Detection Of Liver Cancer Research Articles

Mex3a promoter hypomethylation can be utilized to diagnose HBV-associated hepatocellular carcinoma: a randomized controlled trial

BackgroundHepatocellular carcinoma remains a health challenge for humanity. Therefore, there is an urgent need to develop novel biomarkers with high efficiency yet fast ability to meet the requirements of hepatocellular carcinoma treatment.MethodsA total of 229 patients with HBV-associated hepatocellular carcinoma (HCC), 298 patients with chronic hepatitis B (CHB), and 96 healthy controls were retrospectively analyzed. Methylation levels of the Mex3a promoter in peripheral blood mononuclear cells (PBMCs) were measured using MethyLight to obtain clinical and laboratory parameters.ResultsThe Mex3a promoter methylation level in HCC patients (median: 0.289% and interquartile range: 0.126%–0.590%) was significantly lower than that in CHB patients (median: 0.999%, interquartile range: 0.417%–1.268%, and p < 0.001) and healthy people (median: 2.172%, interquartile range: 1.225%–3.098%, and p < 0.001). The Mex3a mRNA levels in HCC patients (median: 12.198 and interquartile range: 3.112–18.996) were significantly higher than those in CHB patients (median: 1.623 and interquartile range: 0.066–6.000, and p < 0.001) and healthy controls (median: 0.329, interquartile range: 0.031–1.547, and p < 0.001). MethyLight data were expressed as a percentage of the methylated reference (PMR) value. The Mex3a PMR value was negatively correlated with the mRNA expression level (Spearman’s R = −0.829 and p < 0.001). The Mex3a PMR value of HCC patients was significantly correlated with age (Spearman’s R = 0.113 and p = 0.044), and the mRNA level was significantly correlated with ALT (Spearman’s R = 0.132 and p = 0.046). The Mex3a promoter methylation levels and mRNA levels were also independent factors in the development of liver cancer. The Mex3a promoter methylation and mRNA levels were better at distinguishing HCC from CHB than AFP [area under the receiver operating characteristic curve (AUC) for predicting HCC vs. CHB: 0.915 vs. 0.715: p < 0.001]. The combined use of AFP and Mex3a methylation levels and mRNA levels further improved the area under the receiver operating characteristic curve.ConclusionThe presence of Mex3a promoter hypomethylation in hepatocellular carcinoma can be used as a non-invasive biomarker for the early detection of liver cancer.

Prospective cohort for early detection of liver cancer (Pearl): a study protocol

IntroductionHepatocellular carcinoma (HCC) is the fastest-rising and fourth most common cause of cancer death worldwide. Liver cirrhosis is the largest underlying risk factor for HCC. Therefore, patients with cirrhosis should...

Surface-enhanced Raman scatting microfluidic chip based on the identification competition strategy were used for rapid and simultaneous detection of liver cancer related proteins

Biomarker testing plays a crucial role in the early detection of liver cancer. Herein, we developed a dual-signal amplification approach utilizing magnetic aggregation and a recognition competition strategy for the simultaneous detection of alpha-fetoprotein (AFP) and manganese superoxide dismutase (MnSOD) in serum. 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 were synthesized by functionalizing Raman signaling molecules and aptamer complementary chains onto the surface of gold nanoparticles (AuNPs). The detection complex Raman signal molecule@AuNPs@H-Fe3O4@cDNA was assembled by conjugating 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 with two nucleic acid aptamers (cDNA1 and cDNA2) modified with Fe3O4 through partial base complementary pairing. The target protein exhibited specific binding with the aptamer, leading to the competitive displacement of 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 from the Fe3O4 array surface, consequently resulting in a reduction of the Surface-Enhanced Raman Spectroscopy (SERS) signal. Through this approach, the limit of detection (LOD) for AFP and MnSOD in serum was achieved at remarkably low levels of 5.89 pg/mL and 6.23 pg/mL, respectively, with a reaction incubation period of only 5 min. Finally, the platform was utilized for the quantification of AFP and MnSOD in the serum of a nude mouse hepatocellular carcinoma model. The results obtained through SERS were consistent with those from enzyme-linked immunosorbent assay (ELISA), validating its accuracy. This methodology presents a novel approach for the swift and concurrent detection of proteins, holding significant clinical promise for the early diagnosis of hepatocellular carcinoma.

Protein tyrosine phosphatase non-receptor II: A possible biomarker of poor prognosis and mediator of immune evasion in hepatocellular carcinoma.

The incidence of primary liver cancer is increasing year by year. In 2022 alone, more than 900000 people were diagnosed with liver cancer worldwide, with hepatocellular carcinoma (HCC) accounting for 75%-85% of cases. HCC is the most common primary liver cancer. China has the highest incidence and mortality rate of HCC in the world, and it is one of the malignant tumors that seriously threaten the health of Chinese people. The onset of liver cancer is occult, the early cases lack typical clinical symptoms, and most of the patients are already in the middle and late stage when diagnosed. Therefore, it is very important to find new markers for the early detection and diagnosis of liver cancer, improve the therapeutic effect, and improve the prognosis of patients. Protein tyrosine phosphatase non-receptor 2 (PTPN2) has been shown to be associated with colorectal cancer, triple-negative breast cancer, non-small cell lung cancer, and prostate cancer, but its biological role and function in tumors remain to be further studied. To combine the results of relevant data obtained from The Cancer Genome Atlas (TCGA) to provide the first in-depth analysis of the biological role of PTPN2 in HCC. The expression of PTPN2 in HCC was first analyzed based on the TCGA database, and the findings were then verified by immunohistochemical staining, quantitative real-time polymerase chain reaction (qRT-PCR), and immunoblotting. The value of PTPN2 in predicting the survival of patients with HCC was assessed by analyzing the relationship between PTPN2 expression in HCC tissues and clinicopathological features. Finally, the potential of PTPN2 affecting immune escape of liver cancer was evaluated by tumor immune dysfunction and exclusion and immunohistochemical staining. The results of immunohistochemical staining, qRT-PCR, and immunoblotting in combination with TCGA database analysis showed that PTPN2 was highly expressed and associated with a poor prognosis in HCC patients. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that PTPN2 was associated with various pathways, including cancer-related pathways, the Notch signaling pathway, and the MAPK signaling pathway. Gene Set Enrichment Analysis showed that PTPN2 was highly expressed in various immune-related pathways, such as the epithelial mesenchymal transition process. A risk model score based on PTPN2 showed that immune escape was significantly enhanced in the high-risk group compared with the low-risk group. This study investigated PTPN2 from multiple biological perspectives, revealing that PTPN2 can function as a biomarker of poor prognosis and mediate immune evasion in HCC.

Identifying liver cancer cells using cascaded convolutional neural network and gray level co-occurrence matrix techniques

<p>Liver cancer has a high mortality rate, especially in South Asia, East Asia, and Sub-Saharan Africa. Efforts to reduce these rates focus on detecting liver cancer at all stages. Early detection allows more treatment options, though symptoms may not always be apparent. The staging process evaluates tumor size, location, lymph node involvement, and spread to other organs. Our research used the CLD staging system, assessing tumor size (C), lymph nodes (L), and distant invasion (D). We applied a deep learning approach with a cascaded convolutional neural network (CNN) and gray level co-occurrence matrix (GLCM)-based texture features to distinguish benign from malignant tumors. The method validated with the cancer imaging archive (TCIA) dataset, demonstrating superior accuracy compared to existing techniques.</p>

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SignificanceLiver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.

A novel cell-free multi-omics approach for enhancing multi-cancer early detection.

103 Background: While circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection (MCED) have shown significant potential, their performance is hindered by the limited amount and inherent variability of ctDNA, particularly in cancer types with low ctDNA shedding. To enhance the accuracy of MCED, recent studies have focused on profiling cell-free RNA (cfRNA), which is secreted not only by tumor cells but also by other cell types to reflect the systemic tumor responses. Herein, we developed a novel cell-free multi-omics approach integrating cfDNA and cfRNA analyses from a single blood draw to improve the sensitivity of an MCED assay, especially for detecting low-shedding ctDNA cancer types. Methods: We recruited 535 healthy subjects and 287 patients across five cancer types (77 breast cancers, 102 colorectal cancers, 35 gastric cancers, 34 liver cancers, and 42 lung cancers). We established a multi-omics workflow to comprehensively profile cfDNA signatures (methylation, fragment length, motif ends, and copy number alterations), and cfRNA signatures (cfmRNA). Distinctive signatures were identified to differentiate cancers from healthy subjects and used for constructing robust machine learning classifiers. Results: We identified multiple significantly different features distinguishing cancer from healthy individuals, with 81% originating from cfDNA. Notably, cfmRNA features, accounting for 19% of total significant features, were mainly enriched in immune-related pathways, reflecting tumor-immune cell interactions. The cfDNA-based model achieved 81.2% accuracy, with highest performance in detecting liver cancer (100%) and 64%, 42%, and 36.4% for breast, colorectal, and gastric cancers, respectively, at 93% specificity. Combining cfDNA and cfRNA signatures increased sensitivity to 73%, 71%, and 46% for breast, colorectal, and gastric cancers. Overall, the multi-omic model achieved 85.6% accuracy and 72.7% sensitivity for detecting five cancer types at 92.6% specificity. Conclusions: The cell-free multi-omics approach holds potential for improving ctDNA-based MCED tests by simultaneously profiling both cfDNA and transcriptomic cfmRNA from a single blood draw. Validation on larger cohorts could lead to a paradigm shift in cancer screening practices.

Integration of Fuzzy Logic and Soft Computing Approaches for the Detection and Classification of Liver Cancer

Integration of Fuzzy Logic and Soft Computing Approaches for the Detection and Classification of Liver Cancer

Economic analysis of combined multi-cancer detection blood test and usual care.

e23055 Background: A previous multi-cancer detection blood test (MCDBT) in six prevalent and deadly cancers including colorectal, esophageal, liver, lung, ovarian, and pancreatic cancers, has been reported to decrease late-stage incidence and increase 5-year survival rate. However, the economic cost-effectiveness of integrating the MCDBT with usual care and the factors influencing it have not been investigated. Methods: An interception model was utilized to estimate the stage distribution at the time of cancer diagnosis, while a decision-tree model was developed to assess the clinical and economic outcomes of annual MCDBT for individuals aged 50 to 79 in the United States. The performance metrics for the MCDBT were based on the findings of a previous study called THUNDER. The incidence data were derived from the Surveillance, Epidemiology, and End Results Program (SEER), and the associated costs were obtained from SEER-Medicare. Other input data was estimated based on information available in the published literature. Results: In addition to the usual care, the integration of the MCDBT resulted in a shift of 27.9% in cancer cases from stage III-IV to stage I-II, which led to a decrease in per-cancer treatment cost by $5971 and an increase in quality-adjusted life-years by 0.10 per cancer case. The major factors that influenced the incremental cost-effectiveness ratio (ICER) included price, incidence, specificity, and sensitivity. The MCDBT with a price less than $800 showed cost-effectiveness with an ICER below three times the per capita GDP of the United States in the average-risk population. In a high-risk population with the incidence two-fold higher than the average-risk population, the ICER decreased by 58.3%. When compared the influence of specificity and sensitivity on the ICER of the MCDBT, a 5% increase in specificity resulted a decrease in ICER by 48.8%, while a 5% increase in sensitivity only reduced the ICER by 12.7%, suggesting the importance of high specificity in MCDBT. Conclusions: The MCDBT with a price less than $800 showed cost-effectiveness in the detection of colorectal, esophageal, liver, lung, ovarian, and pancreatic cancers. Price, incidence of the intended-use population, specificity and sensitivity were the major factors that influenced the cost-effectiveness.

Interleukin-41: a novel serum marker for the diagnosis of alpha-fetoprotein-negative hepatocellular carcinoma.

For the lack of effective serum markers for hepatocellular carcinoma(HCC) diagnosis, it is difficult to detect liver cancer and identify its recurrence early. Databases were used to analyze the genes potentially associated with alpha-fetoprotein(AFP). ELISA assay was used to detect the serum IL-41 in HCC, liver metastases, hepatitis, and healthy people. Immunohistochemical staining was used to analyze the relative quantification of IL-41 in HCC and paracancer tissues. Various survival curves were plotted according to clinical pathological data and helped us draw the ROC curve of IL-41 diagnosis of HCC. The serum expression of IL-41 was highest in AFP negative HCC patients and significantly higher than that in AFP positive HCC and metastatic cancer patients. There was a significant negative correlation between elevated serum IL-41 and AFP(<1500ng/ml). The clinicopathological features suggested that the serum IL-41 level was significantly correlated with capsule invasion, low differentiation and AFP. High serum expression of IL-41 suggests poorer survival and earlier recurrence after resection, and IL-41 upregulated in patients with early recurrence and death. The expression of IL-41 was higher in HCC tissues of patients with multiple tumors or microvascular invasion. The ROC curve showed that serum IL-41 had a sensitivity of 90.17 for HCC and a sensitivity of 96.63 for AFP-negative HCC, while the specificity was higher than 61%. IL-41 in serum and tissue suggests poor prognosis and postoperative recurrence in HCC patients and could be a new serum diagnostic marker for AFP negative patients.

Trend analysis and age-period-cohort effects on morbidity and mortality of liver cancer from 2010 to 2020 in Guangzhou, China.

Liver cancer is one of the most common malignant gastrointestinal tumors worldwide. This study intends to provide insight into the epidemiological characteristics and development trends of liver cancer incidence and mortality from 2010 to 2020 in Guangzhou, China. Data were collected from the Cancer Registry and Reporting Office of Guangzhou Center for Disease Control and Prevention. Cross-sectional study, Joinpoint regression (JPR) model, and Age-Period-Cohort (APC) model were conducted to analyze the age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) trend of liver cancer among the entire study period. The age-standardized incidence and mortality of liver cancer in Guangzhou showed an overall decreasing trend. The disparity in risk of morbidity and mortality between the two sexes for liver cancer is increasing. The cohort effect was the most significant among those born in 1965~1969, and the risk of liver cancer incidence and mortality in the total population increased and then decreased with the birth cohort. Compared with the birth cohort born in 1950~1954 (the reference cohort), the risk of liver cancer incidence and mortality in the males born in 1995~1999 decreased by 32% and 41%, respectively, while the risk in the females decreased by 31% and 32%, respectively. The early detection, prevention, clinical diagnosis, and treatment of liver cancer in Guangzhou have made remarkable achievements in recent years. However, the risk of liver cancer in the elderly and the middle-aged males is still at a high level. Therefore, the publicity of knowledge related to the prevention and treatment of liver cancer among the relevant population groups should be actively carried out to enhance the rate of early diagnosis and treatment of liver cancer and to advocate a healthier lifestyle.

Ultrasonography of Hepatocellular Carcinoma: From Diagnosis to Prognosis.

Hepatocellular carcinoma (HCC) is a prominent contributor to cancer-related mortality worldwide. Early detection and diagnosis of liver cancer can significantly improve its prognosis and patient survival. Ultrasound technology, serving has undergone substantial advances as the primary method of HCC surveillance and has broadened its scope in recent years for effective management of HCC. This article is a comprehensive overview of ultrasound technology in the treatment of HCC, encompassing early detection, diagnosis, staging, treatment evaluation, and prognostic assessment. In addition, the authors summarized the application of contrast-enhanced ultrasound in the diagnosis of HCC and assessment of prognosis. Finally, the authors discussed further directions in this field by emphasizing overcoming existing obstacles and integrating cutting-edge technologies.

Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study

PurposeTo assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC). MethodsThis single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test. Results67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001–0.039). For clinically-relevant liver lesions, the detection rates (91.4%–93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%–89.8%, p = 0.230–0.231) but were superior to PI-DWI (82.7%–85.8%, p = 0.015–0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%–97.4% vs 76.9%–79.5% vs 64.1%–69.2%, p = 0.002–0.045) among the three DWI sequences. ConclusionIn patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.

An approach for liver cancer detection from histopathology images using hybrid pre-trained models

An approach for liver cancer detection from histopathology images using hybrid pre-trained models

Abstract 985: An effective cfDNA methylation-based assay in discriminating primary liver cancer from cirrhosis or chronic hepatitis virus infection: marker discovery, phase I pilot, and phase II clinical validation

Abstract Background: Early detection of primary liver cancer (PLC) in individuals with liver cirrhosis (LC) or chronic hepatitis virus infection (CHVI) improves survival. Timely, effective, and affordable tools with high sensitivity are urgently needed in clinical practice. Methods: Tissue and/or plasma samples from 159 healthy individuals and 89 PLC, LC, or CHVI patients were sequenced by a targeted methylation panel (~70,000 CpGs) to screen candidate methylated DNA markers (MDMs). In phase I, the performance of each selected MDM was validated in 175 plasma samples (PLC, n=101; LC/CHVI, n=74) by a CO-methylation aMplification rEal-Time PCR (COMET) assay. Logistic models were then trained and validated in phase II with 310 plasma samples (hepatocellular carcinoma [HCC], n=212; combined hepatocellular-cholangiocarcinoma [cHCC-CC], n=12; LC/CHVI, n=106; training vs. validation, 2:1). Results: The methylation levels of eleven selected MDMs with top performance showed a significant increase in PLC compared with LC/CHVI in both tissue and plasma samples (P&amp;lt;0.05). In phase I, eight of the above eleven MDMs with an area under the curve (AUC) over 0.80 to differentiate PLC and LC/CHVI were chosen for further investigation. In phase II, the MDM-based logistic model achieved sensitivity of 87.2% (95% confidence interval [CI], 80.8%-92.4%) and 88.0% (78.4%-94.4%), at respective specificity of 97.1% (90.1%-99.7%) and 100% (90.3%-100%) in the training and validation sets. In the validation set, sensitivity in patients with BCLC stage 0, diameter&amp;lt;3 cm, AFP-negative, and PIVKA-II-negative was 90.0% (55.5%-99.7%), 88.9% (65.3%-98.6%), 80.6% (64.0%-91.8%), and 81.3% (54.4%-96.0%), respectively. The classifier achieved similar sensitivity in patients with or without HBV/HCV infection (88.2% vs 90.0%). Additionally, our model detected 19 of 24 (79.3%, 57.8%-92.9%) intrahepatic cholangiocarcinoma. Combining AFP and PIVKA-II, the model achieved higher sensitivity of 93.3% (85.1%-97.8%) and specificity of 100.0% (90.3%-100%). Conclusion: In conclusion, the present study demonstrated the feasibility of using an easy-to-implement cfDNA methylation assay (COMET) to discriminate early-stage liver cancer from other liver diseases, with superior accuracy over AFP and PIVKA-II. Notably, the combination of the COMET, AFP, and PIVKA-II increased the overall sensitivity, indicating the potential benefit of integrating these approaches in clinical practices. Moving forward, further investigation is needed to validate these findings in larger prospective clinical studies. Keywords: primary liver cancer, early detection, liver cirrhosis, chronic hepatitis virus infection, methylated plasma DNA marker. Citation Format: Yang Tian, Yanan Wang, Mingxin Pan, Lei Zhang, Qiancheng You, Bingsi Li, Shangli Cai, Feng Shen, Guoyue Lv. An effective cfDNA methylation-based assay in discriminating primary liver cancer from cirrhosis or chronic hepatitis virus infection: marker discovery, phase I pilot, and phase II clinical validation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 985.

Abstract 6091: Multi-cancer early cancer detection and cancer-type identification with high sensitivity non-invasive using urine: Pancreatic lung cancer early detection

Abstract N-NOSE, a breakthrough cancer detection test from HIROTSU BIO SCIENCE, utilizes the unique chemotactic behavior of the nematode C. elegans. This nematode is drawn to the urine of individuals with 15 different types of cancers (including stomach, colon-rectum, lung, breast, pancreas, liver, prostate, uterus, esophagus, gallbladder, bile duct, kidney, urinary bladder, ovary, oropharynx) while avoiding urine from healthy persons, making N-NOSE a comprehensive primary screening tool. It has a high accuracy rate, with 87.5% sensitivity and 90.2% specificity for early-stage cancers. Being non-invasive and reasonably priced, N-NOSE has been a significant success in Japan since its 2020 launch, with over 500,000 screenings.This presentation delves into the technology aspect of N-NOSE in Japan, comparing its clinical research and key findings with competitors to underscore its effectiveness in early cancer detection. Furthermore, N-NOSE now advances to not only detect cancer but also identify its type in a noninvasive way, using urine and with high PPV.With pancreatic cancer being particularly lethal, we outline “N-NOSE plus pancreas,” the world’s first pancreatic cancer detection test at an early stage. This involves genetically modifying C. elegans to react specifically to early-stage pancreatic cancer urine samples, a development centered on the newly identified cr-4 GPCR in AWC neurons. Next, we profile our newest liver-cancer-specific test, “N-NOSE plus liver,” which offers early liver cancer detection with high sensitivity and specificity using urine.Finally, we highlight how N-NOSE bridges the gap in cancer screening and type identification, offering a complete, non-invasive solution. Citation Format: Eric Di Luccio, Takaaki Hirotsu. Multi-cancer early cancer detection and cancer-type identification with high sensitivity non-invasive using urine: Pancreatic lung cancer early detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6091.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages

BackgroundSurface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. ResultsSerum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SignificanceThe experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Multi-scale representation of surface-enhanced Raman, spectroscopy data for deep learning-based liver, cancer detection

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Multi-omics combined test performance effectiveness on opportunistic screening of high-risk liver cancer population

Objective: To validate the performance of a multi-omics combined test for early screening of high-risk liver cancer populations. Methods: 173 high-risk patients with liver cancer were prospectively screened in a real-world setting, and 164 cases were finally enrolled. B-ultrasound, alpha-fetoprotein (AFP), and HCC screens were conducted in all patients. A multi-omics early screening test was performed for liver cancer in combination with multi-gene methylation, TP53/TERT/CTNNB1 mutations, AFP, and abnormal prothrombin (PIVKA-II). Differences in rates were compared using the chi-square test, adjusted chi-square test, or Fisher's exact probability method for count data. A non-parametric rank test (Mann-Whitney) was used to compare the differences between the two groups of data. Results: The HCCscreen detection had a sensitivity of 100% for liver cancer screening, 93.8% for liver cancer and precancerous diseases, 34.1% for positive predictive value, 99.2% for negative predictive value, and 0.89 for an area under the curve (AUC). Parallel detection of AFP, AFP+B-ultrasound, and methylation+mutation had a sensitivity/specificity and AUC of 31.3%/88.5% (AUC=0.78), 56.3%/88.2% (AUC=0.86), and 81.3%/82.4 % (AUC=0.84). At the same time, the disease severity range was significantly correlated with the methylation+mutation score, HCCscreen score, or positive detection rate (PDR). There was no significant correlation between AFP serum levels and methylation+mutation or HCCscreen scores, while there was a significant linear correlation between methylation+mutation scores and HCCscreen scores (r = 0.73, P < 0.001). Conclusion: In real-world settings, HCCscreen shows high sensitivity for screening opportunistic, high-risk liver cancer populations. Furthermore, it may efficaciously detect liver cancer and precancerous diseases, with superior performance to AFP and AFP+ultrasound. Hence, HCCscreen has the potential to become an effective screening tool that is superior to existing screening methods for high-risk liver cancer populations.