Benign Samples Research Articles

Low SMARCD3 expression is associated with poor prognosis in patients with prostate cancer.

SWI/SNF complexes represent a family of multi-subunit chromatin remodelers that are affected by alterations in >20% of human tumors. While mutations of SWI/SNF genes are relatively uncommon in prostate cancer (PCa), the literature suggests that deregulation of various subunits plays a role in prostate tumorigenesis. To assess SWI/SNF functions in a clinical context, we studied the mutually exclusive, paralogue accessory subunits SMARCD1, SMARCD2, and SMARCD3 that are included in every known complex and are sought to confer specificity. Performing immunohistochemistry (IHC), the protein levels of the SMARCD family members were measured using a tissue microarray (TMA) comprising malignant samples and matching healthy tissue of non-metastatic PCa patients (n = 168). Moreover, IHC was performed in castration-resistant tumors (n = 9) and lymph node metastases (n = 22). To assess their potential role as molecular biomarkers, SMARCD1 and SMARCD3 protein levels were correlated with clinical parameters such as T stage, Gleason score, biochemical recurrence, and progression-free survival. SMARCD1 protein levels in non-metastatic primary tumors, lymph node metastases, and castration-resistant samples were significantly higher than in benign tissues. Likewise, SMARCD3 protein expression was elevated in tumor tissue and especially lymph node metastases compared to benign samples. While SMARCD1 levels in primary tumors did not exhibit significant associations with any of the tested clinical parameters, SMARCD3 exhibited an inverse correlation with pre-operative PSA levels. Moreover, low SMARCD3 expression was associated with progression to metastasis. In congruence with previous literature, our results implicate that both SMARCD1 and SMARCD3 may exhibit relevant functions in the context of prostate tumorigenesis. Moreover, our approach suggests a potential role of SMARCD3 as a novel prognostic marker in clinically non-metastatic PCa.

Differential Urinary Microbiome and Its Metabolic Footprint in Bladder Cancer Patients Following BCG Treatment.

Recent studies have identified a urinary microbiome, dispelling the myth of urine sterility. Intravesical bacillus Calmette-Guérin (BCG) therapy is the preferred treatment for intermediate to high-risk non-muscle-invasive bladder cancer (BCa), although resistance occurs in 30-50% of cases. Progression to muscle-invasive cancer necessitates radical cystectomy. Our research uses 16S rRNA gene sequencing to investigate how the urinary microbiome influences BCa and its response to BCG therapy. Urine samples were collected via urethral catheterization from patients with benign conditions and non-muscle-invasive BCa, all of whom underwent BCG therapy. We utilized 16S rRNA gene sequencing to analyze the bacterial profiles and metabolic pathways in these samples. These pathways were validated using a real metabolite dataset, and we developed predictive models for malignancy and BCG response. In this study, 87 patients participated, including 29 with benign diseases and 58 with BCa. We noted distinct bacterial compositions between benign and malignant samples, indicating the potential role of the toluene degradation pathway in mitigating BCa development. Responders to BCG had differing microbial compositions and higher quinolone synthesis than non-responders, with two Bifidobacterium species being prevalent among responders, associated with prolonged recurrence-free survival. Additionally, we developed highly accurate predictive models for malignancy and BCG response. Our study delved into the mechanisms behind malignancy and BCG responses by focusing on the urinary microbiome and metabolic pathways. We pinpointed specific beneficial microbes and developed clinical models to predict malignancy and BCG therapy outcomes. These models can track recurrence and facilitate early predictions of treatment responses.

Application of Machine Learning Models for Malware Classification With Real and Synthetic Datasets

Stacking of multiple Machine Learning (ML) classifiers have gained popularity in addressing anomalous data classification along with Deep Learning (DL) algorithms. This study compares traditional ML classifiers, multi-layer stacking ML classifiers, and DL classifiers using an open-source malware dataset-containing equal numbers of benign and malware samples. The results on the realistic dataset indicate that the DL classifier, utilizing a Bidirectional Long Short-Term Memory (BiLSTM) model, outperformed the stacked classifiers with Logistic Regression (LR) and Support Vector Machine (SVM) as Meta learners by 36.78% and 39.69%, respectively, in terms of classification accuracy and performance. The research work was extended to study the impact of Generative Adversarial Network (GAN) based synthetic dataset of relatively smaller size on deep learning models. It was observed that the Deep Learning Multi-Layer Perceptron (DLMLP) Model had relatively superior performance as compared to complex deep learning models like Long Short-Term Memory LSTM and BiLSTM

Strategic safeguarding: A game theoretic approach for analyzing attacker-defender behavior in DNN backdoors

Deep neural networks (DNNs) are fundamental to modern applications like face recognition and autonomous driving. However, their security is a significant concern due to various integrity risks, such as backdoor attacks. In these attacks, compromised training data introduce malicious behaviors into the DNN, which can be exploited during inference or deployment. This paper presents a novel game-theoretic approach to model the interactions between an attacker and a defender in the context of a DNN backdoor attack. The contribution of this approach is multifaceted. First, it models the interaction between the attacker and the defender using a game-theoretic framework. Second, it designs a utility function that captures the objectives of both parties, integrating clean data accuracy and attack success rate. Third, it reduces the game model to a two-player zero-sum game, allowing for the identification of Nash equilibrium points through linear programming and a thorough analysis of equilibrium strategies. Additionally, the framework provides varying levels of flexibility regarding the control afforded to each player, thereby representing a range of real-world scenarios. Through extensive numerical simulations, the paper demonstrates the validity of the proposed framework and identifies insightful equilibrium points that guide both players in following their optimal strategies under different assumptions. The results indicate that fully using attack or defense capabilities is not always the optimal strategy for either party. Instead, attackers must balance inducing errors and minimizing the information conveyed to the defender, while defenders should focus on minimizing attack risks while preserving benign sample performance. These findings underscore the effectiveness and versatility of the proposed approach, showcasing optimal strategies across different game scenarios and highlighting its potential to enhance DNN security against backdoor attacks.

B-314 Utility of he4 in differential diagnosis of pleural effusions

Abstract Background The usefulness of tumor markers in the differential diagnosis of cancer in patients with pleural effusions (PE) remains controversial. Few studies have reported the measurement of human epididymal protein 4 (HE4) in pleural fluid. HE4 is a tumor marker associated with different types of cancer. An elevated serum concentration has been observed in ovarian and lung cancers. Other benign pathologies can increase HE4 levels, which can be detect with different laboratory tests identified with adenosine deaminase (ADA), C-reactive protein (CRP), % polynuclear cell count (%PMN) and glomerular filtration rate (GFR) can be used in order to detect possible false positives. The objective of this study was to assess the diagnostic usefulness of HE4 in malignant pleural effusions by comparing HE4 concentrations between benign and malignant samples, while considering the causes of HE4 false positives. Methods HE4, ADA, differential leukocyte count, CRP and GFR concentrations were determined in 238 pleural effusion samples. Diagnostic efficacy analysis using receiver operating curves (ROC) identified the ability of HE4 to detect malignancy, searching for cut-off points with 100% specificity. Results HE4 concentrations showed significant differences (P<0.01) between malignant (median 1065pmol/L) and benign (median 699pmol/L) pleural effusions. Significant differences (P<0.01) were found in HE4 concentrations for gynecological and lung cancer origin (median 7397 pmol/L and 5150 pmol/L respectively) and other malignant pleural effusions (median 1065 pmol/L). Patients with benign diseases with renal failure showed a higher HE4 concentration (median 964pmol/L) than in patients without these pathologies (median 696pmol/L) identified with GFR<30 mL/min. No differences were demonstrated in HE4 concentration levels using ADA, differential leukocyte count or CRP, so only glomerular filtration rate was considered cause of false positives. A sensitivity of 23% was obtained for HE4, with a cut-off point of 3050pmol/L and a specificity of 100% for the whole group. In the potential false positive group, the sensitivity of HE4 was 28% with a cutoff point of 3050 pmol/L with a specificity of 100%. In the group without potential false positives, we obtained a sensitivity of 30% with a specificity of 100% for a cutoff point of 1992pmol/L. When we used a specific cut-off point for each group, we obtained a sensitivity of 38% with a specificity of 100%. Conclusions HE4 levels are higher in malignant pleural effusions compared to other pathologies. HE4 levels allow the identification of malignant pleural effusions with moderate sensitivity and maximum specificity. HE4 levels can differentiate between tumors of gynecological or lung origin and others. Glomerular filtration rate (GFR<30 mL/min) was considered the main cause of false positives. If we identify possible false positives, we can use specific cut-offs and improve sensitivity up to 38% with maximum specificity compared to using a single cut-off for all effusions.

Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks.

Despite providing high-performance solutions for computer vision tasks, the deep neural network (DNN) model has been proved to be extremely vulnerable to adversarial attacks. Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. Besides, commonly used adaptive learning and fine-tuning technique is unsuitable for adversarial defense since it is essentially a zero-shot problem when deployed. Thus, to tackle this challenge, we propose an attack-agnostic defense method named Meta Invariance Defense (MID). Specifically, various combinations of adversarial attacks are randomly sampled from a manually constructed Attacker Pool to constitute different defense tasks against unknown attacks, in which a student encoder is supervised by multi-consistency distillation to learn the attack-invariant features via a meta principle. The proposed MID has two merits: 1) Full distillation from pixel-, feature- and prediction-level between benign and adversarial samples facilitates the discovery of attack-invariance. 2) The model simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration. Theoretical and empirical studies on numerous benchmarks such as ImageNet verify the generalizable robustness and superiority of MID under various attacks.

Dual Attention Adversarial Attacks With Limited Perturbations.

The construction of undetectable adversarial examples with few perturbances remains a difficult problem in adversarial attacks. At present, most solutions use the standard gradient optimization algorithm to build adversarial examples by applying global perturbations to benign samples and then launch attacks on the targets (e.g., face recognition systems). However, when the perturbance size is limited, the performance of these approaches suffers substantially. The content of crucial places in an image, on the other hand, will impact the final prediction; if these areas can be investigated and limited perturbances introduced, an acceptable adversarial example will be constructed. Based on the foregoing research, this article offers a dual attention adversarial network (DAAN) to produce adversarial examples with limited perturbations. DAAN initially searches for effective areas in an input image using the spatial attention network and channel attention network, and then creates space and channel weights. Following that, these weights direct an encoder and a decoder to generate effective perturbation, which is then combined with the input to produce an adversarial example. Finally, the discriminator determines if the created adversarial examples are true or false, and the attacked model is utilized to determine whether the generated samples fit the attack targets. Extensive studies on various datasets show that DAAN not only delivers the best attack performance across all comparison algorithms with few perturbations, but it can also significantly improve the defensiveness of the attacked models.

Clinical cell-surface targets in metastatic and primary solid cancers.

Therapies against cell-surface targets (CSTs) represent an emerging treatment class in solid malignancies. However, high-throughput investigations of CST expression across cancer types have been reliant on data sets of mostly primary tumors, despite therapeutic use most commonly in metastatic disease. We identified a total of 818 clinical trials of CST therapies with 78 CSTs. We assembled a data set spanning RNA-seq and microarrays in 7,927 benign samples, 16,866 primary tumor samples, and 6,124 metastatic tumor samples. We also utilized single-cell RNA-seq data from 36 benign tissues and 558 primary and metastatic tumor samples, and matched RNA versus protein expression in 29 benign tissue samples, 1,075 tumor samples, and 942 cell lines. High RNA expression accurately predicted high protein expression across CST therapies in benign tissues, tumor samples, and cell lines. We compared metastatic versus primary tumor expression, identified potential opportunities for repositioning, and matched cell lines to tumor types based on CST and global RNA expression. We evaluated single-cell heterogeneity across tumors, and identified rare normal cell subpopulations that may contribute to toxicity. Finally, we identified combinations of CST therapies for which bispecific approaches could improve tumor specificity. This study helps better define the landscape of CST expression in metastatic and primary cancers.

Increased peritoneal TGF-β1 is associated with ascites-induced NK-cell dysfunction and reduced survival in high-grade epithelial ovarian cancer.

Natural killer (NK) cell therapy represents an attractive immunotherapy approach against recurrent epithelial ovarian cancer (EOC), as EOC is sensitive to NK cell-mediated cytotoxicity. However, NK cell antitumor activity is dampened by suppressive factors in EOC patient ascites. Here, we integrated functional assays, soluble factor analysis, high-dimensional flow cytometry cellular component data and clinical parameters of advanced EOC patients to study the mechanisms of ascites-induced inhibition of NK cells. Using a suppression assay, we found that ascites from EOC patients strongly inhibits peripheral blood-derived NK cells and CD34+ progenitor-derived NK cells, albeit the latter were more resistant. Interestingly, we found that higher ascites-induced NK cell inhibition correlated with reduced progression-free and overall survival in EOC patients. Furthermore, we identified transforming growth factor (TGF)-β1 to correlate with ascites-induced NK cell dysfunction and reduced patient survival. In functional assays, we showed that proliferation and anti-tumor reactivity of CD34+ progenitor-derived NK cells are significantly affected by TGF-β1 exposure. Moreover, inhibition of TGF-β1 signaling with galunisertib partly restored NK cell functionality in some donors. For the cellular components, we showed that the secretome is associated with a different composition of CD45+ cells between ascites of EOC and benign reference samples with higher proportions of macrophages in the EOC patient samples. Furthermore, we revealed that higher TGF-β1 levels are associated with the presence of M2-like macrophages, B cell populations and T-regulatory cells in EOC patient ascites. These findings reveal that targeting TGF-β1 signaling could increase NK cell immune responses in high-grade EOC patients.

Abstract B023: Unravelling fibroinflammatory and immune signatures for pancreatic cancer early detection

Abstract Pancreatic cancer is associated with late-stage diagnosis and poor patient prognosis. The disease is characterised by chronic dysregulated immunity and inflammation. Moreover, chronic pancreatitis is linked with an increased risk of pancreatic cancer. Tissue remodelling during disease progression involves a complex network of cells and a crosstalk between cancer cells, stromal components and immune cells, but it remains poorly explored. Thus, to improve the outcome of patients, there is a need for better understanding of biological changes associated with the initiation and progression of pancreatic cancer at the molecular and spatial levels. Using samples obtained from our Accelerated Diagnosis of neuroEndocrine and Pancreatic TumourS (ADEPTS) biobank, we have evaluated blood samples for the discovery of non- invasive biomarkers to discriminate patients with pancreatic cancer from symptomatic patients without cancer. In this pilot study, the transcriptome of peripheral blood mononuclear cells (PBMCs), isolated using a ficoll gradient was explored to identify PDAC-associated mRNA signatures. Moreover, the Olink® Explore Inflammation and Oncology panels were performed in a cohort of 40 patients with early stage (I-II) PDAC, 40 chronic pancreatitis samples and 40 control patients with benign symptomatic upper GI diseases. To validate and enrich these results, a panel of inflammatory and immune markers including immunoglobulins, cytokines and chemokines was also run using multiplex techniques. Symptoms observed in our cohort were also analysed together with clinical (e.g diabetic status, biochemistry profile) and demographic data (age, sex, ethnicity, post code etc). We have identified mRNA transcriptome signatures and differentially expressed proteins associated with inflammatory and immunological processes in PDAC compared to benign symptomatic samples. Our results offer potential diagnostic utility for early-stage disease. Additionally, our unique cohort of patient samples comprising of those with confirmed PDAC and those presenting to clinic with benign disease and similar non-specific symptoms, allows for the development of non-invasive diagnostic tests with better translational applicability. Future work will involve the validation of these findings in a larger patient cohort and the use of NanoString GeoMx® Digital Spatial Profiler to perform whole transcriptome and proteome profiling in human chronic pancreatic and pancreatic cancer tissue samples. Citation Format: Kyra Fraser, Maria Rosado, Stephen P Pereira, Pilar Acedo. Unravelling fibroinflammatory and immune signatures for pancreatic cancer early detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B023.

Non-Invasive Endometrial Cancer Screening through Urinary Fluorescent Metabolome Profile Monitoring and Machine Learning Algorithms.

Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n = 77), patients with benign uterine tumors (n = 23), and control gynecological patients attending regular checkups or follow-ups (n = 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods.

Precision strike: Precise backdoor attack with dynamic trigger

Deep neural networks have advanced significantly in the last several years and are now widely employed in numerous significant real-world applications. However, recent research has shown that deep neural networks are vulnerable to backdoor attacks. Under such attacks, attackers release backdoor models that achieve satisfactory performance on benign samples while behaving abnormally on inputs with predefined triggers. Successful backdoor attacks can have serious consequences, such as attackers using backdoor generation methods to bypass critical face recognition authentication systems. In this paper, we propose PBADT, a precise backdoor attack with dynamic trigger. Unlike existing work that uses static or random trigger masks, we design an interpretable trigger mask generation framework that places triggers at positions that have the most significant impact on the prediction results. Meanwhile, backdoor attacks are made more efficient by using forgettable events to improve the efficiency of backdoor attacks. The proposed backdoor method is extensively evaluated on three face recognition datasets, LFW, CelebA, and VGGFace, while further evaluated on two general image datasets, CIFAR-10 and GTSRB. Our approach achieves almost perfect attack performance on backdoor data.

Mixed training backdoor attack method improves generalization of backdoor attacks: An empirical study based on multiple models and datasets

In recent years, backdoor attacks have posed significant security threats to the training process of deep neural networks (DNNs). Attackers attempt to embed triggers in the training set, causing the victim model to behave normally when processing benign samples, but maliciously alter predictions when the hidden backdoor is activated by these triggers. However, our research found that when the trigger locations differ between the training and test data, the success rate of backdoor attacks decreases. This finding was consistent across different models and datasets through our experiments. To address the issue of decreased attack success rates caused by inconsistent trigger distributions between training and test data, we propose a mixed training backdoor attack method. This method enhances the generalization of attacks by constructing backdoor attack data with triggers in various positions during the training phase. When applying this mixed training backdoor attack approach to the BadNets and Blended attack methods, the ASR (Attack Success Rate) on test sets improved by up to 99.57% and 94.96%, respectively, across different models and datasets.

Overcoming challenges in conducting early phase breast cancer prevention trials: Bazedoxifene and conjugated estrogens vs waitlist control

BackgroundThe combination of bazedoxifene 20 mg (BZA) and conjugated estrogens 0.45 mg (CE) marketed as Duavee® is approved for vasomotor symptom relief and osteoporosis prevention. Our pilot study suggested it had potential breast cancer risk reduction, and we proposed a multisite Phase IIB primary prevention trial assessing change in breast imaging and tissue risk biomarkers. By the time funding was acquired in February 2021, Duavee® was unavailable with an uncertain return date. A redesign was needed to salvage the study. MethodsThe basic trial design was minimally altered. Women age 45–64 at elevated risk for breast cancer with vasomotor symptoms and no menses for at least 2 months have mammography, phlebotomy, and benign breast tissue sampling before and after 6 months of intervention. However, instead of Duavee® (single pill) vs placebo, women are randomized to 6 months of BZA + CE vs Waitlist. Those initially randomized to Waitlist can receive BZA + CE after 6 months. The primary endpoint is between arm difference in change in a fully automated measure of mammographic density with blood and tissue-based secondary endpoints. OutcomesAccrual initiation was delayed due to contractual difficulties surrounding BZA importation during COVID-19 and deploying a fully automated method (Volpara®) to assess the primary endpoint. To accommodate this delay, a mid-grant no cost extension along with amended eligibility requirements were employed. 61/120 participants needed were entered in the initial 27 months of accrual and 37 months of funding. Despite a late start, accrual is likely to be completed within the funding period.

Detecting adversarial samples by noise injection and denoising

Deep learning models are highly vulnerable to adversarial examples, leading to significant attention on techniques for detecting them. However, current methods primarily rely on detecting image features for identifying adversarial examples, often failing to address the diverse types and intensities of such examples. We propose a novel adversarial example detection method based on perturbation estimation and denoising to overcome this limitation. We develop an autoencoder to predict the latent adversarial perturbations of samples and select appropriately sized noise based on these predictions to cover the perturbations. Subsequently, we employ a non-blind denoising autoencoder to remove noise and residual perturbations effectively. This approach allows us to eliminate adversarial perturbations while preserving the original information, thus altering the prediction results of adversarial examples without affecting predictions on benign samples. Inconsistencies in predictions before and after processing by the model identify adversarial examples. Our experiments on datasets such as MNIST, CIFAR-10, and ImageNet demonstrate that our method surpasses other advanced detection methods in accuracy.

Multiple DNA Viruses and HPV Integration in Inverted Papilloma and Associated Sinonasal Carcinoma.

Sinonasal inverted papilloma (IP) has a locally destructive growth pattern, can relapse, and can undergo malignant transformation (IP-associated sinonasal squamous cell carcinoma (IP-SNSCC)). Human papillomaviruses (HPV)-6 and -16 are frequently detected in IPs. To clarify the possible roles of other DNA viruses in IPs, we explored viruses not studied in this context before. With the setting of pre- and post-malignant transformation samples, we investigated HPV genomes in depth to assess the integration of HPV into the human genome and the presence of minor intratypic variants. We analyzed 35 IP samples representing 28 individuals, of which six had IP-SNSCC. For virus screening, we applied qPCR to detect 16 different DNA viruses in three virus families, comprising herpesviruses, parvoviruses, and polyomaviruses. In addition, targeted next generation sequencing (NGS) was used for detailed HPV analysis. We detected herpes-, parvo-, and polyomaviruses in 13/28 (46%) patients, with codetections of multiple viruses in six (21%) patients. NGS revealed HPV16 DNA in 2/6 IP-SNSCC and in their respective earlier benign IP samples, as well as in a plasma sample from one of these patients. HPV6 was detected in two IP samples without subsequent malignant transformation. We identified sequence reads containing junctions of HPV6 and HPV16 and host genome suggestive of viral integration. HPV6 and HPV16 minor intratypic variants were present across pre- and post-malignant transformation, with mostly nonsynonymous mutations. Multiple DNA viruses were present in IPs. HPV16 was detected only in IP-SNSCCs or in tumors that later underwent malignant transformation. 3 Laryngoscope, 2024.

Expression of Growth Hormone-Releasing Hormone and Its Receptor Splice Variants in a Cohort of Hungarian Pediatric Patients with Hematological and Oncological Disorders: A Pilot Study.

Hematological and oncological diseases are still among the leading causes of childhood mortality. Expression of growth hormone-releasing hormone (GHRH) and its receptors (GHRH-R) has been previously demonstrated in various human tumors, but very limited findings are available about the presence and potential function of GHRH-Rs in oncological and hematological disorders of children. In this study, we aimed to investigate the expression of mRNA for GHRH and splice variant 1 (SV) of GHRH-R in 15 pediatric hematological/oncological specimens by RT-PCR. The presence and binding characteristics of GHRH-R protein were also studied by Western blot and ligand competition assays. Of the fifteen specimens studied, eleven pediatric samples (73%) showed the expression of mRNA for GHRH. These eleven samples also expressed mRNA for GHRH receptor SV1. GHRH-R protein was found to be expressed in two benign tumor samples and five malignant tumors examined by Western blot. The presence of specific, high affinity binding sites on GHRH-R was demonstrated in all of the seven human pediatric solid tumor samples investigated. Our results show that the expression of GHRH and SV1 of GHRH-R in hemato-oncological diseases in children can pave the way for further investigation of GHRH-Rs as potential molecular targets for diagnosis and therapy.

Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400–950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Detection of differentially methylated CpGs between tumour and adjacent benign cells in diagnostic prostate cancer samples

Differentially methylated CpG sites (dmCpGs) that distinguish prostate tumour from adjacent benign tissue could aid in the diagnosis and prognosis of prostate cancer. Previously, the identification of such dmCpGs has only been undertaken in radical prostatectomy (RP) samples and not primary diagnostic tumour samples (needle biopsy or transurethral resection of the prostate). We interrogated an Australian dataset comprising 125 tumour and 43 adjacent histologically benign diagnostic tissue samples, including 41 paired samples, using the Infinium Human Methylation450 BeadChip. Regression analyses of paired tumour and adjacent benign samples identified 2,386 significant dmCpGs (Bonferroni p < 0.01; delta-β ≥ 40%), with LASSO regression selecting 16 dmCpGs that distinguished tumour samples in the full Australian diagnostic dataset (AUC = 0.99). Results were validated in independent North American (npaired = 19; AUC = 0.87) and The Cancer Genome Atlas (TCGA; npaired = 50; AUC = 0.94) RP datasets. Two of the 16 dmCpGs were in genes that were significantly down-regulated in Australian tumour samples (Bonferroni p < 0.01; GSTM2 and PRKCB). Ten additional dmCpGs distinguished low (n = 34) and high Gleason (n = 88) score tumours in the diagnostic Australian dataset (AUC = 0.95), but these performed poorly when applied to the RP datasets (North American: AUC = 0.66; TCGA: AUC = 0.62). The DNA methylation marks identified here could augment and improve current diagnostic tests and/or form the basis of future prognostic tests.

LCP1 correlates with immune infiltration: a prognostic marker for triple-negative breast cancer

ObjectiveTriple-Negative Breast Cancer (TNBC) is known for its aggressiveness and treatment challenges due to the absence of ER, PR, and HER2 receptors. Our work emphasizes the prognostic value of LCP1 (Lymphocyte cytosolic protein 1), which plays a crucial role in cell processes and immune cell activity, to predict outcomes and guide treatments in TNBC.MethodsWe explored LCP1 as a potential biomarker in TNBC and investigated the mRNA and protein expression levels of LCP1. We investigated different databases, including GTEX, TCGA, GEO, cBioPortal and Kaplan-Meier Plotter. Immunohistochemistry on TNBC and benign tumor samples was performed to examine LCP1's relationship with patient clinical characteristics and macrophage markers. We also assessed survival rates, immune cell infiltration, and drug sensitivity related to LCP1 using various bioinformatics tools.ResultsThe results indicated that LCP1 expression was higher in TNBC tissues compared to adjacent normal tissues. However, high expression of LCP1 was significantly associated with favorable survival outcomes in patients with TNBC. Enrichment analysis revealed that genes co-expressed with LCP1 were significantly enriched in various immune processes. LCP1 showed a positive correlation with the infiltration of resting dendritic cells, M1 macrophages, and memory CD4 T cells, and a negative correlation with M2 macrophages. Further analysis suggested a link between high levels of LCP1 and increased survival outcomes in cancer patients receiving immunotherapy.ConclusionLCP1 may serve as a potential diagnostic and prognostic biomarker for TNBC, which was closely associated with immune cell infiltration, particularly M1 and M2 macrophages. Our findings may provide valuable insights into immunotherapeutic strategies for TNBC patients.