BACKGROUND: Prostate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear.
METHODS: Somatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored.
RESULTS: TEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients\' sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT\'s potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated.
CONCLUSIONS: TEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients.

Pancreatic ductal adenocarcinoma (PDAC) manifests diverse molecular subtypes, including the classical/progenitor and basal-like/squamous subtypes, with the latter known for its aggressiveness. We employed integrative transcriptome and metabolome analyses to identify potential genes contributing to the molecular subtype differentiation and its metabolic features. Our comprehensive analysis revealed that adrenoceptor alpha 2A (ADRA2A) was downregulated in the basal-like/squamous subtype, suggesting its potential role as a candidate suppressor of this subtype. Reduced ADRA2A expression was significantly associated with a high frequency of lymph node metastasis, higher pathological grade, advanced disease stage, and decreased survival among PDAC patients. In vitro experiments demonstrated that ADRA2A transgene expression and ADRA2A agonist inhibited PDAC cell invasion. Additionally, ADRA2A-high condition downregulated the basal-like/squamous gene expression signature, while upregulating the classical/progenitor gene expression signature in our PDAC patient cohort and PDAC cell lines. Metabolome analysis conducted on the PDAC cohort and cell lines revealed that elevated ADRA2A levels were associated with suppressed amino acid and carnitine/acylcarnitine metabolism, which are characteristic metabolic profiles of the classical/progenitor subtype. Collectively, our findings suggest that heightened ADRA2A expression induces transcriptome and metabolome characteristics indicative of classical/progenitor subtype with decreased disease aggressiveness in PDAC patients. These observations introduce ADRA2A as a candidate for diagnostic and therapeutic targeting in PDAC.

OBJECTIVE: This prospective study aims to evaluate the value of [18F]AlF-NOTA-fibroblast activation protein inhibitor (FAPI)-04 positron emission tomography-computed tomography (PET/CT) in predicting molecular subtypes of breast cancer.
METHODS: The study consecutively recruited patients suspected of having breast cancer from a single center who were prospectively enrolled from July 2023 to May 2024 and underwent [18F]AlF-NOTA-FAPI-04 PET/CT. This study compared the differences in tracer uptake among breast cancers with different adverse prognostic factors and molecular subtypes. The classification performance for each molecular subtype of breast cancer was assessed using a receiver operating characteristic (ROC) curve.
RESULTS: Fifty-three participants (mean age, 51 ± 11 years; 52 females) were evaluated. Breast cancer lesions with adverse prognostic factors showed higher tracer uptake. The five different molecular subtypes exhibited varying levels of uptake. The luminal A and luminal B (HER2-negative) subtypes had relatively low uptake, while the luminal B (HER2-positive), HER2-positive, and triple-negative subtypes had relatively high uptake. ROC analysis identified the max standardized uptake value (SUVmax) as a significant classifier (AUC = 0.912, P = 0.0005) for the luminal A subtype, with 100% sensitivity and 83% specificity. For predicting the luminal B (HER2-negative) subtype, SUVmax had an AUC of 0.770 (P = 0.0015). SUVmax, with an AUC of 0.781 (P = 0.003), was used to identify the triple-negative subtype tumors, resulting in a sensitivity of 100% and specificity of 51%. Lastly, the ROC curve showed the cut-off 15.40 (AUC = 0.921, P < 0.0001) could classify luminal A & luminal B (HER2-negative), and luminal B (HER2-positive) & HER2-positive & triple-negative, yielding a sensitivity of 94% and specificity of 79%.
CONCLUSIONS: The uptake of [18F]AlF-NOTA-FAPI-04 is significantly correlated with the molecular subtypes of breast cancer, and [18F]AlF-NOTA-FAPI-04 PET/CT is a potential tool for noninvasive identification of luminal A subtypes and guidance of FAP-targeted therapies.

Background: Epithelial-mesenchymal transition (EMT), deemed a pivotal hallmark of tumours, is intricately regulated by DNA methylation and encompasses multiple states along tumour progression. The potential mechanisms that drive the intrinsic heterogeneity of breast cancer (BC) via EMT transformation have not been identified, presenting a significant obstacle in clinical diagnosis and treatment. Methods: A total of 7,602 patients have been included in this study. We leveraged integrated multiomics data (epigenomic, genomic, and transcriptomic data) to delineate the comprehensive landscape of EMT in BC. Subsequently, a subtyping classifier was developed through a machine learning framework proposed by us. Results: We classified the BC samples into three methylation-driven EMT subtypes with distinct features, namely, C1 (the mammary duct development subtype with TP53 activation), C2 (the immune infiltration subtype with high TP53 mutation), and C3 (the ERBB2 amplification subtype with an unfavorable prognosis). Specifically, patients with the C1 subtype might respond to endocrine therapy or the p53-MDM2 antagonist nutlin-3. Patients with the C2 subtype might benefit from combined therapeutic regimens involving radiotherapy, PARP inhibitors, and immune checkpoint blockade therapy. Patients with the C3 subtype might benefit from anti-HER2 agents such as lapatinib. Notably, to increase the clinical applicability of the EMT subtypes, we devised a 96-gene panel-based classifier via a machine learning framework. Conclusions: Our study identified three methylation-driven EMT subtypes with distinct prognoses and biological traits to capture heterogeneity in BC and provided a rationale for the use of this classification as a powerful tool for developing new strategies for clinical trials.

UNASSIGNED: Mitochondrial dysfunction has been shown to play a critical role in cancer biology. However, its involvement in intrahepatic cholangiocarcinoma (iCCA) remains significantly understudied.
UNASSIGNED: RNA sequencing data of 30 pairs of iCCA and paracancerous tissues were collected from the First Affiliated Hospital of Wenzhou Medical University (WMU). The WMU cohort (n = 30) was integrated with public TCGA (n = 30) and GSE107943 (n = 30) datasets to establish a multi-center iCCA cohort. We merged the TCGA and GSE107943 cohorts into an exploration cohort to develop a mitochondria signature for prognosis assessment, and utilized the WMU cohort for external validation. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmarker analyses were used for functional interpretation of iCCA associated mitochondria-related genes (MRGs). In addition, unsupervised clustering was performed to identify mitochondria-based iCCA subtypes with the data of three institutions. Further investigations were conducted to examine the impact of mitochondrial dysfunction on drug responses, alteration of the tumor immune microenvironment, and immune responses.
UNASSIGNED: Two hundred and sixty-three iCCA-related MRGs were identified to be related to fatty acid metabolism, oxidative phosphorylation, and apoptosis. Through univariate and multivariate Cox, and LASSO analyses, a mitochondria signature with five optimal MRGs was established to evaluate the prognosis of iCCA patients with the AUC values ranged from 0.785 to 0.928 in the exploration cohort. The signature also exhibited satisfactory performance in the WMU cohort with AUC values of 0.817-0.871, and was identified as an independent risk predictor in both cohorts. Additionally, we found that patients with higher mitochondria score with poor prognosis presented lower infiltration levels of CD4+ T-cell, NK cells, and monocytes, and demonstrated higher sensitivity to targeted therapies, including sorafenib. Furthermore, two distant mitochondria-based subtypes were determined, and subtype 2 was associated with shorter survival time and immunosuppressive tumor microenvironment. Finally, the differential protein expression of five key MRGs was verified by Immunohistochemistry.
UNASSIGNED: We found mitochondrial dysfunction modulates aberrant metabolism, oxidative stress, immune responses, apoptosis, and drug sensitivity in iCCA. A mitochondria signature and two mitochondria-based iCCA subtypes were identified for clinical risk stratification and immunophenotyping.

BACKGROUND: As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive.
METHODS: We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified IGF2 as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay.
RESULTS: Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and IGF2 emerged as a key gene linked to prognosis. Experiments have proven that IGF2 can promote the glycolysis pathway and the malignant biological progression of OC cells.
CONCLUSIONS: We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified IGF2 as the marker gene. These insights provided a new avenue for exploring OC\'s molecular mechanisms and personalized treatment approaches.

Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.

BACKGROUND: Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker-drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter- or intra-tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians.
METHODS: In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations.
CONCLUSIONS: In recent years, exploration of subtypes through cell lines, animal models, patient-derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype-specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR-associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.

Bladder cancers are heterogeneous in nature, showing diverse molecular profiles and histopathological characteristics, which pose challenges for diagnosis and treatment. However, understanding the molecular basis of such heterogeneity has remained elusive. This study aimed to elucidate the molecular landscape of neuroendocrine-like bladder tumors, focusing on the involvement of β-catenin localization. Analyzing the transcriptome data and benefiting from the molecular classification tool, we undertook an in-depth analysis of muscle-invasive bladder cancers to uncover the molecular characteristics of the neuroendocrine-like differentiation. The study explored the contribution of transcription factors and chromatin remodeling complexes to neuroendocrine differentiation in bladder cancer. The study revealed a significant correlation between β-catenin localization and neuroendocrine differentiation in muscle-invasive bladder tumors, highlighting the molecular complexity of neuroendocrine-like tumors. Enrichment of YY1 transcription factor, E2F family members, and Polycomb repressive complex components in β-catenin-positive tumors suggest their potential contribution to neuroendocrine phenotypes. Our findings contribute valuable insights into the molecular complexity of neuroendocrine-like bladder tumors. By identifying potential therapeutic targets and refining diagnostic strategies, this study advances our understanding of endocrinology in the context of bladder cancer. Further investigations into the functional implications of these molecular relationships are warranted to enhance our knowledge and guide future therapeutic interventions.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a highly heterogenous neurodegenerative disorder that primarily affects upper and lower motor neurons, affecting additional cell types and brain regions. Underlying molecular mechanisms are still elusive, in part due to disease heterogeneity. Molecular disease subtyping through integrative analyses including RNA editing profiling is a novel approach for identification of molecular networks involved in pathogenesis.
METHODS: We aimed to highlight the role of RNA editing in ALS, focusing on the frontal cortex and the prevalent molecular disease subtype (ALS-Ox), previously determined by transcriptomic profile stratification. We established global RNA editing (editome) and gene expression (transcriptome) profiles in control and ALS-Ox cases, utilizing publicly available RNA-seq data (GSE153960) and an in-house analysis pipeline. Functional annotation and pathway analyses identified molecular processes affected by RNA editing alterations. Pearson correlation analyses assessed RNA editing effects on expression. Similar analyses on additional ALS-Ox and control samples (GSE124439) were performed for verification. Targeted re-sequencing and qRT-PCR analysis targeting CACNA1C, were performed using frontal cortex tissue from ALS and control samples (n = 3 samples/group).
RESULTS: We identified reduced global RNA editing in the frontal cortex of ALS-Ox cases. Differentially edited transcripts are enriched in synapses, particularly in the glutamatergic synapse pathway. Bioinformatic analyses on additional ALS-Ox and control RNA-seq data verified these findings. We identified increased recoding at the Q621R site in the GRIK2 transcript and determined positive correlations between RNA editing and gene expression alterations in ionotropic receptor subunits GRIA2, GRIA3 and the CACNA1C transcript, which encodes the pore forming subunit of a post-synaptic L-type calcium channel. Experimental data verified RNA editing alterations and editing-expression correlation in CACNA1C, highlighting CACNA1C as a target for further study.
CONCLUSIONS: We provide evidence on the involvement of RNA editing in the frontal cortex of an ALS molecular subtype, highlighting a modulatory role mediated though recoding and gene expression regulation on glutamatergic synapse related transcripts. We report RNA editing effects in disease-related transcripts and validated editing alterations in CACNA1C. Our study provides targets for further functional studies that could shed light in underlying disease mechanisms enabling novel therapeutic approaches.