Benign Disease Patients Research Articles

Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed-Cohort Study Based on 16,189 Participants.

Early detection of bladder cancer (BCa) can have a positive impact on patients' prognosis. However, there is currently no widely accepted method for early screening of BCa. We aimed to develop an efficient, clinically applicable, and noninvasive method for the early screening of BCa by detecting specific serum miRNA levels. A mixed-cohort (including BCa, 12 different other cancers, benign disease patients, and health population) study was conducted using a sample size of 16,189. Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. The performance of the model was evaluated using receiver operating characteristic curve and decision curve analysis on the testing dataset, and subsequently, the model with the best predictive power was selected. Furthermore, the selected model's screening performance was evaluated using both the validation set and external set. The BCaS3miR model, utilizing only three serum miRNAs (miR-6087, miR-1343-3p, and miR-5100) and based on the KNN algorithm, is the superior screening model chosen for BCa. BCaS3miR consistently performed well in both the testing, validation, and external sets, exceeding 90% sensitivity and specificity levels. The area under the curve was 0.990 (95% CI: 0.984-0.991), 0.964 (95% CI: 0.936-0.984), and 0.917 (95% CI: 0.836-0.953) in the testing, validation, and external set. The subgroup analysis revealed that the BCaS3miR model demonstrated outstanding screening accuracy in various clinical subgroups of BCa. In addition, we developed a BCa screening scoring model (BCaSS) based on the levels of miR-1343-3p/miR-6087 and miR-5100/miR-6087. The screening effect of BCaSS is investigated and the findings indicate that it has predictability and distinct advantages. Using a mixed cohort with the largest known sample size to date, we have developed effective screening models for BCa, namely BCaS3miR and BCaSS. The models demonstrated remarkable screening accuracy, indicating potential for the early detection of BCa.

Exosomal miR-4745-5p/3911 from N2-polarized tumor-associated neutrophils promotes gastric cancer metastasis by regulating SLIT2

Tumor cells remodel the phenotype and function of tumor microenvironment (TME) cells to favor tumor progression. Previous studies have shown that neutrophils in TME are polarized to N2 tumor-associated neutrophils (TANs) by tumor derived factors, thus promoting tumor growth and metastasis, angiogenesis, therapy resistance, and immunosuppression. Exosomes act as critical intercellular messengers in human health and diseases including cancer. So far, the biological roles of exosomes from N2 TANs in gastric cancer have not been well characterized. Herein, we represented the first report that exosomes from N2 TANs promoted gastric cancer metastasis in vitro and in vivo. We found that exosomes from N2 TANs transferred miR-4745-5p/3911 to gastric cancer cells to downregulate SLIT2 (slit guidance ligand 2) gene expression. Adenovirus-mediated overexpression of SLIT2 reversed the promotion of gastric cancer metastasis by N2 TANs derived exosomes. We further revealed that gastric cancer cells induced glucose metabolic reprogramming in neutrophils through exosomal HMGB1 (high mobility group protein B1)/NF-κB pathway, which mediated neutrophil N2 polarization and miR-4745-5p/3911 upregulation. We further employed ddPCR (droplet digital PCR) to detect the expression of miR-4745-5p/3911 in N2 TANs exosomes from human serum samples and found their increased levels in gastric cancer patients compared to healthy controls and benign gastric disease patients. Conclusively, our results indicate that N2 TANs facilitate cancer metastasis via regulation of SLIT2 in gastric cancer cells by exosomal miR-4745-5p/3911, which provides a new insight into the roles of TME cells derived exosomes in gastric cancer metastasis and offers a potential biomarker for gastric cancer diagnosis.

Blood metabolites–based MCTarg for multi-cancer screening and clinical diagnosis.

e15028 Background: Multi-cancer early detection (MCED) tests have emerged as promising tools for reducing cancer-related healthcare costs and mortality. As metabolome is closely linked to the phenotype of bio-individual, metabolomics analysis enables the high throughput investigating of the pathophysiological conditions. Based on metabolomics and machine learning, we developed the Multiple Cancer Target (MCTarg) analysis for multiple cancer screening and diagnosis by a single blood draw. Methods: A total of 1153 individuals (172 healthy participants, 623 patients with lung cancer, gastric cancer, and colorectal cancer, and 358 these organ-related benign disease patients) were enrolled from three independent clinical sites in China. Plasma samples were collected and measured by mass spectrometry (MS)-based platforms (LC-MS polar and lipid). MCTarg was developed with three machine learning modules for three scenarios (community screening for non-healthy individuals, clinical diagnosis for classifying malignant and benign diseases, and tracing tumor of origin). Results: MCTarg exhibited an Area Under the Curve (AUC) of 0.96 with 92.82% sensitivity at 80% specificity to differentiate the non-healthy individuals from healthy controls, and an AUC of 0.86 with 75.41% sensitivity at 79.66% specificity in discriminating multi-cancer from benign diseases. Notably, the classification accuracy for distinguishing between malignant and benign conditions in the colorectal organ was reached 90.74%, with a sensitivity of 83.87% and a specificity of 100%. Furthermore, the overall accuracy for detecting the cancer origin reached 84.78% with an average sensitivity and specificity of 78.08% and 90.7%, respectively. Conclusions: The outstanding results indicated that blood-metabolites-based MCTarg is poised to be a cost-effective, precise, and versatile tool for diagnosing multi-cancer and discriminating tumor of origin. Its potential applications encompass health checkups and supplementary diagnostic scenarios. More deadly cancer types will be extended and the performance of MCTarg will be validated by prospective and population-scale cohorts with longitudinal follow-up.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Genome-wide 5-hydroxymethylcytosines in circulating cell-free DNA as noninvasive diagnostic markers for gastric cancer.

5-Hydroxymethylcytosine-enriched gene profiles and regions show tissue-specific and tumor specific. There is a potential value to explore cell-free DNA 5-hydroxymethylcytosine feature biomarkers for early gastric cancer detection. A matched case‒control study design with 50 gastric cancer patients and 50 controls was performed to sequence the different 5-hydroxymethylcytosine modification features of cell free DNA. Significantly differential 5-hydroxymethylcytosine modification genes were identified to construct a gastric cancer diagnostic model. Data set from GEO was used as an external testing set to test the robustness of the diagnostic model. Accounting for more than 90% of 5-hydroxymethylcytosine peaks were distributed in the gene body in both the gastric cancer and control groups. The diagnostic model was developed based on five different 5-hydroxymethylcytosine modification genes, FBXL7, PDE3A, TPO, SNTG2 and STXBP5. The model could effectively distinguish gastric cancer patients from controls in the training (AUC = 0.95, sensitivity = 88.6%, specificity = 94.3%), validation (AUC = 0.87, sensitivity = 73.3%, specificity = 93.3%) and testing (AUC = 0.90, sensitivity = 81.9%, specificity = 90.2%) sets. The risk scores of the controls from the model were significantly lower than those of gastric cancer patients in bothour own data (P < 0.001) and GEO external testing data (P < 0.001), and no significant difference between different TNM stage patients (P = 0.09 and 0.66). Furthermore, there was no significant difference between the healthy control and benign gastric disease patients in the testing set from GEO (P = 0.10). The characteristics of 5-hydroxymethylcytosine in cell free DNA are specific to gastric cancer patients, and the diagnostic model constructed by five genes' 5-hydroxymethylcytosine features could effectively identify gastric cancer patients.

Clinicopathological factors associated with sentinel lymph node detection in non-small-cell lung cancer

BackgroundMapping of the pulmonary lymphatic system by near-infrared (NIR) fluorescence imaging might not always identify the first lymph node relay. The aim of this study was to determine the clinicopathologic factors allowing the identification of sentinel lymph nodes (SLNs) by NIR fluorescence imaging in thoracic surgery for non-small-cell lung cancer (NSCLC).MethodsWe conducted a retrospective review of 92 patients treated for suspected or confirmed cN0 lung cancer with curative intent who underwent an intraoperative injection of indocyanine green (ICG) either by direct peritumoral injection or by endobronchial injection using electromagnetic navigational bronchoscopy (ENB). After exclusion of patients for technical failure, benign disease and metastasis, we analyzed the clinicopathologic findings of 65 patients treated for localized-stage NSCLC, comparing the group with identification of SLNs (SLN-positive group) with the group without identification of SLNs (SLN-negative group).ResultsForty-eight patients (73.8%) were SLN-positive. Patients with SLN positivity were more frequently female (50%) than the SLN-negative patients were (11.8%) (p = 0.006). The mean value of diffusing capacity for carbon monoxide (DLCO) was lower among the patients in the SLN-negative group (64.7% ± 16.7%) than the SLN-positive group (77.6% ± 17.2%, p < 0.01). The ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FCV) was higher in the SLN-positive group (69.0% vs. 60.8%, p = 0.02). Patients who were SLN-negative were characterized by a severe degree of emphysema (p = 0.003). There was no significant difference in pathologic characteristics. On univariate analyses, age, female sex, DLCO, FEV1/FVC, degree of emphysema, and tumor size were significantly associated with SLN detection. On multivariate analysis, DLCO > 75% (HR = 4.92, 95% CI: 1.27–24.7; p = 0.03) and female sex (HR = 5.55, 95% CI: 1.25–39.33; p = 0.04) were independently associated with SLN detection.ConclusionsAt a time of resurgence in the use of the sentinel lymph node mapping technique in the field of thoracic surgery, this study enabled us to identify, using multivariate analysis, two predictive factors for success: DLCO > 75% and female sex. Larger datasets are needed to confirm our results.

CircBRIP1: a plasma diagnostic marker for non-small-cell lung cancer

BackgroundCircular RNA (circRNA), which has been demonstrated in studies to be abundantly prevalent in tumor cells and bodily fluids and to play a significant role in tumors, has the potential for biological markers to be used to assist tumor diagnosis. This study mainly discusses the potential of circBRIP1 as a biomarker for diagnosing non-small-cell lung cancer (NSCLC).MethodsFirst, high-throughput sequencing screened the differentially expressed circBRIP1, and real-time fluorescence quantitative PCR (qRT-PCR) verified its expression in NSCLC. Next, sanger sequencing, agarose gel electrophoresis, RNase R assay, and fluorescence in situ hybridization (FISH) were used to verify its molecular characteristics. The diagnostic value was analyzed by the subject operating characteristic curve (ROC), and the cardinality test was analyzed for correlation with clinicopathological parameters. Finally, we tentatively predicted the downstream miRNA- or RNA-binding protein that may bind to circBRIP1.ResultsCircBRIP1 is highly expressed in NSCLC tissues, cells and plasma with good specificity and stability. CircBRIP1 not only can well-distinguish NSCLC patients from benign pulmonary diseases (BPD) patients, healthy individuals and small cell lung cancer (SCLC) patients, but it also has some potential for dynamic monitoring. Combined with the analysis of clinicopathological data, the high level of circRNA expression was related to the degree of tumor differentiation, TNM stage, T stage, lymph node metastasis and distal metastasis in NSCLC patients. In addition, circBRIP1 has a high diagnostic value.ConclusionsPlasma circBRIP1 is significantly overexpressed in NSCLC patients. It can be used as a sensitive biomarker with unique value for early diagnosis, tumor development and prognosis detection.

Application of a nomogram from coagulation-related biomarkers and C1q and total bile acids in distinguishing advanced and early-stage lung cancer.

This study aimed to establish a nomogram to distinguish advanced- and early-stage lung cancer based on coagulation-related biomarkers and liver-related biomarkers. A total of 306 patients with lung cancer and 172 patients with benign pulmonary disease were enrolled. Subgroup analyses based on histologic type, clinical stage, and neoplasm metastasis status were carried out and multivariable logistic regression analysis was applied. Furthermore, a nomogram model was developed and validated with bootstrap resampling. The concentrations of complement C1q, fibrinogen, and D-dimers, fibronectin, inorganic phosphate, and prealbumin were significantly changed in lung cancer patients compared to benign pulmonary disease patients. Multiple regression analysis based on subgroup analysis of clinical stage showed that compared with early-stage lung cancer, female (P < 0.001), asymptomatic admission (P = 0.001), and total bile acids (P = 0.011) were negatively related to advanced lung cancer, while C1q (P = 0.038), fibrinogen (P < 0.001), and D-dimers (P = 0.001) were positively related. A nomogram model based on gender, symptom, and the levels of total bile acids, C1q, fibrinogen, and D-dimers was constructed for distinguishing advanced lung cancer and early-stage lung cancer, with an area under the receiver operating characteristic curve of 0.919. The calibration curve for this nomogram revealed good predictive accuracy (P-Hosmer-Lemeshow = 0.697) between the predicted probability and the actual probability. We developed a nomogram based on gender, symptom, and the levels of fibrinogen, D-dimers, total bile acids, and C1q that can individually distinguish early- and advanced-stage lung cancer.

Exosomal miRNAs from neutrophils act as accurate biomarkers for gastric cancer diagnosis

BackgroundGastric cancer (GC) is the third leading cause of cancer-related death worldwide. Sensitive and accurate biomarkers can greatly aid in early diagnosis and favorable prognosis. Neutrophils are the most abundant immune cells in human circulation and play a critical role in tumor progression. Neutrophil-derived exosomes (Neu-Exo) contain abundant bioactive molecules and are critically involved in disease progression. MethodsWe proposed a Dynabeads-based (CD66b antibody-coupled) separation and detection system for Neu-Exo analysis. Dual antibody-assisted fluorescent Dynabeads was established to detect Neu-Exo abundance. MiRNA signature of Neu-Exo was identified by RNA sequencing. QRT-PCR and droplet digital PCR (ddPCR) were used for candidate miRNA detection and the potential of Neu-Exo miRNAs in the diagnosis of gastric cancer was evaluated. ResultsDual antibody-assisted fluorescent Dynabeads obtained a detection limit of 7.8 × 105 particles/mL of Neu-Exo and a recovery rate of 81 % under optimized conditions. ROC curve indicated that the abundance of CD66b+ Neu-Exo could well distinguish GC patients from healthy controls (HC) (AUC > 0.8). Additionally, miR-223-3p was found among the top differentially expressed miRNAs in Neu-Exo and presented superior diagnostic value in gastric cancer. Droplet digital PCR (ddPCR) significantly improved the diagnostic efficiency to differentiate GC patients from HC and benign gastric diseases (BGD) patients (AUC > 0.9). ConclusionThe Dynabeads-based separation and detection system, assisted with ddPCR analysis, provides a promising platform to enrich Neu-Exo and analyze miRNA profile for gastric cancer liquid biopsy.

Bilirubin is a superior biomarker for hepatocellular carcinoma diagnosis and for differential diagnosis of benign liver disease

Abstract Objectives To develop a novel diagnostic model combining bilirubin, protein induced by vitamin K absence or antagonist-II (PIVKA-II), and alpha-fetoprotein (AFP) to improve hepatocellular carcinoma (HCC) diagnosis. Methods The serum levels of PIVKA-Ⅱ, AFP, and bilirubin in 718 HCC patients and 2,763 benign liver disease (BLD) patients were measured. A mathematical model was used as the combined diagnostic model (PIVKA-Ⅱ, AFP, and bilirubin: PAB combination) for improving HCC diagnosis. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic value of the individual markers, the PIVKA-II and AFP (PA) combination, and the PAB combination for HCC diagnosis. Results With the increase in bilirubin, the positive predictive value (PPV) of bilirubin in HCC diagnosis decreased (p&lt;0.001) while the negative predictive value (NPV) increased (p&lt;0.001). The areas under the ROC curves (AUCs) of the PAB combination were 0.935 and 0.862 for the diagnosis of HCC and HCC&lt;3.0 cm, respectively, which were significantly higher than those of PIVKA-Ⅱ, AFP, and the PA combination (p&lt;0.001). The AUC values for PIVKA-Ⅱ, AFP, and the PA combination were significantly decreased for the diagnosis of HCC and HCC&lt;3.0 cm when serum levels of PIVKA-Ⅱ≥40 mAU/mL and/or AFP≥20 ng/mL were used for diagnosis, while the AUC value of the PAB combination increased. Conclusions Bilirubin is a superior biomarker for diagnosing HCC and distinguishing HCC from BLD. The combination of bilirubin with PIVKA-Ⅱ and AFP has superior diagnostic value for HCC and early-stage HCC.

Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

BackgroundFor early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme.MethodsTMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set.ResultsDifferential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%.ConclusionsIntegrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.

Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte

BackgroundNon-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa.MethodsWe retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm.Results197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573–0.853), specificity of 0.869 (95% CI 0.764–0.974), F1 of 0.699 (95% CI 0.557–0.841), and AUC of 0.864 (95% CI 0.794–0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved.ConclusionCombining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.

The diagnostic value of lncRNA HOTAIR for cervical carcinoma in vaginal discharge and serum.

There is a lower incidence of cervical carcinoma compared with other common carcinomas, however, the mortality rate of cervical carcinoma is higher, suggesting that the treatment and prognosis of cervical carcinoma are relatively poor. Therefore, cervical carcinoma patients urgently need to find new diagnostic markers for early detection and treatment. One hundred and fifty cervical carcinoma and 100 benign cervical disease patients from 2019 January to 2021 December in Tianjin Central Hospital of Gynecology Obstetrics were selected and 100 healthy women were as normal group. The expression of HOX transcript antisense RNA (HOTAIR) in cervical carcinoma and paracancerous tissue, serum sample was measured by realtime PCR assay. The receiver operating characteristic of HOTAIR for cervical carcinoma was analyzed. The study found that the expression level of HOTAIR in primary cervical carcinoma is closely related to tumor metastasis and prognosis. The expression level of HOTAIR in paracancerous tissue was significantly lower than that in cancer tissue, and the expression level of HOTAIR in vaginal discharge and serum was higher than that in cervical carcinoma patients which was positively correlated with tumor malignancy, meanwhile, HOTAIR was significantly reduced after surgery 3 months both in vaginal discharge and serum. In order to examine the diagnostic efficiency of HOTAIR for cervical carcinoma, we found that the area under curve of vaginal discharge was 0.9723, sensitivity was 92%, specificity was 98%, the area under curve of serum was 0.8518, sensitivity was 79%, and specificity was 94% by receiver operating characteristic analysis. The accuracy were 92.7% and 89.3% in vaginal discharge and serum via certified by cervical carcinoma and benign cervical disease patient and healthy people. The above results show that the diagnostic performance of HOTAIR in vaginal discharge is higher than that of serum, and it is expected to become a marker for cervical carcinoma diagnosis and treatment.

Intramolecular fluorescence resonance energy transfer strategy for accurate detection of AFP-L3% and improved diagnosis of hepatocellular carcinoma

Early and accurate diagnosis of hepatocellular carcinoma (HCC) is of significant importance for improving the survival rate and quality of life for HCC patients. The combined detection of alpha-fetoprotein (AFP) and alpha-fetoprotein-L3 (AFP-L3), namely AFP-L3%, can greatly improve the accuracy of HCC diagnosis compared with AFP detection. Herein, we developed a novel intramolecular fluorescence resonance energy transfer (FRET) strategy for sequential detection of AFP and AFP-specific core fucose to improve the diagnosis accuracy of HCC. Firstly, fluorescence-labeled AFP aptamer (AFP Apt-FAM) was used to specifically recognize all AFP isoforms, and total AFP was quantitatively determined using fluorescence intensity of FAM. Then, 4-((4-(dimethylamino)phenyl)azo)benzoic acid (Dabcyl) labeled lectins (PhoSL-Dabcyl) were used to specifically recognize the core fucose expressed on AFP-L3 that does not bind to other AFP isoforms. The combination of FAM and Dabcyl on the same AFP molecule could generate FRET effect, thereby quenching the fluorescence signal of FAM and quantitatively determining AFP-L3. After that, AFP-L3% was calculated according to the ratio of AFP-L3 to AFP. With this strategy, the concentration of total AFP, AFP-L3 isoform as well as the AFP-L3% were sensitively detected. Detection limits of 0.66 and 0.186 ng/mL were obtained for AFP and AFP-L3 in human serum, respectively. Clinical human serum test results showed that AFP- L3 % test was more accurate than AFP assay to distinguish healthy people, HCC patients and benign liver disease patients. Therefore, the proposed strategy is simple, sensitive and selective, which can improve the accuracy of early diagnosis of HCC, and has good clinical application potential.

Therapeutic mammoplasty: a "wise" oncoplastic choice-lessons from the largest single-center cohort from Asia.

The majority of breast cancer patients from India usually present with advanced disease, limiting the scope of breast conservation surgery. Therapeutic mammoplasty (TM), an oncoplastic technique that permits larger excisions, is quite promising in such a scenario and well suited to breast cancer in medium-to-large-sized breasts with ptosis and in some cases of large or multifocal/multicentric tumors. Here, we describe our TM cohort of 205 (194 malignant and 11 benign) patients from 2012 to 2019 treated at a single surgeon center in India, the largest Asian dataset for TM. All patients underwent treatment after careful discussions by a multidisciplinary tumor board and patient counseling. We report the clinicopathological profiles and surgical, oncological, cosmetic, and patient-related outcomes with different TM procedures. The median age of breast cancer patients was 49 years; that of benign disease patients was 41 years. The breast cancer cohort underwent simple (n = 84), complex (n = 71), or extreme (n = 44) TM surgeries. All resection margins were analyzed through intra-operative frozen-section assessment with stringent rad-path analysis protocols. The margin positivity rate was found to be 1.4%. A majority of the cohort was observed to have pT1-pT2 tumors, and the median resection volume was 180 cc. Low post-operative complication rates and good-to-excellent cosmetic scores were observed. The median follow-up was 39 months. We observed 2.07% local and 5.7% distal recurrences, and disease-specific mortality was 3.1%. At median follow-up, the overall survival was observed to be 95.9%, and disease-free survival was found to be 92.2%. The patient-reported outcome measures (PROMs) showed good-to-excellent scores for all types of TMs across BREAST-Q domains. We conclude that in India, a country where women present with large and locally advanced tumors, TM safely expands the indications for breast conservation surgery. Our results show oncological and cosmetic outcomes at acceptable levels. Most importantly, PROM scores suggest improved overall wellbeing and better satisfaction with the quality of life. For patients with macromastia, this technique not only focuses on cancer but also improves self-image and reduces associated physical discomfort often overlooked by women in the Indian setting. The popularization of this procedure will enable Indian patients with breast cancer to receive the benefits of breast conservation.

Differential expression of the androgen receptor gene is correlated with CAG polymorphic repeats in patients with prostate cancer

Prostate cancer (PCa) is one of the most common types of cancer in men. The aetiology of the disease is not well established, but it has been related to one of the main pathways of regulation of prostate proliferation, mediated by androgens. The androgen receptor (AR) gene encodes the androgen-receptor protein, which functions as a transactivation factor for steroid hormones. It has been proposed that the AR gene transcription levels are mediated by short tandem repeats corresponding to the CAG sequence. However, there are conflicting results in this relationship. We evaluated the expression levels of the AR gene and identified the number of CAG repeats (CAGn) in the Mexican population, establishing the relationship between expression levels and increase in the number of CAG repeats. We evaluated the expression levels of AR in tissue samples of PCa and benign prostate disease, such as benign prostatic hyperplasia, or prostatitis, to determine the difference in their expression levels. Our results showed a statistically insignificant underexpression of 0.64-fold decrease in AR levels of PCa patients compared to benign prostate disease patients (P = 0.623) and suggest that the number of CAGn was correlated with the relative expression of the AR gene (P = 0.009) and this correlation was positive, moderate, and proportional (ρ = 0.467) and no correlation was found between CAGn with other clinical features.

Protamine 1 as a secreted colorectal cancer-specific antigen facilitating G1/S phase transition under nutrient stress conditions

PurposeCancer testis antigens (CTAs) are optimal tumor diagnostic markers and involved in carcinogenesis. However, colorectal cancer (CRC) related CTAs are less reported with impressive diagnostic capability or relevance with tumor metabolism rewiring. Herein, we demonstrated CRC-related CTA, Protamine 1 (PRM1), as a promising diagnostic marker and involved in regulation of cellular growth under nutrient deficiency.MethodsTranscriptomics of five paired CRC tissues was used to screen CRC-related CTAs. Capability of PRM1 to distinguish CRC was studied by detection of clinical samples through enzyme linked immunosorbent assay (ELISA). Cellular functions were investigated in CRC cell lines through in vivo and in vitro assays.ResultsBy RNA-seq and detection in 824 clinical samples from two centers, PRM1 expression were upregulated in CRC tissues and patients` serum. Serum PRM1 showed impressive accuracy to diagnose CRC from healthy controls and benign gastrointestinal disease patients, particularly more sensitive for early-staged CRC. Furthermore, we reported that when cells were cultured in serum-reduced medium, PRM1 secretion was upregulated, and secreted PRM1 promoted CRC growth in culture and in mice. Additionally, G1/S phase transition of CRC cells was facilitated by PRM1 protein supplementation and overexpression via activation of PI3K/AKT/mTOR pathway in serum deficient medium.ConclusionsIn general, our research presented PRM1 as a specific CRC antigen and illustrated the importance of PRM1 in CRC metabolism rewiring. The new vulnerability of CRC cells was also provided with the potential to be targeted in future.Graphical abstractDiagnostic value and grow factor-like biofunction of PRM1 A represents the secretion process of PRM1 regulated by nutrient deficiency. B represents activation of PI3K/AKT/mTOR pathway of secreted PRM1.

Long non-coding RNAs as novel prognostic biomarkers for breast cancer in Egyptian women

Breast cancer (BC), the most common type of malignant tumor, is the leading cause of death, having the highest incidence rate among women. The lack of early diagnostic tools is one of the clinical obstacles for BC treatment. The current study was designed to evaluate a panel of long non-coding RNAs (lncRNAs) BC040587, HOTAIR, MALAT1, CCAT1, CCAT2, PVT1, UCA1, SPRY4-IT1, PANDAR, and AK058003—and two mRNAs (SNCG, BDNF) as novel prognostic biomarkers for BC. This study was ethically approved by the Institutional Review Board of the National Cancer Institute, Cairo University. Our study included 75 women recently diagnosed with BC and 25 healthy women as normal controls. Patients were divided into three groups: 24 with benign breast diseases, 28 with metastatic breast cancer (MBC, stage IV), and 23 with non-metastatic breast cancer (NMBC, stage III). LncRNA and mRNA expression levels were measured in patient plasma using quantitative real-time PCR. We found that 10 lncRNAs (BCO40587, HOTAIR, PVT1, CCAT2, PANDAR, CCAT1, UCA1, SPRY4-IT1, AK058003, and MALAT1) and both mRNAs demonstrated at least a 2-fold change in expression with a more than 95% probability of significance. BCO40587 and SNCG were significantly up-regulated in MBC and NMBC patients (3.2- and 4-fold, respectively) compared with normal controls. The expression of UCA1 was repressed by 1.78-fold in MBC and NMBC patients compared with those with benign diseases. SPRY4-IT1 was down-regulated by 1.45-fold in MBC patients compared with NMBC and benign disease patients. Up-regulation of lncRNAs plays an important role in BC development. SNCG and BCO40587 may be potential prognostic markers for BC.The organization number is IORG0003381 (IRB No: IRB00004025).

Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study.

BackgroundOvarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis.PurposeWe aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers.MethodsA total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis.ResultsML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001).ConclusionsBased on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

High expression of protein tyrosine phosphatase receptor S (PTPRS) is an independent prognostic marker for cholangiocarcinoma.

Cholangiocarcinoma (CCA) is an aggressive tumor of the bile duct with a high rate of mortality. Lymph node metastasis is an important factor facilitating the progression of CCA. A reliable biomarker for diagnosis, progression status, or prognosis of CCA is still lacking. To identify a novel and reliable biomarker for diagnosis/prognosis of CCA, liquid chromatography-mass spectrometry and tandem mass spectrometry (LC-MS/MS) in combination with bioinformatics analysis were applied for the representative serum samples of patients with CCA. The proteome results showed that protein tyrosine phosphatase receptor S (PTPRS) had the highest potential candidate. Then, a dot blot assay was used to measure the level of serum PTPRS in patients with CCA (n = 80), benign biliary disease patients (BBD; n = 39), and healthy controls (HC; n = 55). PTPRS level of CCA sera (14.38 ± 9.42 ng/ml) was significantly higher than that of BBD (10.7 ± 5.05 ng/ml) or HC (6 ± 3.73 ng/ml) (P < 0.0001). PTPRS was associated with serum albumin (P = 0.028), lymph node metastasis (P = 0.038), and the survival time of patients (P = 0.011). Using a log-rank test, higher serum PTPRS level was significantly (P = 0.031) correlated with a longer overall survival time of patients with CCA, and PTPRS was an independent prognostic marker for CCA superior to carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA) or alkaline phosphatase (ALP). High expression of PTPRS could be a good independent prognostic marker for CCA.