Identification Of Biomarkers Research Articles

Graphene in Medicine: Revolutionizing Healthcare through Advanced Nanomaterials

Nanomaterials, particularly carbon-based structures like graphene nanostructures, have revolutionized science and technology, offering groundbreaking advancements in bioengineering and medicine. These developments have enhanced our ability to detect, diagnose, and treat diseases, leading to significant innovations in therapy methods. The evolving field of graphene synthesis, with its less complex and more effective strategies, has captured the scientific community's interest. This review seeks to delve into the latest progress in the creation and application of graphene-based nanomaterials, especially their role in biomedical applications, with a primary focus on drug delivery systems. Graphene has emerged as a versatile material in the medical field. Its application as a sensor in monitoring vital parameters such as blood pressure and blood sugar levels, as well as nitric oxide levels in oxygen, has been noteworthy. In dentistry, graphene, combined with specific peptides, can identify individual bacterial strains, aiding in accurate disease diagnosis. Its role as an immunosensor is also significant, enabling the detection of trace amounts of substances like growth hormones. In the battle against cancer, graphene has shown promising results. It is actively being researched for broader medical applications including cancer therapy, diagnostic tools for diseases, tissue engineering, implants, DNA sequencing, biomarker identification, genetic material transfer, and in the combined fields of biomedical imaging and neuroscience. This review underscores the potential of graphene in medical science. As research continues, it is anticipated that graphene-based technologies will significantly contribute to personalized therapies, enhancing healthcare quality. The conclusion of this review emphasizes the need for ongoing research to explore and refine graphene-derived smart therapeutics. Such advancements will not only improve patient care but also drive us towards the zenith of human welfare, showcasing the transformative impact of nanotechnology in healthcare.Keywords: Graphene, Biomedical Applications, Drug Delivery Systems, Diagnostic Innovations and Personalized Therapeutics

Development of an Electrochemical Biosensor for GFAP with Carbon-Based Substrate for Traumatic Brain Injury

This study concentrates on creating an electrochemical biosensor designed for the real-time identification and monitoring of biomarkers, specifically Glial Fibrillary Acidis Protein (GFAP). The intended applications involve the diagnosis and prognosis of Traumatic Brain Injury (TBI). TBIs pose a significant global health challenge, impacting millions each year, and existing diagnostic methods, such as neuroimaging, face constraints related to cost, radiation exposure and a greater delay in providing a diagnosis. The suggested biosensor exploits electrochemical techniques, offering benefits such as non-invasiveness and swift results. The primary objective is to advance wearable microelectrodes for TBI detection, with an emphasis on utilizing bodily fluids like sweat as practical samples. Moreover, the biosensor's versatility makes it well-suited for various environments, including extreme conditions.The objective of this research is to enhance the precision of TBI diagnosis and prognosis, thereby ultimately improving the efficacy of point-of-care treatment. Our methodology involves the utilization of the Capture Systematic Evolution of Ligands by Exponential Enrichment (Capture-SELEX) technique to create a highly specific aptamer designed for the detection of Glial Fibrillary Acidic Protein. The biosensor we are developing will feature electrodes modified with aptamers and a carbon-based substrate, enabling continuous real-time monitoring of the target substance. The primary objective of our biosensor is to identify GFAP, thereby reducing risks associated with exposure to radiation, and healthcare costs. To ensure the long-term stability of our biosensor, our design centers around a carbon-based substrate. Carbon-based nanomaterials have garnered significant attention in biosensor applications due to their diverse range of properties, encompassing chemical stability, biocompatibility, functionalization, and biodegradability.

T2-FLAIR mismatch sign, an imaging biomarker for CDKN2A-intact in non-enhancing astrocytoma, IDH-mutant

IntroductionThe WHO classification of central nervous system tumors (5th edition) classified astrocytoma, IDH-mutant accompanied with CDKN2A/B homozygous deletion as WHO grade 4. Loss of immunohistochemical (IHC) staining for methylthioadenosine phosphorylase (MTAP) was developed as a surrogate marker for CDKN2A-HD. Identification of imaging biomarkers for CDKN2A status is of immense clinical relevance. In this study, we explored the association between radiological characteristics of non-enhancing astrocytoma, IDH-mutant to the CDKN2A/B status.MethodsThirty-one cases of astrocytoma, IDH-mutant with MTAP results by IHC were included in this study. The status of CDKN2A was diagnosed by IHC staining for MTAP in all cases, which was further confirmed by comprehensive genomic analysis in 12 cases. The T2-FLAIR mismatch sign, cystic component, calcification, and intratumoral microbleeding were evaluated. The relationship between the radiological features and molecular pathological diagnosis was analyzed.ResultsTwenty-six cases were identified as CDKN2A-intact while 5 cases were CDKN2A-HD. The presence of > 33% and > 50% T2-FLAIR mismatch was observed in 23 cases (74.2%) and 14 cases (45.2%), respectively, and was associated with CDKN2A-intact astrocytoma (p = 0.0001, 0.0482). None of the astrocytoma, IDH-mutant with CDKN2A-HD showed T2-FLAIR mismatch sign. Cystic component, calcification, and intratumoral microbleeding were not associated with CDKN2A status.ConclusionIn patients with non-enhancing astrocytoma, IDH-mutant, the T2-FLAIR mismatch sign is a potential imaging biomarker for the CDKN2A-intact subtype. This imaging biomarker may enable preoperative prediction of CDKN2A status among astrocytoma, IDH-mutant.

A Fluorescent Lateral Flow Immunoassay for the Detection of Skeletal Muscle Troponin I in Serum for Muscle Injury Monitoring at the Point of Care.

Exercise-induced muscle injury is one of the most common types of sports injuries. Skeletal muscle troponin I (skTnI) serves as an ideal biomarker in assessing such injuries, facilitating timely detection and evaluation. In this study, we develop a fluorescent sandwich lateral flow immunoassay (LFIA) combined with a desktop analyzer for rapid detection of skTnI. Through optimizing the reaction system, the assay achieves a satisfying detection performance, reaching a limit of detection (LOD) of 0.5 ng/mL with a turnaround time of 15 min. The proposed detection platform offers portability, ease of use, and high sensitivity, which facilitates the monitoring of exercise-induced muscle injuries at the point of care. This feature is particularly advantageous for end users, enabling timely detection of sports-related injuries and ultimately enhancing prognosis and sports life.

Predicting Response to Medical Treatment in Acromegaly via Granulation Pattern, Expression of Somatostatin Receptors Type 2 and 5 and E-Cadherin.

Resistance to first-generation somatostatin receptor ligand (fgSRL) treatment in acromegaly is common, making the identification of biomarkers that predict fgSRL response a desired goal. We conducted a retrospective analysis on 21 patients with acromegaly who underwent surgery and subsequent pharmacological treatment. Through immunohistochemistry (IHC), we assessed the expression of the somatostatin receptor subtypes SSTR2 and SSTR5, E-Cadherin, and cytokeratin granulation pattern (sparsely or densely). Patients were divided into responders and non-responders based on their biochemical response to fgSRL and/or the newer agent, Pasireotide, or the GH-blocker, Pegvisomant. Patients resistant to fgSRL (n = 12) exhibited lower SSTR2 and E-Cadherin expressions. Sparsely granulated tumors were more frequent in the non-responder group. SSTR2 (p = 0.024, r = 0.49) and E-Cadherin (p = 0.009, r = 0.64) positively correlated with the Insulin-like Growth Factor 1 (IGF-1) decrease after fgSRL, while SSTR5 (p = 0.107, r = -0.37) showed a trend towards negative correlation. SSTR5 positivity seemed to be associated with Pasireotide response, albeit the number of treated patients was too low (n = 4). No IHC markers correlated with Pegvisomant response. Our findings suggest that densely granulated tumors, with positive SSTR2 and E-Cadherin seem to be associated with favorable fgSRL responses. The strongest predictive value of the studied markers was found for E-Cadherin, which seems to surpass even SSTR2.

Elevated Cardiovascular Biomarkers Following Hypertensive Disorder of Pregnancy.

Hypertensive disorder of pregnancy (HDP) is associated with an increased risk for later-life cardiovascular disease (CVD). Whether the HDP pregnancy itself confers risk towards CVD later in life is suggested in several epidemiologic studies. Given this connection and that the HDP exposure itself may play a role, understanding whether markers associated with cardiovascular risk vary based on HDP history in the years following pregnancy may assist with risk stratification and development of targeted interventions. We measured 77 proteins (CVD-associated and inflammatory markers) in n=22 individuals with a history of HDP and n=43 matched controls with no HDP history at a median of 4 years after pregnancy. Several CVD-associated proteins (fibrinogen, fetuin-A, L-selectin, and alpha-1-acid glycoprotein) were significantly elevated, by orders of magnitude, in individuals with a history of HDP compared to normotensive pregnancies (all p<0.0001). In multivariable linear regression models controlling for age, body mass index, chronic hypertension, and diabetes, a history of HDP remained associated with higher levels of CVD-associated proteins (all p<0.0001). We clustered samples based on global patterns of CVD protein expression and found a significant difference in CVD protein expression patterns between post-Normal and post-HDP samples. Conversely, differences in circulating inflammatory markers were largely insignificant or more subtle than that observed with the CVD-associated proteins. Identification of biomarkers associated with CVD in the intervening years after HDP but before evident CVD is critical to understanding post-HDP cardiovascular risk to provide insight for the development of therapeutic interventions that mitigate CVD event risk in this high-risk population.

Biomarkers in Heart Failure with Preserved Ejection Fraction: A Perpetually Evolving Frontier.

Heart failure with preserved ejection fraction (HFpEF) represents a complex clinical syndrome, often very difficult to diagnose using the available tools. As the global burden of this disease is constantly growing, surpassing the prevalence of heart failure with reduced ejection fraction, during the last few years, efforts have focused on optimizing the diagnostic and prognostic pathways using an immense panel of circulating biomarkers. After the paradigm of HFpEF development emerged more than 10 years ago, suggesting the impact of multiple comorbidities on myocardial structure and function, several phenotypes of HFpEF have been characterized, with an attempt to find an ideal biomarker for each distinct pathophysiological pathway. Acknowledging the limitations of natriuretic peptides, hundreds of potential biomarkers have been evaluated, some of them demonstrating encouraging results. Among these, soluble suppression of tumorigenesis-2 reflecting myocardial remodeling, growth differentiation factor 15 as a marker of inflammation and albuminuria as a result of kidney dysfunction or, more recently, several circulating microRNAs have proved their incremental value. As the number of emerging biomarkers in HFpEF is rapidly expanding, in this review, we aim to explore the most promising available biomarkers linked to key pathophysiological mechanisms in HFpEF, outlining their utility for diagnosis, risk stratification and population screening, as well as their limitations.

A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology.

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer's Disease.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. Its accurate pathogenic mechanisms are incompletely clarified, and effective therapeutic treatments are still inadequate. Autophagy is closely associated with AD and plays multiple roles in eliminating harmful aggregated proteins and maintaining cell homeostasis. This study identified 1191 differentially expressed genes (DEGs) based on the GSE5281 dataset from the GEO database, intersected them with 325 autophagy-related genes from GeneCards, and screened 26 differentially expressed autophagy-related genes (DEAGs). Subsequently, GO and KEGG enrichment analysis was performed and indicated that these DEAGs were primarily involved in autophagy-lysosomal biological process. Further, eight hub genes were determined by PPI construction, and experimental validation was performed by qRT-PCR on a SH-SY5Y cell model. Finally, three hub genes (TFEB, TOMM20, GABARAPL1) were confirmed to have potential application for biomarkers. A multigenic prediction model with good predictability (AUC = 0.871) was constructed in GSE5281 and validated in the GSE132903 dataset. Hub gene-targeted miRNAs closely associated with AD were also retrieved through the miRDB and HDMM database, predicting potential therapeutic agents for AD. This study provides new insights into autophagy-related genes in brain tissues of AD patients and offers more candidate biomarkers for AD mechanistic research as well as clinical diagnosis.

Effects of pesticide exposure on the expression of selected genes in normal and cancer samples: Identification of predictive biomarkers for risk assessment

Pesticides pivotal in controlling pests, can represent a threat for human health. Regulatory agencies constantly monitor their harmful effects, regulating their use. Several studies support a positive association between long-term exposure to pesticides and chronic pathologies, such as cancer. Geno-toxicological biomonitoring has proven to be valuable to assess genetic risks associated with exposure to pesticides, representing a promising tool to improve preventive measures and identify workers at higher risk. In this study, a differential gene expression analysis of 70 candidate genes deregulated upon pesticide exposure, was performed in 10 GEO human gene expression DataSets. It was found that six genes (PMAIP1, GCLM, CD36, SQSTM1, ABCC3, NR4A2) had significant AUC predictive values. Also, CD36 was upregulated in non-transformed cell samples and healthy workers, but downregulated in cancer cells. Further validation in larger groups of workers will corroborate the importance of the identified candidates as biomarkers of exposure/effect.

Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging—Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

Neuroplasticity in Parkinson's disease.

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder, affecting millions of people and rapidly increasing over the last decades. Even though there is no intervention yet to stop the neurodegenerative pathology, many efficient treatment methods are available, including for patients with advanced PD. Neuroplasticity is a fundamental property of the human brain to adapt both to external changes and internal insults and pathological processes. In this paper we examine the current knowledge and concepts concerning changes at network level, cellular level and molecular level as parts of the neuroplastic response to protein aggregation pathology, synapse loss and neuronal loss in PD. We analyse the beneficial, compensatory effects, such as augmentation of nigral neurons efficacy, as well as negative, maladaptive effects, such as levodopa-induced dyskinesia. Effects of physical activity and different treatments on neuroplasticity are considered and the opportunity of biomarkers identification and use is discussed.

Prognostic Utility of dNLR, ALRI, APRI, and SII in COVID-19 Patients with Diabetes: A Cross-Sectional Study.

The coronavirus disease 2019 (COVID-19) pandemic has necessitated the identification of biomarkers that can predict disease severity, particularly in vulnerable populations such as individuals with diabetes. This study aims to evaluate the predictive value of inflammatory and liver function markers, specifically derived Neutrophil to Lymphocyte Ratio (dNLR), aspartate aminotransferase (AST)-to-lymphocyte ratio (ALRI), AST to Platelet Ratio Index (APRI), and Systemic Inflammation Index (SII), in COVID-19 patients with and without diabetes. This cross-sectional study included 336 participants, comprising 168 patients with diabetes matched with 168 without, based on gender, body mass index (BMI), and COVID-19 severity at hospitalization. The study was conducted at Victor Babes Hospital for Infectious Diseases and Pulmonology from January 2021 to December 2023. All participants had a confirmed SARS-CoV-2 infection and met the inclusion criteria of being 18 years or older with type 1 or type 2 diabetes as per American Diabetes Association guidelines. At 3 days post symptom onset, significant differences in inflammatory and liver function markers were observed between the two groups. The dNLR, ALRI, APRI, and SII were notably higher in diabetic patients. At a dNLR cutoff of 2.685, the sensitivity and specificity were 70.312% and 65.978%, respectively, with an AUC of 0.624 (p < 0.001). The ALRI showed a cutoff of 0.812, with a sensitivity of 76.429% and specificity of 69.541% (AUC 0.752, p < 0.001). These markers demonstrated statistically significant hazard ratios at both 3 and 7 days, indicating their predictive relevance for severe COVID-19 outcomes. For instance, at 7 days, SII demonstrated a hazard ratio of 2.62 (CI: 1.29-5.04, p < 0.001), highlighting its strong prognostic capability. The study successfully identified significant differences in inflammatory and liver function markers between COVID-19 patients with and without diabetes, with these markers showing good predictive value for disease severity. The results underscore the potential of these biomarkers, particularly ALRI and SII, as valuable tools in managing COVID-19, aiding in the timely identification of patients at increased risk of severe outcomes.

GLI1 confers resistance to PARP inhibitors by activating the DNA damage repair pathway.

Identifying the mechanisms of action of anticancer drugs is an important step in the development of new drugs. In this study, we established a comprehensive screening platform consisting of 68 oncogenes (MANO panel), encompassing 243 genetic variants, to identify predictive markers for drug efficacy. Validation was performed using drugs that targeted EGFR, BRAF, and MAP2K1, which confirmed the utility of this functional screening panel. Screening of a BRCA2-knockout DLD1 cell line (DLD1-KO) revealed that cells expressing SMO and GLI1 were resistant to olaparib. Gene set enrichment analysis identified genes associated with DNA damage repair that were enriched in cells overexpressing SMO and GLI1. The expression of genes associated with homologous recombination repair (HR), such as the FANC family and BRCA1/2, was significantly upregulated by GLI1 expression, which is indicative of PARP inhibitor resistance. Although not all representative genes of the nucleotide excision repair (NER) pathway were upregulated, NER activity was enhanced by GLI1. The GLI1 inhibitor was effective against DLD1-KO cells overexpressing GLI1 both in vitro and in vivo. Furthermore, the combination therapy of olaparib and GLI1 inhibitor exhibited a synergistic effect on DLD1-KO, suggesting the possible clinical application of GLI1 inhibitor targeting cancer with defective DNA damage repair. This platform enables the identification of biomarkers associated with drug sensitivity, and is a useful tool for drug development.

Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms

BackgroundPatients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers.MethodsGene expression profiles for AF and stable CAD were downloaded from the Gene Expression Omnibus database. Overlapping genes related to both diseases were identified using weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Hub genes were then identified using the machine learning algorithm. Immune cell infiltration and correlations with hub genes were explored, followed by drug predictions. Hub gene expression in AF and CAD patients was validated by real-time qPCR.ResultsWe obtained 28 common overlapping genes in AF and stable CAD, mainly enriched in the PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathway. Two hub genes, COL6A3 and FKBP10, were positively correlated with the abundance of MDSC, plasmacytoid dendritic cells, and regulatory T cells in AF and negatively correlated with the abundance of CD56dim natural killer cells in CAD. The AUCs of COL6A3 and FKBP10 were all above or close to 0.7. Drug prediction suggested that collagenase clostridium histolyticum and ocriplasmin, which target COL6A3, may be potential drugs for AF and stable CAD. Additionally, COL6A3 and FKBP10 were upregulated in patients with AF and CAD.ConclusionCOL6A3 and FKBP10 may be key biomarkers for AF and CAD, providing new insights into the diagnosis and treatment of this disease.

Abstract Mo008: Sub-strain Dependent Vulnerability to Psychosocial Stress Exacerbates Doxorubicin-induced Cardiotoxicity in Adult Male Mice

Introduction: Psychosocial stress is a significant cardiovascular risk factor and an enormous burden on cancer patients. However, limited knowledge is available on the underlying mechanisms at least in part because of lack of translationally relevant animal models. Previous work from our lab established that adult exposure to social stress can exacerbate the cardiovascular effect of juvenile doxorubicin (DOX) exposure. However, the effect of concomitant DOX and stress exposure in adult mice is unknown. Methods: In this study, a ‘two-hit’ mouse model was established, in which chronic subordination stress (CSS) and DOX treatment were given simultaneously. Twelve-week-old male C57BL/6N (6N) and C57BL/6J (6J) mice were subjected to daily episodes of social defeat by dominant CD1 mice with whom they remained in continuous sensory contact with for 28 days, thus establishing social subordination. One week after the start of CSS, mice were administered DOX (8 mg/kg/week) for 3 weeks. Thereafter, cardiac function was assessed by echocardiography and gene expression of inflammatory and pro-fibrotic markers were determined by real-time PCR. Cardiac fibrosis and inflammation were assessed by histopathologic analysis. Results: Cardiac dysfunction and significant cardiac fibrosis were observed in 6N but not in 6J mice that were exposed to both DOX and CSS, as evidenced by reduced ejection fraction, cardiac output, and increased trichrome staining, respectively. Additionally, gene expression analysis demonstrated significant upregulation of atrial natriuretic peptide and interleukin-6 in 6N mice exposed to both DOX and CSS compared to 6J mice. Finally, DOX caused cardiac atrophy and abrogated CSS-induced cardiac hypertrophy in 6N but not in 6J mice, as evidenced by measuring left ventricular mass and heart weight/tibia length. Conclusion: Exposure to CSS exacerbated the cardiotoxic effects of DOX in a sub-strain dependent manner. These findings highlight psychosocial stress as a risk factor for worse cardiovascular outcome in cancer patients receiving cardiotoxic regimens. The study underscores sub-strain differences in the cardiac responses of 6N and 6J mouse to CSS alone and CSS/DOX treatment that may serve as a discovery platform for the identification of genetic biomarkers of resilience.

Therapy-Induced Senescence: Novel Approaches for Markers Identification.

Therapy-induced senescence (TIS) represents a major cellular response to anticancer treatments. Both malignant and non-malignant cells in the tumor microenvironment undergo TIS and may be harmful for cancer patients since TIS cells develop a senescence-associated secretory phenotype (SASP) that can sustain tumor growth. The SASP also modulates anti-tumor immunity, although the immune populations involved and the final results appear to be context-dependent. In addition, senescent cancer cells are able to evade senescence growth arrest and to resume proliferation, likely contributing to relapse. So, research data suggest that TIS induction negatively affects therapy outcomes in cancer patients. In line with this, new interventions aimed at the removal of senescent cells or the reprogramming of their SASP, called senotherapy, have become attractive therapeutic options. To date, the lack of reliable, cost-effective, and easy-to-use TIS biomarkers hinders the application of recent anti-senescence therapeutic approaches in the clinic. Hence, the identification of biomarkers for the detection of TIS tumor cells and TIS non-neoplastic cells is a high priority in cancer research. In this review article, we describe the current knowledge about TIS, outline critical gaps in our knowledge, and address recent advances and novel approaches for the discovery of TIS biomarkers.

Modernizing the NCI60 Cell Line Screen for Phenotypic Drug Discovery in the 21st Century.

Over the past three decades, high-throughput phenotypic cancer cell line screens have revealed unanticipated small-molecule activities and illuminated connections between tumor genotypes and anticancer efficacy. Founded in 1984, the National Cancer Institute's "NCI60" screen laid the conceptual groundwork for the contemporary landscape of phenotypic drug discovery. NCI60 first operated as a primary bioactivity screen, but molecular characterization of the NCI60 cell line panel and development of a small-molecule sensitivity pattern recognition algorithm (called "COMPARE") have enabled subsequent studies into drug mechanisms of action and biomarker identification. In this issue of Cancer Research, Kunkel and colleagues report an updated version of the NCI60 screen, dubbed "HTS384" NCI60, that better aligns with current cell proliferation assay standards and has higher throughput. Changes include the use of a 384-well plate format, automated laboratory equipment, 3 days of compound exposure, and a CellTiter-Glo luminescent endpoint. To confirm that data from the HTS384 and classic NCI60 screen are comparable, the authors tested a library of 1,003 anticancer agents using both protocols and applied COMPARE to analyze patterns of cell line sensitivities. More than three dozen groups of targeted therapies showed high comparability between screens. Modernization of NCI60, and closer integration with other large-scale pharmacogenomic screens and molecular feature sets, will help this public screening service remain pertinent for cancer drug discovery efforts for years to come. See related article by Kunkel et al., p. 2403.

Pembrolizumab in combination with trastuzumab for treatment of HER2-positive advanced gastric or gastro-esophageal junction cancer.

Gastric cancer remains a challenging malignancy with a high global mortality rate. Recent advances in targeted therapy and immunotherapy have shown promise in improving patient outcomes. This paper reviews the impact of incorporating targeted agents such as trastuzumab and immunotherapeutic agents like pembrolizumab into standard chemotherapy regimens for gastric cancer treatment. A comprehensive analysis was conducted on pivotal clinical trials, including KEYNOTE-590, KEYNOTE-811, and ToGA, focusing on their methodologies, patient populations, treatment regimens, and outcome measures. The review also explored emerging research avenues in precision medicine, particularly genomic sequencing and biomarker identification. To assess the efficacy and survival benefits of adding trastuzumab and pembrolizumab to standard chemotherapy in the treatment of gastric cancer and to outline future directions in gastric cancer research. Including trastuzumab and pembrolizumab in treatment regimens for human epidermal growth factor receptor 2 (HER2)-positive and PD-L1-expressing gastric cancers significantly improved progression-free and overall survival rates compared to chemotherapy alone. These findings highlight the potential of personalized therapy in enhancing treatment outcomes. Furthermore, ongoing research into the gastric cancer microenvironment and the role of the microbiome suggests novel targets for future therapeutic interventions. The integration of targeted and immunotherapeutic agents with traditional chemotherapy represents a pivotal shift in gastric cancer treatment, moving towards more personalized and effective regimens.

Construction of lncRNA prognostic model related to disulfidptosis in lung adenocarcinoma

BackgroundLung cancer is one of the malignant tumors with the highest rates of morbidity and mortality worldwide. One of the most common histological types of lung cancer is lung adenocarcinoma (LUAD). Despite the fact that development in medicine has significantly improved some patients’ prognoses, the overall survival (OS) rate is still very low. In glucose-deficient SLC7A11-overexpressed cancer cells, the accumulation of disulfide molecules leads to abnormal disulfide bonding between actin cytoskeletal proteins, interferes with their tissues, and eventually leads to actin network collapse and cell death. This mode of cell death is called disulfidptosis. Studies have shown that disulfidptosis may be a new target for cancer treatment. However, the role of disulfidptosis in LUAD is still unknown. MethodsLUAD transcriptome and clinical information from The Cancer Genome Atlas (TCGA) was downloaded. The co-expression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Cox regression analysis was performed to screen the disulfidptosis-related lncRNAs (DRLs) and build the prognostic model. Kaplan-Meier curve, Cox regression analysis, and receiver operating characteristic (ROC) curve was used to validate the model. Then a nomogram is made to predict the prognosis of LUAD patients. Finally, fresh-collected clinical samples were used to verify the expression of DRLs in LUAD. ResultsThe prognostic model with six DRLs was developed to predict the prognosis of LUAD, with superior prognosis value compared to other clinical variables. The Cox regression analysis revealed that T stage, N stage and the risk score were identified as independent variables that affected LUAD prognosis. ROC curve revealed that the model has a moderate diagnostic value, with an AUC of 1-year 0.684, 3-year 0.664, and 5-year 0.588. Moreover, nine medications connected to LUAD treatment were acquired through drug sensitivity analysis. LUAD tissue validation showed that AC012073.1, AC012615.1, EMSLR, and SNHG12 were highly expressed, while AL606834.1 and AL365181.2 with low expression. ConclusionSix DRLs were screened and verified to construct the prognostic model, which can accurately predict the LUAD prognosis. It establishes a basis for further exploration into the molecular mechanisms underlying LUAD and identification of potential biomarkers for diagnosis, prognosis, and therapeutic targets.