An investigation of the molecular characterization of the tripartite motif (TRIM) family and primary validation of TRIM31 in gastric cancer.

Most TRIM family members characterized by the E3-ubiquitin ligases, participate in ubiquitination and tumorigenesis. While there is a dearth of a comprehensive investigation for the entire family in gastric cancer (GC). By combining the TCGA and GEO databases, common TRIM family members (TRIMs) were obtained to investigate gene expression, gene mutations, and clinical prognosis. On the basis of TRIMs, a consensus clustering analysis was conducted, and a risk assessment system and prognostic model were developed. Particularly, TRIM31 with clinical prognostic and diagnostic value was chosen for single-gene bioinformatics analysis, in vitro experimental validation, and immunohistochemical analysis of clinical tissue microarrays. The combined dataset consisted of 66 TRIMs, of which 52 were differentially expressed and 43 were differentially prognostic. Significant survival differences existed between the gene clusters obtained by consensus clustering analysis. Using 4 differentially expressed genes identified by multivariate Cox regression and LASSO regression, a risk scoring system was developed. Higher risk scores were associated with a poorer prognosis, suppressive immune cell infiltration, and drug resistance. Transcriptomic data and clinical sample tissue microarrays confirmed that TRIM31 was highly expressed in GC and associated with a poor prognosis. Pathway enrichment analysis, cell migration and colony formation assay, EdU assay, reactive oxygen species (ROS) assay, and mitochondrial membrane potential assay revealed that TRIM31 may be implicated in cell cycle regulation and oxidative stress-related pathways, contribute to gastric carcinogenesis. This study investigated the whole functional and expression profile and a risk score system based on the TRIM family in GC. Further investigation centered around TRIM31 offers insight into the underlying mechanisms of action exhibited by other members of its family in the context of GC.

Inflammasome-related gene signatures as prognostic biomarkers in osteosarcoma.

Osteosarcoma, the primary bone cancer in adolescents and young adults, is notorious for its aggressive growth and metastatic potential. Our study delved into the prognostic impact of inflammasome-related gene signatures in osteosarcoma patients, employing comprehensive genetic profiling to uncover signatures linked with patient outcomes. We identified three patient subgroups through consensus clustering, with one showing worse survival rates correlated with high FGFR3 and RARB expressions. Immune profiling revealed significant immune cell infiltration differences among these subgroups, affecting survival. Utilising advanced machine learning, including StepCox and gradient boosting machine algorithms, we developed a prognostic model with a notable c-index of 0.706, highlighting CD36 and MYD88 as key genes. Higher inflammasome risk scores from our model were associated with poorer survival, corroborated across datasets. In vitro experiments validated CD36 and MYD88's roles in promoting osteosarcoma cell proliferation, invasion and migration, emphasising their therapeutic potential. This research offers new insights into inflammasomes' role in osteosarcoma, introducing novel biomarkers for risk assessment and potential therapeutic targets. Our findings suggest a pathway towards personalised treatment strategies, potentially improving patient outcomes in osteosarcoma.

c-MET-positive circulating tumor cells and cell-free DNA as independent prognostic factors in hormone receptor-positive/HER2-negative metastatic breast cancer.

BACKGROUND: Endocrine therapy resistance in hormone receptor-positive/HER2-negative (HR+/HER2-) breast cancer (BC) is a significant clinical challenge that poses several unmet needs in the management of the disease. This study aimed to investigate the prognostic value of c-MET-positive circulating tumor cells (cMET+ CTCs), ESR1/PIK3CA mutations, and cell-free DNA (cfDNA) concentrations in patients with hormone receptor-positive (HR+) metastatic breast cancer (mBC). METHODS: Ninety-seven patients with HR+ mBC were prospectively enrolled during standard treatment at Samsung Medical Center. CTCs were isolated from blood using GenoCTC® and EpCAM or c-MET CTC isolation kits. PIK3CA and ESR1 hotspot mutations were analyzed using droplet digital PCR. CfDNA concentrations were calculated using internal control copies from the ESR1 mutation test. Immunocytochemistry was performed to compare c-MET overexpression between primary and metastatic sites. RESULTS: The proportion of c-MET overexpression was significantly higher in metastatic sites than in primary sites (p = 0.00002). Survival analysis showed that c-MET+ CTC, cfDNA concentration, and ESR1 mutations were significantly associated with poor prognosis (p = 0.0026, 0.0021, and 0.0064, respectively) in HR+/HER2- mBC. By contrast, EpCAM-positive CTC (EpCAM+ CTC) and PIK3CA mutations were not associated with progression-free survival (PFS) in HR+/HER2- mBC. Multivariate analyses revealed that c-MET+ CTCs and cfDNA concentration were independent predictors of PFS in HR+/HER2- mBC. CONCLUSIONS: Monitoring c-MET+ CTC, rather than assessing c-MET expression in the primary BC site, could provide valuable information for predicting disease progression, as c-MET expression can change during treatment. The c-MET+ CTC count and cfDNA concentration could provide complementary information on disease progression in HR+ /HER2- mBC, highlighting the importance of integrated liquid biopsy.

Prognostic Value of Histone Acetyl Transferase 1 (HAT-1) and Inflammatory Signatures in Pancreatic Cancer.

Pancreatic cancer is a type of gastrointestinal tumor with a growing incidence and mortality worldwide. Pancreatic ductal adenocarcinoma (PDAC) constitutes 90% of cases, and late-stage diagnosis is common, leading to a 5-year survival rate of less than 10% in high-income countries. The use of biomarkers has different proven translational applications, facilitating early diagnosis, accurate prognosis and identification of potential therapeutic targets. Several studies have shown a correlation between the tissue expression levels of various molecules, measured through immunohistochemistry (IHC), and survival rates in PDAC. Following the hallmarks of cancer, epigenetic and metabolic reprogramming, together with immune evasion and tumor-promoted inflammation, plays a critical role in cancer initiation and development. In this study, we aim to explore via IHC and Kaplan-Meier analyses the prognostic value of various epigenetic-related markers (histones 3 and 4 (H3/H4), histone acetyl transferase 1 (HAT-1), Anti-Silencing Function 1 protein (ASF1), Nuclear Autoantigenic Sperm Protein (NASP), Retinol Binding Protein 7 (RBBP7), importin 4 (IPO4) and IPO5), metabolic regulators (Phosphoglycerate mutase (PGAM)) and inflammatory mediators (allograft inflammatory factor 1 (AIF-1), interleukin 10 (IL-10), IL-12A and IL-18) in patients with PDAC. Also, through a correlation analysis, we have explored the possible interconnections in the expression levels of these molecules. Our results show that higher expression levels of these molecules are directly associated with poorer survival rates in PDAC patients, except in the case of IL-10, which shows an inverse association with mortality. HAT1 was the molecule more clearly associated with mortality, with a hazard risk of 21.74. The correlogram demonstrates an important correlation between almost all molecules studied (except in the case of IL-18), highlighting potential interactions between these molecules. Overall, our study demonstrates the relevance of including different markers from IHC techniques in order to identify unexplored molecules to develop more accurate prognosis methods and possible targeted therapies. Additionally, our correlation analysis reveals potential interactions among these markers, offering insights into PDAC's pathogenesis and paving the way for targeted therapies tailored to individual patient profiles. Future studies should be conducted to confirm the prognostic value of these components in PDAC in a broader sample size, as well as to evaluate the possible biological networks connecting them.

Identification of novel prognostic autoantibodies in diffuse large B-cell lymphoma treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone via a high-throughput antigen microarray.

BACKGROUND: R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) is a standard first-line treatment for diffuse large B-cell lymphoma (DLBCL). However, 20%-40% of patients survive less than 5 years. Novel prognostic biomarkers remain in demand. METHODS: Baseline plasma autoantibodies (AAbs) were assessed in 336 DLBCLs. In the discovery phase (n = 20), a high-density antigen microarray (∼21,000 proteins) was used to expound AAb profiles. In the verification phase (n = 181), with a DLBCL-focused microarray, comparative results based on event-free survival at 24 months (EFS24) and lasso Cox regression models of progression-free survival (PFS) and overall survival (OS) were integrated to identify potential biomarkers. They were further validated by enzyme-linked immunosorbent assay in validation phase 1 (n = 135) and a dynamic cohort (n = 12). In validation phase 2, a two-AAb-based risk score was established. They were further validated in an immunohistochemistry cohort (n = 55) and four independent Gene Expression Omnibus datasets (n = 1598). RESULTS: Four AAbs (CREB1, N4BP1, UBAP2, and DEAF1) were identified that showed associations with EFS24 status (p < .05) and superior PFS and OS (p < .05). A novel risk score model based on CREB1 and N4BP1 AAbs was developed to predict PFS with areas under the curve of 0.72, 0.71, 0.76, and 0.82 at 1, 3, 5, and 7 years, respectively, in DLBCL treated with R-CHOP independent of the International Prognostic Index (IPI) and provided significant additional recurrence risk discrimination (p < .05) for the IPI. CREB1 and N4BP1 proteins and messenger RNAs were also associated with better PFS and OS (p < .05). CONCLUSIONS: This study identified a novel prognostic panel of CREB1, N4BP1, DEAF1, and UBAP2 AAbs that is independent of the IPI in DLBCL.

Genomic instability-associated two-miRNA signature as a novel prognostic biomarker in breast cancer.

BACKGROUND: Breast cancer (BC) is the most common cancer among women worldwide and a leading cause of cancer-associated deaths among women. However, there is a lack of accurate prognostic biomarkers for BC. In the present study, we aimed to identify a genomic instability (GI)-associated microRNA signature as a novel potential prognostic biomarker in BC. METHODS: We performed an integrative analysis to investigate the relationship between GI and BC and identify GI-associated microRNAs (miRNAs). Subsequently, we conducted a discovery and validation study using multicenter cohorts. The GI-associated miRNA signature was developed in the discovery cohort and independently validated in internal and external cohorts. RESULTS: GI-associated miRNAs expression in BC showed heterogeneity and was significantly correlated with BC prognosis. We identified a GI-associated two-miRNA signature (miR-105-5p and miR-767-5p), termed GI2miR, that stratified BC patients into high-risk and low-risk groups with significantly different clinical outcomes (log-rank p = 0.027) in The Cancer Genome Atlas (TCGA) discovery cohort (n = 763). The prognostic value of GI2miR was further validated in internal TCGA validation cohort (n = 253) (log-rank p = 0.035) and independent GSE22216 cohort (n = 210) (log-rank p = 0.036). The GI2miR demonstrated independent prognostic value in multivariate Cox proportional hazard regression analyses and stratification analysis. CONCLUSIONS: We have developed a novel prognostic signature based on GI-associated two miRNAs for BC, which may lay the foundation for BC to improve prognosis prediction.

The Ki67 dilemma: investigating prognostic cut-offs and reproducibility for automated Ki67 scoring in breast cancer.

PURPOSE: Quantification of Ki67 in breast cancer is a well-established prognostic and predictive marker, but inter-laboratory variability has hampered its clinical usefulness. This study compares the prognostic value and reproducibility of Ki67 scoring using four automated, digital image analysis (DIA) methods and two manual methods. METHODS: The study cohort consisted of 367 patients diagnosed between 1990 and 2004, with hormone receptor positive, HER2 negative, lymph node negative breast cancer. Manual scoring of Ki67 was performed using predefined criteria. DIA Ki67 scoring was performed using QuPath and Visiopharm® platforms. Reproducibility was assessed by the intraclass correlation coefficient (ICC). ROC curve survival analysis identified optimal cutoff values in addition to recommendations by the International Ki67 Working Group and Norwegian Guidelines. Kaplan-Meier curves, log-rank test and Cox regression analysis assessed the association between Ki67 scoring and distant metastasis (DM) free survival. RESULTS: The manual hotspot and global scoring methods showed good agreement when compared to their counterpart DIA methods (ICC > 0.780), and good to excellent agreement between different DIA hotspot scoring platforms (ICC 0.781-0.906). Different Ki67 cutoffs demonstrate significant DM-free survival (p < 0.05). DIA scoring had greater prognostic value for DM-free survival using a 14% cutoff (HR 3.054-4.077) than manual scoring (HR 2.012-2.056). The use of a single cutoff for all scoring methods affected the distribution of prediction outcomes (e.g. false positives and negatives). CONCLUSION: This study demonstrates that DIA scoring of Ki67 is superior to manual methods, but further study is required to standardize automated, DIA scoring and definition of a clinical cut-off.

Application of individualized differential expression analysis in human cancer proteome.

Liquid chromatography-mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.

Systematic analysis of the role of LDHs subtype in pan-cancer demonstrates the importance of LDHD in the prognosis of hepatocellular carcinoma patients.

BACKGROUND: Lactate dehydrogenase (LDHs) is an enzyme involved in anaerobic glycolysis, including LDHA, LDHB, LDHC and LDHD. Given the regulatory role in the biological progression of certain tumors, we analyzed the role of LDHs in pan-cancers. METHODS: Cox regression, Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves, and correlation of clinical indicators in tumor patients were used to assess the prognostic significance of LDHs in pan-cancer. The TCGA, HPA, TIMER, UALCAN, TISIDB, and Cellminer databases were used to investigate the correlation between the expression of LDHs and immune subtypes, immune checkpoint genes, methylation levels, tumor mutational load, microsatellite instability, tumor-infiltrating immune cells and drug sensitivity. The cBioPortal database was also used to identify genomic abnormalities of LDHs in pan-cancer. A comprehensive assessment of the biological functions of LDHs was performed using GSEA. In vitro, HepG2 and Huh7 cells were transfected with LDHD siRNA and GFP-LDHD, the proliferation capacity of cells was examined using CCK-8, EdU, and colony formation assays; the migration and invasion of cells was detected by wound healing and transwell assays; western blotting was used to detect the levels of MMP-2, MMP-9, E-cadherin, N-cadherin and Akt phosphorylation. RESULTS: LDHs were differentially expressed in a variety of human tumor tissues. LDHs subtypes can act as pro-oncogenes or anti-oncogenes in different types of cancer and have an impact on the prognosis of patients with tumors by influencing their clinicopathological characteristics. LDHs were differentially expressed in tumor immune subtypes and molecular subtypes. In addition, LDHs expression correlated with immune checkpoint genes, tumor mutational load, and microsatellite instability. LDHD was identified to play an important role in the prognosis of HCC patients, according to a comprehensive analysis of LDHs in pan-cancer. In HepG2 and Huh7 cells, knockdown of LDHD promoted cell proliferation, migration, and invasion, promoted the protein expression levels of MMP-2, MMP-9, N-cadherin, and Akt phosphorylation, but inhibited the protein expression level of E-cadherin. In addition, LDHD overexpression showed the opposite changes. CONCLUSION: LDHs subtypes can be used as potential prognostic markers for certain cancers. Prognostic and immunotherapeutic analysis indicated that LDHD plays an important role in the prognosis of HCC patients. In vitro experiments revealed that LDHD can affect HCC proliferation, migration, and invasion by regulating MMPs expression and EMT via Akt signaling pathway, which provides a new perspective on the anti-cancer molecular mechanism of LDHD in HCC.

Roadmap to DILI research in Europe. A proposal from COST action ProEuroDILINet.

In the current article the aims for a constructive way forward in Drug-Induced Liver Injury (DILI) are to highlight the most important priorities in research and clinical science, therefore supporting a more informed, focused, and better funded future for European DILI research. This Roadmap aims to identify key challenges, define a shared vision across all stakeholders for the opportunities to overcome these challenges and propose a high-quality research program to achieve progress on the prediction, prevention, diagnosis and management of this condition and impact on healthcare practice in the field of DILI. This will involve 1. Creation of a database encompassing optimised case report form for prospectively identified DILI cases with well-characterised controls with competing diagnoses, biological samples, and imaging data; 2. Establishing of preclinical models to improve the assessment and prediction of hepatotoxicity in humans to guide future drug safety testing; 3. Emphasis on implementation science and 4. Enhanced collaboration between drug-developers, clinicians and regulatory scientists. This proposed operational framework will advance DILI research and may bring together basic, applied, translational and clinical research in DILI.

Inflammation-related molecular signatures involved in the anticancer activities of brigatinib as well as the prognosis of EML4-ALK lung adenocarcinoma patient.

Although ALK tyrosine kinase inhibitors (ALK-TKIs) have shown remarkable benefits in EML4-ALK positive NSCLC patients compared to conventional chemotherapy, the optimal sequence of ALK-TKIs treatment remains unclear due to the emergence of primary and acquired resistance and the lack of potential prognostic biomarkers. In this study, we systematically explored the validity of sequential ALK inhibitors (alectinib, lorlatinib, crizotinib, ceritinib and brigatinib) for a heavy-treated patient with EML4-ALK fusion via developing an in vitro and in vivo drug testing system based on patient-derived models. Based on the patient-derived models and clinical responses of the patient, we found that crizotinib might inhibit proliferation of EML4-ALK positive tumors resistant to alectinib and lorlatinib. In addition, NSCLC patients harboring the G1269A mutation, which was identified in alectinib, lorlatinib and crizotinib-resistant NSCLC, showed responsiveness to brigatinib and ceritinib. Transcriptomic analysis revealed that brigatinib suppressed the activation of multiple inflammatory signaling pathways, potentially contributing to its anti-tumor activity. Moreover, we constructed a prognostic model based on the expression of IL6, CXCL1, and CXCL5, providing novel perspectives for predicting prognosis in EML4-ALK positive NSCLC patients. In summary, our results delineate clinical responses of sequential ALK-TKIs treatments and provide insights into the mechanisms underlying the superior effects of brigatinib in patients harboring ALKG1269A mutation and resistant towards alectinib, lorlatinib and crizotinib. The molecular signatures model based on the combination of IL6, CXCL1 and CXCL5 has the potential to predict prognosis of EML4-ALK positive NSCLC patients.

Serum cytokines and creatinine/cystatin C ratio as prognostic biomarkers in advanced cancer patients treated with anti-PD-1/PD-L1 therapy.

OBJECTIVE: Immune checkpoint inhibitors (ICIs), specifically targeting the programmed cell death protein-1 or its ligand (PD-1/PD-L1), have been extensively used in the treatment of a spectrum of malignancies, although the predictive biomarkers remain to be elucidated. This study aims to investigate the association between baseline circulating levels of cytokines and the creatinine/cystatin C ratio (CCR) with the treatment outcomes of ICIs in patients with advanced cancer. METHODS: The pre-treatment circulating levels of 10 cytokines (PD-L1, CTLA4, CXCL10, LAG3, HGF, CCL2, MIG, GRANB, IL-18, and IL-6) were measured via automated capillary-based immunoassay platform in the serum of 65 advanced cancer patients treated with anti-PD-1/PD-L1-based systemic therapy and 10 healthy volunteers. The levels of cytokines and CCR were quantified and categorized into high and low groups based on the median value. The associations of serum cytokines and CCR with response to treatment, survival, and immune-related adverse events were assessed. RESULTS: Elevated circulating levels of 6 cytokines (PD-L1, CXCL10, HGF, CCL2, MIG, and IL-6) were observed in cancer patients compared with that in healthy volunteers. The correlation coefficients between cytokines, CCR and nutritional risk index were also calculated. In the cancer cohort (N = 65), low circulating HGF (P = 0.023, P = 0.029), low IL-6 (P = 0.002, P < 0.001), and high CCR (P = 0.031, P = 0.008) were associated with significantly improved progression-free survival (PFS) and overall survival (OS). Multi-variable COX analyses adjusted for clinicopathological factors revealed that low HGF, low IL-6, and high CCR were independent favorable prognostic factors for PFS (P = 0.028, P = 0.010, and P = 0.015, respectively) and OS (P = 0.043, P = 0.003, and P = 0.026, respectively). Grade 2 irAEs occurred more frequently in patients with low levels of circulating CCL2 and LAG3. CONCLUSIONS: Pre-treatment circulating levels of serum IL-6, HGF, and CCR may serve as independent predictive and prognostic biomarkers in advanced cancer patients treated with ICIs-based systemic therapy. These findings might help to identify potential patients who would benefit from these therapies.

Prognostic and predictive biomarkers with therapeutic targets in nonsmall-cell lung cancer: A 2023 update on current development, evidence, and recommendation.

BACKGROUND: Since the publication of the original work in 2014, significant progress has been made in the characterization of genomic alterations that drive oncogenic addiction of nonsmall cell lung cancer (NSCLC) and how the immune system can leverage non-oncogenic pathways to modulate therapeutic outcomes. This update evaluates and validates the recent and emerging data for prognostic and predictive biomarkers with therapeutic targets in NSCLC. DATA SOURCES: We performed a literature search from January 2015 to October 2023 using the keywords non-small cell lung cancer, clinical practice guidelines, gene mutations, genomic assay, immune cancer therapy, circulating tumor DNA, predictive and prognostic biomarkers, and targeted therapies. STUDY SELECTION AND DATA EXTRACTION: We identified, reviewed, and evaluated relevant clinical trials, meta-analyses, seminal articles, and published clinical practice guidelines in the English language. DATA SYNTHESIS: Regulatory-approved targeted therapies include those somatic gene alterations of EGFR ("classic" mutations, exon 20 insertion, and rare EGFR mutations), ALK, ROS1, BRAF V600, RET, MET, NTRK, HER2, and KRAS G12C. Data for immunotherapy and circulating tumor DNA in next-generation sequencing are considered emerging, whereas the predictive role for PIK3CA gene mutation is insufficient. CONCLUSIONS: Advances in sequencing and other genomic technologies have led to identifying novel oncogenic drivers, novel resistance mechanisms, and co-occurring mutations that characterize NSCLC, creating further therapeutic opportunities. The benefits associated with immunotherapy in the perioperative setting hold initial promise, with their long-term results awaiting.

Cuproptosis-Related Genes MTF1 and LIPT1 as Novel Prognostic Biomarker in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a life-threatening hematologic malignant disease with high morbidity and mortality in both adults and children. Cuproptosis, a novel mode of cell death, plays an important role in tumor development, but the functional mechanisms of cuproptosis-related genes (CRGs) in AML are unclear. The differential expression of CRGs between tumors such as AML and normal tissues in UCSC XENA, TCGA and GTEx was verified using R (version: 3.6.3). Lasso regression, Cox regression and Nomogram were used to screen for prognostic biomarkers of AML and to construct corresponding prognostic models. Kaplan-Meier analysis, ROC analysis, clinical correlation analysis, immune infiltration analysis and enrichment analysis were used to further investigate the correlation and functional mechanisms of CRGs with AML. The ceRNA regulatory network was used to identify the mRNA-miRNA-lncRNA regulatory axis. Cuproptosis-related genes LIPT1, MTF1, GLS and CDKN2A were highly expressed in AML, while FDX1, LIAS, DLD, DLAT, PDHA1, SLC31A1 and ATP7B were lowly expressed in AML. Lasso regression, Cox regression, Nomogram and calibration curve finally identified MTF1 and LIPT1 as two novel prognostic biomarkers of AML and constructed the corresponding prognostic models. In addition, all 12 CRGs had predictive power for AML, with MTF1, LIAS, SLC31A1 and CDKN2A showing more reliable results. Further analysis showed that ATP7B was closely associated with mutation types such as FLT3, NPM1, RAS and IDH1 R140 in AML, while the expression of MTF1, LIAS and ATP7B in AML was closely associated with immune infiltration. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) revealed that biological functions such as metal ion transmembrane transporter activity, haptoglobin binding and oxygen carrier activity, pathways such as interferon alpha response, coagulation, UV response DN, apoptosis, hypoxia and heme metabolism all play a role in the development of AML. The ceRNA regulatory network revealed that 6 lncRNAs such as MALAT1, interfere with MTF1 expression through 6 miRNAs such as hsa-miR-32-5p, which in turn affect the development and progression of AML. In addition, APTO-253 has the potential to become an AML-targeted drug. The cuproptosis-related genes MTF1 and LIPT1 can be used as prognostic biomarkers in AML. A total of six lncRNAs, including MALAT1, are involved in the expression and regulation of MTF1 in AML through six miRNAs such as hsa-miR-32-5p.

Deciphering the prognostic landscape of osteosarcoma: Integrating the roles of hippo pathway genes, programmed cell death, and the tumor immune microenvironment.

Osteosarcoma is a highly aggressive cancer prevalent among adolescents and young adults, notorious for its tendency to metastasize to the lungs. This research delves into the molecular foundations of osteosarcoma by examining the role of the Hippo signaling pathway and its interaction with the tumor immune microenvironment (TME). Through analysis of transcriptomic data from the TARGET-OS dataset and control samples from GTEx, we identified a set of 131 genes that link high expression profiles in osteosarcoma with the Hippo pathway. A focused examination through univariate Cox regression analysis revealed eight key genes (DLG5, WNT11, TGFB2, DLG4, WNT16, ID2, WNT10B, and WNT10A) with a significant correlation to patient outcomes. Hierarchical clustering of these genes delineated two distinct patient groups with significantly different survival rates, a finding supported by Kaplan-Meier survival analysis. Further investigation into immune cell infiltration and expression profiles of immunoregulatory factors uncovered a notable pattern of immune evasion in the group with poorer prognosis, marked by reduced effector immune cell activity and lower levels of immunostimulatory factors. Single-cell sequencing highlighted the cellular diversity within osteosarcoma samples and identified markers differentiating malignant from nonmalignant cells, correlating these markers with prognostic risk scores. Our results emphasize the critical prognostic value of Hippo pathway genes and the TME in osteosarcoma, shedding light on new avenues for therapeutic intervention and patient-specific treatment strategies.

Comprehensive analysis of lung adenocarcinoma: Unveiling differential gene expression, survival-linked genes, subtype stratification, and immune landscape implications.

This study offers a detailed exploration of lung adenocarcinoma (LUAD), addressing its heterogeneity and treatment challenges through a multi-faceted analysis that includes gene expression, genetic subtyping, pathway analysis, immune assessment, and drug sensitivity. It identifies 165 genes with significant expression differences and 46 genes associated with survival, revealing insights into oxidative stress and autophagy. LUAD samples were divided into three subtypes using consensus clustering on these 46 genes, with distinct survival outcomes. Gene Set Enrichment Analysis (GSEA) on HALLMARK gene sets indicated pathway variations with survival implications. The immune landscape, analyzed using the CIBERSORT algorithm, showed different immune cell distributions across subtypes, with the first subtype exhibiting a better immune environment and survival prospects. Advanced machine learning techniques developed a risk model from a set of four genes, effectively categorizing patients into high and low-risk groups, validated through external datasets and analyses. This model linked lower risk scores to better clinical stages, with a higher mutation rate and potential immunotherapy benefits observed in the high-risk group. Drug sensitivity assessments highlighted varied treatment responses between risk groups, suggesting avenues for personalized therapy. This comprehensive analysis enhances the understanding of LUAD's molecular and clinical nuances, offering valuable insights for tailored treatment approaches.

Unveiling NRlncRNAs as prognostic biomarkers and therapeutic targets for head-and-neck squamous cell carcinoma through machine learning.

Head-and-neck squamous cell carcinoma (HNSCC) patients often exhibit insensitivity to immunotherapy, leading to treatment failure. Identifying potential biomarkers that can predict prognosis and improve the efficacy of treatment is crucial. In this study, we aimed to identify necroptosis-related long noncoding RNAs (NRlncRNAs) as potential therapeutic targets to improve the prognosis of HNSCC patients. By exploring the Genotype-Tissue Expression Project (GTEx) and the Cancer Genome Atlas (TCGA) databases, we identified NRlncRNAs and developed a risk model comprising 17 NRlncRNAs to predict the prognosis of HNSCC patients and to classify patients into two clusters based on their expression levels. We conducted various analyses, such as the Kaplan-Meier analysis, GSEA and IC50 prediction, to evaluate the differences in sensitivity to immunotherapy between the two clusters. Our findings suggest that NRlncRNAs have potential as therapeutic targets for improving the prognosis of HNSCC patients, and that individualized treatment approaches based on NRlncRNA expression levels can improve the sensitivity of immunotherapy and overall treatment outcomes. This study highlights new perspectives within clinical cancer informatics and provides insight into potential therapeutic strategies for HNSCC patients.

Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer.

Because cancer is a leading cause of death and is thought to be caused by genetic errors or genomic instability in many circumstances, there have been studies exploring cancer's genetic basis using microarray and RNA-seq methods, linking gene expression data to patient survival. This research introduces a methodological framework, combining heterogeneous gene expression data, random forest selection, and pathway analysis, alongside clinical information and Cox regression analysis, to discover prognostic biomarkers. Heterogeneous gene expression data for colorectal cancer were collected from TCGA-COAD (RNA-seq), and GSE17536 and GSE39582 (microarray), and were integrated with Entrez Gene IDs. Using Cox regression analysis and random forest, genes with consistent hazard ratios and significantly affecting patient survivability were chosen. Predictive accuracy was evaluated using ROC curves. Pathway analysis identified potential RNA biomarkers. The authors identified 28 RNA biomarkers. Pathway analysis revealed enrichment in cancer-related pathways, notably EGFR downstream signaling and IGF1R signaling. Three RNA biomarkers (ZEB1-AS1, PI4K2A, and ITGB8-AS1) and two clinical biomarkers (stage and age) were chosen for a prognostic model, improving predictive performance compared to using clinical biomarkers alone. Despite biomarker identification challenges, this study underscores integration of heterogenous gene expression data for discovery.

The Clinicopathological Significance of the Cyclin D1/E1-Cyclin-Dependent Kinase (CDK2/4/6)-Retinoblastoma (RB1/pRB1) Pathway in Epithelial Ovarian Cancers.

Cyclin-dependent kinases (CDK2, CDK4, CDK6), cyclin D1, cyclin E1 and phosphorylated retinoblastoma (pRB1) are key regulators of the G1/S cell cycle checkpoint and may influence platinum response in ovarian cancers. CDK2/4/6 inhibitors are emerging targets in ovarian cancer therapeutics. In the current study, we evaluated the prognostic and predictive significance of the CDK2/4/6-cyclin D1/E1-pRB1 axis in clinical ovarian cancers (OC). The CDK2/4/6, cyclin D1/E1 and RB1/pRB1 protein expression were investigated in 300 ovarian cancers and correlated with clinicopathological parameters and patient outcomes. CDK2/4/6, cyclin D1/E1 and RB1 mRNA expression were evaluated in the publicly available ovarian TCGA dataset. We observed nuclear and cytoplasmic staining for CDK2/4/6, cyclins D1/E1 and RB1/pRB1 in OCs with varying percentages. Increased nuclear CDK2 and nuclear cyclin E1 expression was linked with poor progression-free survival (PFS) and a shorter overall survival (OS). Nuclear CDK6 was associated with poor OS. The cytoplasmic expression of CDK4, cyclin D1 and cyclin E1 also has predictive and/or prognostic significance in OCs. In the multivariate analysis, nuclear cyclin E1 was an independent predictor of poor PFS. Tumours with high nuclear cyclin E1/high nuclear CDK2 have a worse PFS and OS. Detailed bioinformatics in the TCGA cohort showed a positive correlation between cyclin E1 and CDK2. We also showed that cyclin-E1-overexpressing tumours are enriched for genes involved in insulin signalling and release. Our data not only identified the prognostic/predictive significance of these key cell cycle regulators but also demonstrate the importance of sub-cellular localisation. CDK2 targeting in cyclin-E1-amplified OCs could be a rational approach.

Unveiling Bladder Cancer Prognostic Insights by Integrating Patient-Matched Sample and CpG Methylation Analysis.

Bladder cancer prognosis remains a pressing clinical challenge, necessitating the identification of novel biomarkers for precise survival prediction and improved quality of life outcomes. This study proposes a comprehensive strategy to uncover key prognostic biomarkers in bladder cancer using DNA methylation analysis and extreme survival pattern observations in matched pairs of cancer and adjacent normal cells. Unlike traditional approaches that overlook cancer heterogeneity by analyzing entire samples, our methodology leverages patient-matched samples to account for this variability. Specifically, DNA methylation profiles from adjacent normal bladder tissue and bladder cancer tissue collected from the same individuals were analyzed to pinpoint critical methylation changes specific to cancer cells while mitigating confounding effects from individual genetic differences. Utilizing differential threshold settings for methylation levels within cancer-associated pathways enabled the identification of biomarkers that significantly impact patient survival. Our analysis identified distinct survival patterns associated with specific CpG sites, underscoring these sites' pivotal roles in bladder cancer outcomes. By hypothesizing and testing the influence of methylation levels on survival, we pinpointed CpG biomarkers that profoundly affect the prognosis. Notably, CpG markers, such as cg16269144 (PRKCZ), cg16624272 (PTK2), cg11304234, and cg26534425 (IL18), exhibited critical methylation thresholds that correlate with patient mortality. This study emphasizes the importance of tailored approaches to enhancing prognostic accuracy and refining therapeutic strategies for bladder cancer patients. The identified biomarkers pave the way for personalized prognostication and targeted interventions, promising advancements in bladder cancer management and patient care.