Lipid Wizard: Analysis Software for Comprehensive Two-Dimensional Liquid Chromatography-Mass Spectrometry-Based Lipid Profiling.

Lipids play a significant role in life activities and participate in the biological system through different pathways. Although comprehensive two-dimensional liquid chromatography-mass spectrometry (2DLC-MS) has been developed to profile lipid abundance changes, lipid identification and quantification from 2DLC-MS data remain a challenge. We created Lipid Wizard, open-source software for lipid assignment and isotopic peak stripping of the 2DLC-MS data. Lipid Wizard takes the peak list deconvoluted from the 2DLC-MS data as input and assigns each isotopic peak to the lipids recorded in the LIPID MAPS database by precursor ion m/z matching. The matched lipids are then filtered by the first-dimension retention time (1D RT), followed by the second-dimension retention time (2D RT), where the 2D RT of each lipid is predicted using an equivalent carbon number (ECN) model. The remaining assigned lipids are used for isotopic peak stripping via an iterative linear regression. The performance of Lipid Wizard was tested using a set of lipid standards and then applied to study the lipid changes in the livers of mice (fat-1) fed with alcohol.

Mitochondrial and cytosolic one-carbon metabolism is a targetable metabolic vulnerability in cisplatin-resistant ovarian cancer.

One-carbon (C1) metabolism is compartmentalized between the cytosol and mitochondria with the mitochondrial C1 pathway as the major source of glycine and C1 units for cellular biosynthesis. Expression of mitochondrial C1 genes including SLC25A32, serine hydroxymethyl transferase (SHMT) 2, 5,10-methylene tetrahydrofolate dehydrogenase 2, and 5,10-methylene tetrahydrofolate dehydrogenase 1-like was significantly elevated in primary epithelial ovarian cancer (EOC) specimens compared to normal ovaries. 5-Substituted pyrrolo[3,2-d]pyrimidine antifolates (AGF347, AGF359, AGF362) inhibited proliferation of cisplatin sensitive (A2780, CaOV3, IGROV1) and resistant (A2780-E80, SKOV3) EOC cells. In SKOV3 and A2780-E80 cells, colony formation was inhibited. AGF347 induced apoptosis in SKOV3 cells. In IGROV1 cells, AGF347 was transported by folate receptor (FR) α. AGF347 was also transported into IGROV1 and SKOV3 cells by the proton-coupled folate transporter (SLC46A1) and the reduced folate carrier (SLC19A1). AGF347 accumulated to high levels in the cytosol and mitochondria of SKOV3 cells. By targeted metabolomics with [2,3,3-2H]L-serine, AGF347, AGF359 and AGF362 inhibited SHMT2 in the mitochondria. In the cytosol, SHMT1 and de novo purine biosynthesis (i.e., glycinamide ribonucleotide formyltransferase, 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase) were targeted; AGF359 also inhibited thymidylate synthase. Antifolate treatments of SKOV3 cells depleted cellular glycine, mitochondrial NADH and glutathione, and showed synergistic in vitro inhibition toward SKOV3 and A2780-E80 cells when combined with cisplatin. In vivo studies with subcutaneous SKOV3 EOC xenografts in SCID mice confirmed significant antitumor efficacy of AGF347. Collectively, our studies demonstrate a unique metabolic vulnerability in EOC involving mitochondrial and cytosolic C1 metabolism that offers a promising new platform for therapy.

Panobinostat sensitizes AraC-resistant AML cells to the combination of azacitidine and venetoclax.

The majority of acute myeloid leukemia (AML) patients respond to intensive induction therapy, consisting of cytarabine (AraC) and an anthracycline, though more than half experience relapse. Relapsed/refractory (R/R) AML patients are difficult to treat, and their clinical outcomes remain dismal. Venetoclax (VEN) in combination with azacitidine (AZA) has provided a promising treatment option for R/R AML, though the overall survival (OS) could be improved (OS ranges from 4.3 to 9.1 months). Overexpression of c-Myc is associated with chemoresistance in AML. Histone deacetylase (HDAC) inhibitors have been shown to suppress c-Myc and enhance the antileukemic activity of VEN, as well as AZA, though combination of all three has not been fully explored. In this study, we investigated the HDAC inhibitor, panobinostat, in combination with VEN + AZA against AraC-resistant AML cells. Panobinostat treatment downregulated c-Myc and Bcl-xL and upregulated Bim, which enhanced the antileukemic activity of VEN + AZA against AraC-resistant AML cells. In addition, panobinostat alone and in combination with VEN + AZA suppressed oxidative phosphorylation and/or glycolysis in AraC-resistant AML cells. These findings support further development of panobinostat in combination with VEN + AZA for the treatment of AraC-resistant AML.

Human epidermal growth factor receptor-2 (HER2) expression in FIGO3 high-grade endometrial endometrioid carcinoma: Clinicopathologic characteristics and future directions.

OBJECTIVES: To study the expression of HER2 in high-grade FIGO3 endometrial endometroid carcinoma (EEC) and to correlate our findings with the clinicopathologic characteristics of this tumor. METHODS: HER2 expression by immunohistochemistry (IHC) was performed on 10% formalin-fixed paraffin embedded tissue on cases diagnosed as FIGO3 EEC. HER2 expression was interpreted as negative (0), low (1+ and 2+) or positive (3+) using similar criteria as in the breast. HER2 amplification by Fluorescence in situ hybridization (FISH) was performed on cases interpreted as 2+ and 3+ by IHC. RESULTS: One hundred and forty-three FIGO3 EEC were identified. Of these, 70 (49%) cases were HER2 negative (IHC 0), and 73 (51%) cases expressed/amplified HER2 by IHC and/or FISH. Of the 73 cases expressing or amplifying HER2, 59 cases were IHC 1+, 12 cases were IHC 2+, and 2 cases were IHC 3+. FISH testing was performed in 12 cases. Only one of the two HER2 IHC 3+ cases showed HER2 gene amplification by FISH and the other 11 cases were not amplified. The 5-year overall survival (OS) rate for HER2 IHC 1+ cases was 92.20% (95% CI: 83.97-100.00), and the 5-year OS rate for HER2 IHC 2+/3+ cases was 89.50% (95% CI: 56.41-100.00). CONCLUSION: Our findings indicate that about one half of FIGO3 EEC variably expresses HER2 and with the emerging concept of "HER2 low", anti-HER2 agents may be explored as potential therapeutic options in these patients, for possible survival benefits.

Mechanistic Modeling of Intrathecal Chemotherapy Pharmacokinetics in the Human Central Nervous System.

PURPOSE: The pharmacokinetics of intrathecally administered antibody or small-molecule drugs in the human central nervous system (CNS) remains poorly understood. This study aimed to provide mechanistic and quantitative perspectives on the CNS pharmacokinetics of intrathecal chemotherapy, by using a physiologically based pharmacokinetic (PBPK) modeling approach. EXPERIMENTAL DESIGN: A novel CNS PBPK model platform was developed and verified, which accounted for the human CNS general anatomy and physiologic processes governing drug distribution and disposition. The model was used to predict CNS pharmacokinetics of antibody (trastuzumab) and small-molecule drugs (methotrexate, abemaciclib, tucatinib) following intraventricular injection or intraventricular 24-hour infusion, and to assess the key determinants of drug penetration into the deep brain parenchyma. RESULTS: Intraventricularly administered antibody and small-molecule drugs exhibited distinct temporal and spatial distribution and disposition in human CNS. Both antibody and small-molecule drugs achieved supratherapeutic or therapeutic concentrations in the cerebrospinal fluid (CSF) compartments and adjacent brain tissue. While intrathecal small-molecule drugs penetrated the deep brain parenchyma to a negligible extent, intrathecal antibodies may achieve therapeutic concentrations in the deep brain parenchyma. Intraventricular 24-hour infusion enabled prolonged CNS exposure to therapeutically relevant concentrations while avoiding excessively high and potentially neurotoxic drug concentrations. CONCLUSIONS: CNS PBPK modeling, in line with available clinical efficacy data, confirms the therapeutic value of intrathecal chemotherapy with antibody or small-molecule drugs for treating neoplastic meningitis and warrants further clinical investigation of intrathecal antibody drugs to treat brain parenchyma tumors. Compared with intraventricular injection, intraventricular 24-hour infusion may mitigate neurotoxicity while retaining potential efficacy.

Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images.

Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931-0.974). Additionally, the paper presents visualizations of the model's predictions, including confidence scores and heatmaps that highlight the model's focal points-particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning.

Ocular Involvement and Treatment Pattern in Korean Patients with Marfan Syndrome: A Population-Based Study.

PURPOSE: This study aimed to investigate the incidence and prevalence of, and treatment patterns for ocular complications in Korean patients with Marfan syndrome. METHODS: Incidence and prevalence of Marfan syndrome was calculated from 2010 to 2018, based on data from the Korean National Health Insurance Service (KNHIS). Diagnosis codes (for cataract, ectopia lentis, retinal detachment, etc.) and surgery reimbursement codes (lensectomy, phacoemulsification, buckling, vitrectomy, etc.) in the patients with Marfan syndrome were retrieved by complete enumeration of the data. RESULTS: The annual prevalence of Marfan syndrome adjusted by age and sex was gradually increased from 2.44 per 100,000 in 2010 to 4.36 per 100,000 in 2018. The age group of 10-19 years showed the highest prevalence. The prevalence of ectopia lentis was 21.7%, of whom 43.0% underwent surgeries. Surgery for RD was performed in 253 (14.1%) of 2044 patients during the study period. CONCLUSION: Although the most prevalent ophthalmologic manifestation was ectopia lentis, total prevalence rate of RD was more than 10% in the study period; thus, regular fundus examination is recommended for the patients with Marfan syndrome.

The impact of physical activity changes on exercise capacity and health-related quality of life in young patients with CHD: a 3-year follow-up study.

OBJECTIVE: This study examined the relationship between changes in physical activity and their impact on exercise capacity and health-related quality of life over a 3-year span in patients with CHD. METHODS: We evaluated 99 young patients with CHD, aged 13-18 years at the outset. Physical activity, health-related quality of life, and exercise capacity were assessed via questionnaires and peak oxygen uptake measurements at baseline and after 3 years; changes in measures were estimated between the two time points and categorised into quartiles. Participants were stratified according to achieved (active) or not-achieved (inactive) recommended levels of physical activity (≥150 minutes/week) at both time points. RESULTS: Despite increases in physical activity, exercise capacity, and health-related quality of life over 3 years, the changes were not statistically significant (all p > 0.05). However, a positive association was found between physical activity changes and exercise capacity (ß = 0.250, p = 0.040) and health-related quality of life improvements (ß = 0.380, p < 0.001). Those with the most pronounced physical activity increase showed notable exercise capacity (p < 0.001) and health-related quality of life increases (p < 0.001) compared with patients with the largest decline in physical activity. The active-inactive category demonstrated a notable decline in exercise capacity compared to the active-active group, while the inactive-active group showed health-related quality of life improvements. CONCLUSIONS: Over 3 years, increased physical activity was consistently linked to increases in exercise capacity and health-related quality of life in patients with CHD, highlighting the potential of physical activity augmentation as an intervention strategy.

Molecular analysis of XPO1 inhibitor and gemcitabine-nab-paclitaxel combination in KPC pancreatic cancer mouse model.

BACKGROUND: The majority of pancreatic ductal adenocarcinoma (PDAC) patients experience disease progression while on treatment with gemcitabine and nanoparticle albumin-bound (nab)-paclitaxel (GemPac) necessitating the need for a more effective treatment strategy for this refractory disease. Previously, we have demonstrated that nuclear exporter protein exportin 1 (XPO1) is a valid therapeutic target in PDAC, and the selective inhibitor of nuclear export selinexor (Sel) synergistically enhances the efficacy of GemPac in pancreatic cancer cells, spheroids and patient-derived tumours, and had promising activity in a phase I study. METHODS: Here, we investigated the impact of selinexor-gemcitabine-nab-paclitaxel (Sel-GemPac) combination on LSL-KrasG12D/+ ; LSL-Trp53R172H/+ ; Pdx1-Cre (KPC) mouse model utilising digital spatial profiling (DSP) and single nuclear RNA sequencing (snRNAseq). RESULTS: Sel-GemPac synergistically inhibited the growth of the KPC tumour-derived cell line. The Sel-GemPac combination reduced the 2D colony formation and 3D spheroid formation. In the KPC mouse model, at a sub-maximum tolerated dose (sub-MTD) , Sel-GemPac enhanced the survival of treated mice compared to controls (p < .05). Immunohistochemical analysis of residual KPC tumours showed re-organisation of tumour stromal architecture, suppression of proliferation and nuclear retention of tumour suppressors, such as Forkhead Box O3a (FOXO3a). DSP revealed the downregulation of tumour promoting genes such as chitinase-like protein 3 (CHIL3/CHI3L3/YM1) and multiple pathways including phosphatidylinositol 3'-kinase-Akt (PI3K-AKT) signalling. The snRNAseq demonstrated a significant loss of cellular clusters in the Sel-GemPac-treated mice tumours including the CD44+ stem cell population. CONCLUSION: Taken together, these results demonstrate that the Sel-GemPac treatment caused broad perturbation of PDAC-supporting signalling networks in the KPC mouse model. HIGHLIGHTS: The majority of pancreatic ductal adenocarcinoma (PDAC) patients experience disease progression while on treatment with gemcitabine and nanoparticle albumin-bound (nab)-paclitaxel (GemPac). Exporter protein exportin 1 (XPO1) inhibitor selinexor (Sel) with GemPac synergistically inhibited the growth of LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1-Cre (KPC) mouse derived cell line and enhanced the survival of mice. Digital spatial profiling shows that Sel-GemPac causes broad perturbation of PDAC-supporting signalling in the KPC model.

Association between knee osteoarthritis and mortality: a serial propensity score-matched cohort study.

BACKGROUND/AIMS: The association between symptomatic knee osteoarthritis (OA) and higher cardiovascular disease (CVD) mortality is established; however, findings from studies that utilized regression analysis were limited, attributed to the strong association between OA and metabolic risk factors. This study aimed to evaluate the association between knee OA and mortality through propensity score matching. METHODS: This was a cohort study including Korean National Health and Nutrition Examination Survey (2010-2013) participants aged ≥ 50 years. By linking the survey data to cause of death data (through 2019) from Statistics Korea, mortality and cause-specific mortality data were obtained. Radiographic knee OA (ROA) was defined as bilateral Kellgren-Lawrence grade ≥ 2. Propensity score matching (1:1) was conducted between asymptomatic ROA, knee pain, and symptomatic ROA groups and normal groups, balancing the confounding factors. Time to death was analyzed using Cox proportional hazard modeling. RESULTS: A higher CVD mortality was observed in the symptomatic ROA group, but not in others; the risk estimates were asymptomatic ROA (hazard ratio [HR] 1.12; 95% confidence interval [CI] 0.77-1.65), knee pain (HR 0.61; 95% CI 0.27-1.38), and symptomatic ROA (HR 1.39; 95% CI 0.89-2.17). No association was found between the all-cause/cancer mortality and other groups. CONCLUSION: When propensity score matching controls metabolic risk factor imbalances, the association between symptomatic knee OA and higher CVD mortality was weaker compared to results of prior studies that used regression adjustment. The results may be more precise estimates of the total risk of knee OA for mortality in Koreans.

Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images.

In recent decades, medical imaging techniques have revolutionized the field of disease diagnosis, enabling healthcare professionals to noninvasively observe the internal structures of the human body. Among these techniques, optical coherence tomography (OCT) has emerged as a powerful and versatile tool that allows high-resolution, non-invasive, and real-time imaging of biological tissues. Deep learning algorithms have been successfully employed to detect and classify various retinal diseases in OCT images, enabling early diagnosis and treatment planning. However, existing deep learning algorithms are primarily designed for single-disease diagnosis, which limits their practical application in clinical settings where OCT images often contain symptoms of multiple diseases. In this paper, we propose an effective approach for multi-disease diagnosis in OCT images using a multi-scale learning (MSL) method and a sparse residual network (SRN). Specifically, the MSL method extracts and fuses useful features from images of different sizes to enhance the discriminative capability of a classifier and make the disease predictions interpretable. The SRN is a minimal residual network, where convolutional layers with large kernel sizes are replaced with multiple convolutional layers that have smaller kernel sizes, thereby reducing model complexity while achieving a performance similar to that of existing convolutional neural networks. The proposed multi-scale sparse residual network significantly outperforms existing methods, exhibiting 97.40% accuracy, 95.38% sensitivity, and 98.25% specificity. Experimental results show the potential of our method to improve explainable diagnosis systems for various eye diseases via visual discrimination.

The role of the immune response and inflammatory pathways in TNF-related apoptosis-inducing ligand (TRAIL) resistance in triple-negative breast cancer cells.

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer, and the majority of TNBC lacks targeted therapies. Previous studies have shown that TNBC cells are highly sensitive to TNF-related apoptosis-inducing ligand (TRAIL), making it a potentially viable treatment option for TNBC. However, the development of TRAIL resistance limits its potential for clinical use, and the underlying mechanisms are not fully understood. To better understand the mechanism of resistance to TRAIL, we performed RNA sequencing to identify the candidates that are responsible for resistance to TRAIL in two previously established TRAIL-resistant MDA231 and SUM159 cells. This approach led us to identify differentially expressed genes (DEGs) and pathways in TRAIL-resistant MDA231 and SUM159 cells compared to their TRAIL-sensitive counterparts. We showed that several DEGs and pathways were associated with inflammation in TRAIL-resistant cells, including IL-1α and IL6. By downregulating IL-1α and IL6 expression, we showed that TRAIL sensitivity can be significantly restored in TRAIL-resistant cells. Therefore, this study identifies a mechanism by which the inflammation pathway promotes TRAIL resistance, which could be targeted for enhancing TRAIL-based therapies in TNBC cells.

Adaptor proteins mediate CXCR4 and PI4KA crosstalk in prostate cancer cells and the significance of PI4KA in bone tumor growth.

The chemokine receptor, CXCR4 signaling regulates cell growth, invasion, and metastasis to the bone-marrow niche in prostate cancer (PCa). Previously, we established that CXCR4 interacts with phosphatidylinositol 4-kinase IIIα (PI4KIIIα encoded by PI4KA) through its adaptor proteins and PI4KA overexpressed in the PCa metastasis. To further characterize how the CXCR4-PI4KIIIα axis promotes PCa metastasis, here we identify CXCR4 binds to PI4KIIIα adaptor proteins TTC7 and this interaction induce plasma membrane PI4P production in prostate cancer cells. Inhibiting PI4KIIIα or TTC7 reduces plasma membrane PI4P production, cellular invasion, and bone tumor growth. Using metastatic biopsy sequencing, we found PI4KA expression in tumors correlated with overall survival and contributes to immunosuppressive bone tumor microenvironment through preferentially enriching non-activated and immunosuppressive macrophage populations. Altogether we have characterized the chemokine signaling axis through CXCR4-PI4KIIIα interaction contributing to the growth of prostate cancer bone metastasis.

Association Between Three Atopic Triad and Retinal Vein Occlusion Risk: A Nationwide Population-Based Study.

PURPOSE: To evaluate the association between three allergic diseases (allergic dermatitis, allergic rhinitis, and asthma) and the development of retinal vein occlusion (RVO), a major retinal disease that causes visual impairment. METHOD: This study used data obtained from the Korean National Health Insurance Claims database between 2009 and 2018. The association between the three atopic triads (allergic dermatitis, allergic rhinitis, and asthma) and the occurrence of sight-threatening RVO, as determined by diagnostic and treatment codes, were analyzed. Multivariate adjusted Cox regression analysis was used to determine the hazard ratios (HRs) and 95% confidence intervals for RVO development in the presence of allergic disease. RESULTS: In this population-based study, 2,160,195 (54.6%) individuals were male, 1,794,968 (45.4%) were female, and 620,938 (15.7%) were diagnosed with allergic diseases. Patients with either asthma or allergic rhinitis had a greater risk of RVO (adjusted hazard ratio (aHR) = 1.101, 95% confidence interval [CI] = 1.029-1.178 for asthma; aHR = 1.181, 95% CI = 1.147-1.215 for allergic rhinitis) compared to those without asthma or allergic rhinitis; however, patients with atopic dermatitis did not show a significant association with RVO (aHR = 1.071, 95% CI = 0.889-1.290), after adjusting for other risk factors. CONCLUSION: Our study revealed that allergic rhinitis, asthma, and coexisting multiple allergic conditions were associated with an increased risk of RVO. Thus, it may be advisable to suggest an ophthalmological examination for patients with allergies due to the increased possibility of the occurrence of retinal vascular disease.

Self-FI: Self-Supervised Learning for Disease Diagnosis in Fundus Images.

Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultra-wide-field fundus images (UFI). The proposed method utilizes contrastive learning to pre-train a deep learning model and then fine-tune it with a small set of labeled images. This approach reduces the reliance on labeled data, which is often limited and costly to obtain, and has the potential to improve disease detection in UFI. This method employs two contrastive learning techniques, namely bi-lateral contrastive learning and multi-modality pre-training, to form positive pairs using the data correlation. Bi-lateral learning fuses multiple views of the same patient's images, and multi-modality pre-training leverages the complementary information between UFI and conventional fundus images (CFI) to form positive pairs. The results show that the proposed contrastive learning method achieves state-of-the-art performance with an area under the receiver operating characteristic curve (AUC) score of 86.96, outperforming other approaches. The findings suggest that self-supervised learning is a promising direction for medical image analysis, with potential applications in various clinical settings.

Detection of thiocyanate in postmortem blood of a fatal potassium thiocyanate-poisoning patient using gas chromatography-mass spectrometry.

Thiocyanate is an inorganic compound used in industrial applications. Here, we report a case of suicidal death due to acute thiocyanate overdose. A 44-year-old man who consumed an unknown amount of thiocyanate solution was transferred to the emergency room and died 2 h after admission. An autopsy was performed 2 days after death. General toxicological analysis of blood using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry found no drug or alcohol. Quantification using GC-MS post-derivatization with pentafluorobenzyl bromide revealed 2,290 and 1,920 mg/L of thiocyanate in the heart and femoral blood samples, respectively. Thus, the cause of death was attributed to thiocyanate overdose. This study provides useful information for the interpretation of thiocyanate-related fatalities.

ShinyMetID: An R shiny package for metabolite identification by mass spectral matching.

We present metabolite identification software in the form of R Shiny. Metabolite identification by mass spectral matching in gas chromatography (GC-MS)-based untargeted metabolomics can be done by using the easy-to-use software. Various similarity measures are given and toy example using graphical user interface is presented.

Identifying Cancer Type-Specific Transcriptional Programs through Network Analysis.

Identifying cancer type-specific genes that define cell states is important to develop effective therapies for patients and methods for detection, early diagnosis, and prevention. While molecular mechanisms that drive malignancy have been identified for various cancers, the identification of cell-type defining transcription factors (TFs) that distinguish normal cells from cancer cells has not been fully elucidated. Here, we utilized a network biology framework, which assesses the fidelity of cell fate conversions, to identify cancer type-specific gene regulatory networks (GRN) for 17 types of cancer. Through an integrative analysis of a compendium of expression data, we elucidated core TFs and GRNs for multiple cancer types. Moreover, by comparing normal tissues and cells to cancer type-specific GRNs, we found that the expression of key network-influencing TFs can be utilized as a survival prognostic indicator for a diverse cohort of cancer patients. These findings offer a valuable resource for exploring cancer type-specific networks across a broad range of cancer types.

The Intersection of Metabolomics and Data Science.

Metabolomics generates a vast amount of data and heavily relies on data science for biological interpretation [...].