Objectives: To investigate and correlate salivary and gingival tissue metabolomic profiles in periodontitis patients and healthy controls using proton nuclear magnetic resonance (¹H NMR) spectroscopy. Methods: Forty participants were enrolled, including 20 Stage II/III Grade B periodontitis patients and 20 healthy controls. Saliva and 2×2 mm gingival tissue samples were collected and analyzed using 700 MHz NMR spectroscopy. Data were processed using univariate ROC analysis, partial least squares discriminant analysis (PLS-DA), and variable importance in projection (VIP) scores through MetaboAnalyst 6.0. Results: Salivary samples from periodontitis patients showed elevated alanine, lactate, butyrate, pyruvate, glycine, valine, proline, acetate, taurine, GABA, succinate, and trimethylamine. Gingival tissue samples exhibited increased glycine, proline, glutamate, glutamine, valine, pyruvate, glycerophosphocholine, and taurine. Glycine, proline, valine, and taurine were consistently altered in both sample types. Metabolic pathway analysis indicated involvement of amino acid metabolism, microbial fermentation, and energy metabolism. Conclusions: NMR-based metabolomics effectively identifies site-specific and systemic metabolic alterations in periodontitis. Glycine, proline, valine, and taurine demonstrate potential as diagnostic biomarkers for periodontal disease.

Prostate cancer (PCa) is the most commonly detected malignancy in men worldwide. PCa is a slow-growing cancer with the absence of symptoms at early stages. The pathogenesis has not been entirely understood including the key risk factors related to PCa development like diet and microbiota derived metabolites. Microbiota may influence the host's immunological responses, inflammatory responses, and metabolic pathways, which may be crucial for the development and metastasis. Similarly, short-chain fatty acids, methylamines, hippurate, bile acids, and other metabolites generated by microbiota may have potential roles in cancer inflammation and progression of cancer. Most studies have focused on the role of metabolites and their pathways involved in chronic inflammation, tumor initiation, proliferation, and progression. In summary, the review discusses the role of microbiota and microbial-derived metabolite-built strategies in inflammation and progression of the PCa.

ObjectivesTo evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM–DKI) and their machine‐learning‐based texture analysis for the detection and assessment of severity in prostate cancer (PCa).Materials and methodsEighty‐eight patients underwent MRI on a 3 T scanner after giving informed consent. IVIM–DKI data were acquired using 13 b values (0–2000 s/mm2) and analyzed using the IVIM–DKI model with the total variation (TV) method. PCa patients were categorized into two groups: clinically insignificant prostate cancer (CISPCa) (Gleason grade ≤ 6) and clinically significant prostate cancer (CSPCa) (Gleason grade ≥ 7). One‐way analysis‐of‐variance, t test, and receiver operating characteristic analysis was performed to measure the discriminative ability to detect PCa using IVIM–DKI parameters. A chi‐square test was used to select important texture features of apparent diffusion coefficient (ADC) and IVIM–DKI parameters. These selected texture features were used in an artificial neural network for PCa detection.ResultsADC and diffusion coefficient (D) were significantly lower (p < 0.001), and kurtosis (k) was significantly higher (p < 0.001), in PCa as compared with benign prostatic hyperplasia (BPH) and normal peripheral zone (PZ). ADC, D, and k showed high areas under the curves (AUCs) of 0.92, 0.89, and 0.88, respectively, in PCa detection. ADC and D were significantly lower (p < 0.05) as compared with CISPCa versus CSPCa. D for detecting CSPCa was high, with an AUC of 0.63. A negative correlation of ADC and D with GS (ADC, ρ = −0.33; D, ρ = −0.35, p < 0.05) and a positive correlation of k with GS (ρ = 0.22, p < 0.05) were observed. Combined IVIM–DKI texture showed high AUC of 0.83 for classification of PCa, BPH, and normal PZ.ConclusionD, f, and k computed using the IVIM–DKI model with the TV method were able to differentiate PCa from BPH and normal PZ. Texture features of combined IVIM–DKI parameters showed high accuracy and AUC in PCa detection.

•Identified 20 NSCLC biomarkers using a novel XAI-driven L1-regularized architecture.•Proposed a novel modification of standard L1-regularized gradient descent algorithm.•9 of the 20 discovered biomarkers confirm to already known lung cancer biomarkers.•12 out of 20 biomarkers are found potentially druggable.•Achieved improved accuracy of 84.95% in NSCLC subtype classification.

Background and Objective: Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes - Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data.Methods: An eXplainable AI (XAI)-driven LI-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard LI-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers. Results: The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug-Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K-M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients' likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List.Conclusion: A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC's genetic diversity, using only one omics data type may not adequately capture the tumor's complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

BACKGROUND AND OBJECTIVE:The early diagnosis of Non-small cell lung cancer (NSCLC) is of prime importance to improve the patient's survivability and quality of life. Being a heterogeneous disease at the molecular and cellular level, the biomarkers responsible for the heterogeneity aid in distinguishing NSCLC into its prominent subtypes-adenocarcinoma and squamous cell carcinoma. Moreover, if identified, these biomarkers could pave the path to targeted therapy. Through this work, a novel explainable AI (XAI)-guided deep learning framework is proposed that assists in discovering a set of significant NSCLC-relevant biomarkers using methylation data.METHODS:The proposed framework is divided into two blocks- the first block combines an autoencoder and a neural network to classify NSCLC instances. The second block utilizes various eXplainable AI (XAI) methods, namely IntegratedGradients, GradientSHAP, and DeepLIFT, to discover a set of seven significant biomarkers.RESULTS:The classification performance of the biomarkers discovered using the proposed framework is evaluated by employing multiple machine learning algorithms, among which the Multilayer Perceptron (MLP) algorithm-based model outperforms others, yielding a 10-fold cross-validation accuracy of 91.53%. An improved accuracy of 96.37% is achieved by integrating RNA-Seq, CNV, and methylation data. On performing statistical analysis using the Friedman and Nemenyi tests, the MLP model is found to be significantly better than other machine learning-based models. Further, the clinical efficacy of the resultant biomarkers is established based on their potential druggability, the likelihood of predicting NSCLC patients' survival, gene-disease association, and biological pathways targeted by them. While the biomarkers C18orf18, CCNT2, THOP1, and TNPO2, are found potentially druggable, the biomarkers CCDC15, SNORA9, THOP1, and TNPO2 are found prognostically relevant. On further analysis, some of the discovered biomarkers are found to be associated with around 104 diseases. Moreover, five KEGG, ten Reactome, and three Wiki pathways are found to be triggered by the biomarkers discovered.CONCLUSION:In summary, the proposed framework uncovers a set of clinically effective biomarkers that accurately classify NSCLC. As a future course of work, efforts would be made to combine a variety of omics data with histopathological data to unveil more precise biomarkers for devising personalized therapy.

Prostate cancer (PCa) imaging forms an important part of PCa clinical management. Magnetic resonance imaging is the modality of choice for prostate imaging. Most of the current imaging assessment is qualitative i.e. , based on visual inspection and thus subjected to inter-observer disagreement. Quantitative imaging is better than qualitative assessment as it is more objective, and standardized, thus improving interobserver agreement. Apart from detecting PCa, few quantitative parameters may have potential to predict disease aggressiveness, and thus can be used for prognosis and deciding the course of management. There are various magnetic resonance imaging-based quantitative parameters and few of them are already part of PIRADS v.2.1. However, there are many other parameters that are under study and need further validation by rigorous multicenter studies before recommending them for routine clinical practice. This review intends to discuss the existing quantitative methods, recent developments, and novel techniques in detail.

Non-Small Cell Lung Cancer (NSCLC) exhibits intrinsic heterogeneity at the molecular level that aids in distinguishing between its two prominent subtypes - Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). This paper proposes a novel explainable AI (XAI)-based deep learning framework to discover a small set of NSCLC biomarkers. The proposed framework comprises three modules - an autoencoder to shrink the input feature space, a feed-forward neural network to classify NSCLC instances into LUAD and LUSC, and a biomarker discovery module that leverages the combined network comprising the autoencoder and the feed-forward neural network. In the biomarker discovery module, XAI methods uncovered a set of 52 relevant biomarkers for NSCLC subtype classification. To evaluate the classification performance of the discovered biomarkers, multiple machine-learning models are constructed using these biomarkers. Using 10-Fold cross-validation, Multilayer Perceptron achieved an accuracy of 95.74% (±1.27) at 95% confidence interval. Further, using Drug-Gene Interaction Database, we observe that 14 of the discovered biomarkers are druggable. In addition, 28 biomarkers aid the prediction of the survivability of the patients. Out of 52 discovered biomarkers, we find that 45 biomarkers have been reported in previous studies on distinguishing between the two NSCLC subtypes. To the best of our knowledge, the remaining seven biomarkers have not yet been reported for NSCLC subtyping and could be further explored for their contribution to targeted therapy of lung cancer.

Gleason score (GS) is currently clinical predictor of prostate cancer (PCa) mortality. The present study investigates the metabolic profile of blood plasma for distinguishing aggressive PCa patients from non aggressive using targeted 1 H-NMR metabolomics. A significantly higher concentration of lactate, pyruvate, choline, dimethylamine, glucose, betaine and lower concentration of leucine, isoleucine, valine, histidine, phenylalanine, and tyrosine were observed in patients with GS 8-10 as compared to patients with GS 7 and 6. Our results suggested that pathway alterations, amino acids, phospholipids and glucose related to PCa progression. Identifying biomarker/s associated with GS can be improved diagnosis and treatment.

PDF file - 241K, Histological analysis of LC3 spatial expression patterns in relation to pimonidazole hydrochloride (Hypoxia) and Glut1 expression. MDA-MB-231 MFP tumor was histologically stained for LC3, Glut1, and pimonidazole hydrochloride. Pixel analysis was performed to highlight the areas of highest expression. High spatial concordance was observed for all three histological markers. Pixel analysis was performed using AperioTM Positive Pixel Count v9 (Red - strong positive)

Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. An automated or semi-automated PI-RADS assessment system could be beneficial in the screening process of PCa and could improve the consistency of scoring. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automatic framework for PI-RADS assessment using machine learning classifiers.

Breast cancer is a leading cause of cancer-related deaths among women. The multi-omic data has revolutionized the methodology to unravel molecular heterogeneity in breast cancer. As genetic variations captured from Copy Number Variation (CNV) data are considered the most stable amongst the multi-omic data, it leads to robust biomarkers. Thus, this paper targets the discovery of a set of CNV biomarkers for dissecting this heterogeneity. The existing algorithms yield biomarkers, too huge to be interpreted clinically. So, in this paper, we have proposed XAI-CNVMarker—an explainable AI-based post-hoc biomarker discovery framework to discover a small set of interpretable biomarkers. We exploit the power of deep learning to build DLmodel—a deep learning model for breast cancer classification. Subsequently, the trained model is analyzed using different explainable AI methods to arrive at a set of 44 CNV biomarkers. Using 5-fold cross-validation, we obtained a classification accuracy of 0.712 (± 0.048) at a 95% confidence interval. Gene set analysis revealed 37 subtype-specific enriched Reactome and Kegg pathways, 21 druggable genes, and 13 biomarkers linked with the prognostic outcome. Finally, we validated the efficacy of the identified biomarkers on METABRIC. Thus, the proposed framework demonstrates the role of explainable AI in discovering clinically reliable biomarkers.

Breast cancer—a heterogeneous disease marked with a high mortality rate, necessitates early diagnosis and treatment. The availability of multi-omic data has revolutionized our understanding of how molecular changes mark the variations in different breast cancer subtypes. Epigenomic changes in the form of DNA methylation differentially impact the expression level of the genes that play a vital role in the onset and spread of these subtypes. So, in this paper, we study the role of these variations in distinguishing between the various breast cancer subtypes. The cardinality of the existing biomarker sets is often too large to be interpreted clinically, and their relevance in classification remains unclear. In this paper, we propose a two-stage XAI-MethylMarker—an explainable AI-based biomarker discovery framework applied to DNA methylation data to arrive at a small set of biomarkers for breast cancer classification. In the first stage, we build a deep-learning network MethylNet that employs an autoencoder for dimensionality reduction and a feed-forward neural network to classify breast cancer subtypes. In the second stage, we propose a biomarker discovery algorithm, MethylBDA, which employs different explainable techniques for analyzing MethylNet model and discovers a small set of 52 biomarkers. Using 5-fold cross-validation, we achieved a classification accuracy of 0.8145 ± 0.07 at a 95% confidence interval. To establish the clinical relevance of the discovered biomarkers, we performed a gene set analysis that revealed 14 druggable genes, nine genes linked to prognostic outcomes, and several enriched pathways are known to be significantly associated with distinct breast cancer subtypes.

PDF file - 344K, (A) Reference guide for the phospho-protein array. (B) Representative fluorescent arrays for whole cell lysates from MDA-MB-231 cells cultured at pH 7.4 or pH 6.7 for 72 hours. (C-D) Quantitative analysis of fluorescence intensity for all phosphorylated proteins. Data represent the mean S.D. of two replicate arrays for each sample

PDF file - 431K, Histological analysis of in-vivo LC3 expression in HS766T subcutaneous tumors buffered with sodium bicarbonate. (A) Whole cell lysates from HS766T cells cultured at neutral pH 7.4 or pH 6.7 for 48 hours were analyzed for LC3-II expression. (B) Positive pixel analysis was completed for LC3 staining on whole tissue cross sections from HS766T tumors treated with tap or NaHCO3. An overall significant decrease in total positive and strong positive LC3 pixels was observed in NaHCO3 treated samples. The data are plotted as the mean standard deviation of three whole tumor cross sections from each treatment group. (C) A 5x magnification of representative tissue regions from HS766T tumors stained for LC3