated poor prognosis, especially in lymph node recurrence. NUF2 emerges as a promising marker for predicting lymph node metastatic recurrence, offering potential utility in guiding post-surgical adjuvant therapy for lung cancer or assisting in intraoperative decisions for sublobar resection.

Pan-cancer analysis of RNA 5-methylcytosine reader (ALYREF)

The increasing interest in RNA modifications has significantly advanced epigenomic and epitranscriptomic technologies. This study focuses on the immuno-oncological impact of ALYREF in human cancer through a pan-cancer analysis, enhancing understanding of this gene's role in cancer. We observed differential ALYREF expression between tumor and normal samples, correlating strongly with prognosis in various cancers, particularly kidney renal papillary cell carcinoma (KIRP) and liver hepatocellular carcinoma (LIHC). ALYREF showed a negative correlation with most tumor-infiltrating cells in lung squamous cell carcinoma (LUSC) and lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), while positive correlations were noted in LIHC, kidney chromophobe (KICH), mesothelioma (MESO), KIRP, pheochromocytoma and paraganglioma (PARD), and glioma (GBMLGG). Additionally, ALYREF expression was closely associated with tumor heterogeneity, stemness indices, and a high mutation rate in TP53 across these cancers. In conclusion, ALYREF may serve as an oncogenic biomarker in numerous cancers, meriting further research attention.

Aptamer Conjugate-Based Ratiometric Fluorescent Probe for Precise Imaging of Therapy-Induced Cancer Senescence

Therapy-induced cellular senescence has been increasingly recognized as a key mechanism to promote various aspects of carcinogenesis in a nonautonomous manner. Thus, real-time imaging monitoring of cellular senescence during cancer therapy is imperative not only to further elucidate its roles in cancer progression but also to provide guidance for medical management of cancer. However, it has long been a challenging task due to the lack of effective imaging molecule tools with high specificity and accuracy toward cancer senescence. Herein, we report the rational design, synthesis, and evaluation of an aptamer conjugate-based ratiometric fluorescent probe for precise imaging of therapy-induced cancer senescence. Unlike traditional senescence imaging systems, our probe targets two senescence-associated markers at both cellular and subcellular dimensions, namely, aptamer-mediated membrane marker recognition for active cell targeting and lysosomal marker-triggered ratiometric fluorescence changes of two cyanine dyes for site-specific, high-contrast imaging. Moreover, such a two-channel fluorescence response is activated after a one-step reaction and at the same location, avoiding the diffusion-caused signal decay previously encountered in dual-marker activated probes, contributing to spatiotemporally specific imaging of therapy-induced cancer senescence in living cells and three-dimensional multicellular tumor spheroids. This work may offer a valuable tool for a basic understanding of cellular senescence in cancer biology and interventions.

DIRECT: Digital Microfluidics for Isolation-Free Shared Library Construction of Single-Cell DNA Methylome and Transcriptome

Simultaneous profiling of DNA methylation and gene expression within single cells is a powerful technology to dissect complex gene regulatory network of cells. However, existing methods are based on picking a single-cell in a tube and split single-cell lysate into two parts for transcriptome and methylome library construction, respectively, which is costly and cumbersome. Here, DIRECT is proposed, a digital microfluidics-based method for high-efficiency single-cell isolation and simultaneous analysis of the methylome and transcriptome in a single library construction. The accuracy of DIRECT is demonstrated in comparison with bulk and single-omics data, and the high CpG site coverage of DIRECT allows for precise analysis of copy number variation information, enabling expansion of single cell analysis from two- to three-omics. By applying DIRECT to monitor the dynamics of mouse embryonic stem cell differentiation, the relationship between DNA methylation and changes in gene expression during differentiation is revealed. DIRECT enables accurate, robust, and reproducible single-cell DNA methylation and gene expression co-analysis in a more cost-effective, simpler library preparation and automated manner, broadening the application scenarios of single-cell multi-omics analysis and revealing a more comprehensive and fine-grained map of cellular regulatory landscapes.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches

Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%. The code is available at https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images.

Rare urachal mucinous cystic tumor of low malignant potential with peritoneal pseudomyxoma: A case report

Mucinous cystic tumors of low malignant potential (MCTLMP) are rare urachal neoplasms. The morphological characteristics and clinical prognosis of MCTLMP is similar to that of mucinous cystic tumors occurring in the ovary and appendix. After complete resection, almost no cases of recurrence or metastasis have been reported. Because MCTLMP is rare, it may be missed in the clinic. MCTLMP can lead to the formation of pseudomyxoma peritonei (PMP), which manifests as the widespread production of mucus in the abdominal cavity and makes the disease complex or difficult to diagnose. At present, only 3 cases of MCTLMP with PMP have been reported in the literature. In the present study a fourth case of urachal MCTLMP in a 74-year-old male that resulted in widespread PMP is presented. Initially, a multilocular cystic lesion was revealed in the urachal duct area at the anterior upper margin of the bladder after a patient, experiencing lower abdominal pain, was imaged. As revealed using light microscopy, the cyst was lined with a mucous columnar epithelium, and part of the epithelium indicated pseudolamellar hyperplasia and papillary structures. The cells indicated mild atypia and low mitotic activity. There was no stromal infiltration of tumor cells, and a large amount of mucous exudate was observed. As preoperative computed tomography examination suggested the presence of a large amount of ascites and there were increased levels of blood tumor markers, carcinoembryonic antigen and carbohydrate antigen 125, clinicians considered that the diagnosis maybe a malignant tumor of the urachal gland with peripheral dissemination. However, the diagnosis of MCTLMP with PMP was confirmed by histopathological examination. The mass was completely removed, along with part of the peritoneum and bladder wall as these were within the tumor margin. The appendix appeared normal during surgery. A one off dose of intraperitoneal infusion chemotherapy with 1,000 mg 5-fluorouracil was performed after surgery. No recurrence was observed during the 8-month follow-up period.

Biphasic function of GSK3β in gefitinib‑resistant NSCLC with or without EGFR mutations

Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs), such as gefitinib, are effective in the treatment of non-small cell lung cancer (NSCLC) harboring EGFR mutations. However, the mechanism underlying acquired resistance to EGFR-TKIs remains largely unknown. Therefore, the present study generated gefitinib-resistant PC-9 (PC-9G) cells, which were revealed to be more resistant to gefitinib-induced reductions in proliferation, migration and invasion, and increases in apoptosis, and had no detectable EGFR mutations compared with the control PC-9 cell line. In addition, the present study performed genome-wide transcriptomic analysis of differentially expressed genes between PC-9 and PC-9G cell lines. Cell proliferation, colony formation, invasion, migration and flow cytometry analyses were also performed. The genome-wide transcriptomic analysis revealed that glycogen synthase kinase 3β (GSK3β) was downregulated in PC-9G cells compared with that in PC-9 cells. Furthermore, GSK3β overexpression increased the proliferation, migration and invasion of PC-9 and H1975 gefitinib-resistant cells. Conversely, overexpression of GSK3β suppressed the proliferation, migration and invasion of PC-9G cells. Furthermore, AKT inhibition reduced the proliferation, migration and invasion, and induced the apoptosis of PC-9, PC-9G and H1975 cells, the effects of which were reversed following AKT activation; notably, the tumor suppressor function of GSK3β was inconsistent with the tumor promotor role of the AKT pathway in PC-9G cells without EGFR mutation. The present study may provide novel insights into the distinctive role of GSK3β in gefitinib-resistant NSCLC with or without EGFR mutations, suggesting that a more detailed investigation on GSK3β as a therapeutic target for gefitinib-resistant NSCLC may be warranted.

CXXC finger protein 1 (CFP1) bridges the reshaping of genomic H3K4me3 signature to the advancement of lung adenocarcinoma

Histone H3 lysine 4 trimethylation (H3K4me3) is a canonical chromatin modification associated with active gene transcription, playing a pivotal role in regulating various cellular functions. Components of the H3K4me3 methyltransferase complex, known as the proteins associated with SET1 (COMPASS), have been implicated in exerting cancer-protective or cancer-inhibitory effects through inducive H3K4me3 modification. However, the role of the indispensable non-catalytic component of COMPASS CXXC-type zinc finger protein 1 (CFP1) in malignant progression remains unclear. We have unveiled that CFP1 promote lung adenocarcinoma (LUAD) cell proliferation, migration, and invasion while impairing cell apoptosis through in vitro and in vivo models. In addition, high CFP1 expression was identified as emerged as an adverse prognostic indicator across multiple public and in-house LUAD datasets. Notably, CFP1 deficiency led to dual effects on cancer cell transcriptome including extensive inactivation of cancer-promoting as well as activation of cancer repressors. Combining this with the chromatin immunoprecipitation sequencing (ChIP-seq) analysis, we showed that CFP1 ablation reshaped the genomic H3K4me3 distribution signature, with prominent effects on TGF-β and WNT signaling pathways. Collectively, our study proposes that CFP1 mediates tumorigenesis by genomic histone methylation reprogramming, offering insights for future investigations into epigenetic modifications in cancer progression and potential therapeutic advancements.

Application of nanotechnology in bladder cancer diagnosis and therapeutic drug delivery

Bladder cancer (BC) is one of the most common malignant tumors in the urinary system, and its high recurrence rate is a great economic burden to patients. Traditional diagnosis and treatment methods have the disadvantages of insufficient targeting, obvious side effects and low sensitivity, which seriously limit the accurate diagnosis and efficient treatment of BC. Due to their small size, easy surface modification, optical properties such as plasmon resonance, and surface enhanced Raman scattering, good electrical conductivity and photothermal conversion properties, nanomaterials have great potential application value in the realization of specific diagnosis and targeted therapy of BC. At present, the application of nanomaterials in the diagnosis and treatment of BC is attracting great attention and achieving rich research results. Therefore, this paper summarizes the recent research on nanomaterials in the diagnosis and treatment of BC, clarifies the existing advantages and disadvantages, and provides theoretical guidance for promoting the accurate diagnosis and efficient treatment of BC.

Earlier Diagnosis of Pancreatic Cancer: Is It Possible?

Pancreatic ductal adenocarcinoma has a very high mortality rate which has been only minimally improved in the last 30 years. This high mortality is closely related to late diagnosis, which is usually made when the tumor is large and has extensively infiltrated neighboring tissues or distant metastases are already present. This is a paradoxical situation for a tumor that requires nearly 15 years to develop since the first founding mutation. Response to chemotherapy under such late circumstances is poor, resistance is frequent, and prolongation of survival is almost negligible. Early surgery has been, and still is, the only approach with a slightly better outcome. Unfortunately, the relapse percentage after surgery is still very high. In fact, early surgery clearly requires early diagnosis. Despite all the advances in diagnostic methods, the available tools for improving these results are scarce. Serum tumor markers permit a late diagnosis, but their contribution to an improved therapeutic result is very limited. On the other hand, effective screening methods for high-risk populations have not been fully developed as yet. This paper discusses the difficulties of early diagnosis, evaluates whether the available diagnostic tools are adequate, and proposes some simple and not-so-simple measures to improve it.

The Current Landscape of Glioblastoma Biomarkers in Body Fluids

Glioblastoma (GBM) is a highly aggressive and lethal primary brain cancer that necessitates early detection and accurate diagnosis for effective treatment and improved patient outcomes. Traditional diagnostic methods, such as imaging techniques and tissue biopsies, have limitations in providing real-time information and distinguishing treatment-related changes from tumor progression. Liquid biopsies, used to analyze biomarkers in body fluids, offer a non-invasive and dynamic approach to detecting and monitoring GBM. This article provides an overview of GBM biomarkers in body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), cell-free RNA (cfRNA), microRNA (miRNA), and extracellular vesicles. It explores the clinical utility of these biomarkers for GBM detection, monitoring, and prognosis. Challenges and limitations in implementing liquid biopsy strategies in clinical practice are also discussed. The article highlights the potential of liquid biopsies as valuable tools for personalized GBM management but underscores the need for standardized protocols and further research to optimize their clinical utility.

Construction of Exosome SORL1 Detection Platform Based on 3D Porous Microfluidic Chip and its Application in Early Diagnosis of Colorectal Cancer

Exosomes are promising new biomarkers for colorectal cancer (CRC) diagnosis, due to their rich biological fingerprints and high level of stability. However, the accurate detection of exosomes with specific surface receptors is limited to clinical application. Herein, an exosome enrichment platform on a 3D porous sponge microfluidic chip is constructed and the exosome capture efficiency of this chip is ≈90%. Also, deep mass spectrometry analysis followed by multi-level expression screenings revealed a CRC-specific exosome membrane protein (SORL1). A method of SORL1 detection by specific quantum dot labeling is further designed and the ensemble classification system is established by extracting features from 64-patched fluorescence images. Importantly, the area under the curve (AUC) using this system is 0.99, which is significantly higher (p < 0.001) than that using a conventional biomarker (carcinoembryonic antigen (CEA), AUC of 0.71). The above system showed similar diagnostic performance, dealing with early-stage CRC, young CRC, and CEA-negative CRC patients.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.