Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.

Deubiquitylating enzymes (DUBs) maintain relative homeostasis of the cellular ubiquitome by removing the post‐translational modification ubiquitin moiety from substrates. Numerous DUBs have been demonstrated specificity for cleaving a certain type of ubiquitin linkage or positions within ubiquitin chains. Moreover, several DUBs perform functions through specific protein–protein interactions in a catalytically independent manner, which further expands the versatility and complexity of DUBs’ functions. Dysregulation of DUBs disrupts the dynamic equilibrium of ubiquitome and causes various diseases, especially cancer and immune disorders. This review summarizes the Janus‐faced roles of DUBs in cancer including proteasomal degradation, DNA repair, apoptosis, and tumor metastasis, as well as in immunity involving innate immune receptor signaling and inflammatory and autoimmune disorders. The prospects and challenges for the clinical development of DUB inhibitors are further discussed. The review provides a comprehensive understanding of the multi‐faced roles of DUBs in cancer and immunity.

Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.

To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Background: AlkB homolog 5 (ALKBH5) is a N⁶-methyladenosine (m⁶A) demethylase associated with the development, growth, and progression of multiple cancer types. However, the biological role of ALKBH5 has not been investigated in pan-cancer datasets. Therefore, in this study, comprehensive bioinformatics analysis of pan-cancer datasets was performed to determine the mechanisms through which ALKBH5 regulates tumorigenesis.Methods: Online websites and databases such as NCBI, UCSC, CCLE, HPA, TIMER2, GEPIA2, cBioPortal, UALCAN, STRING, SangerBox, ImmuCellAl, xCell, and GenePattern were used to extract data of ALKBH5 in multiple cancers. The pan-cancer patient datasets were analyzed to determine the relationship between ALKBH5 expression, genetic alterations, methylation status, and tumor immunity. Targetscan, miRWalk, miRDB, miRabel, LncBase databases and Cytoscape tool were used to identify microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that regulate expression of ALKBH5 and construct the lncRNA-miRNA-ALKBH5 network. In vitro CCK-8, wound healing, Transwell and M2 macrophage infiltration assays as well as in vivo xenograft animal experiments were performed to determine the biological functions of ALKBH5 in glioma cells.Results: The pan-cancer analysis showed that ALKBH5 was upregulated in several solid tumors. ALKBH5 expression significantly correlated with the prognosis of cancer patients. Genetic alterations including duplications and deep mutations of the ALKBH5 gene were identified in several cancer types. Alterations in the ALKBH5 gene correlated with tumor prognosis. GO and KEGG enrichment analyses showed that ALKBH5-related genes were enriched in the inflammatory, metabolic, and immune signaling pathways in glioma. ALKBH5 expression correlated with the expression of immune checkpoint (ICP) genes, and influenced sensitivity to immunotherapy. We constructed a lncRNA-miRNA network that regulates ALKBH5 expression in tumor development and progression. In vitro and in vivo experiments showed that ALKBH5 promoted proliferation, migration, and invasion of glioma cells and recruited the M2 macrophage to glioma cells.Conclusions: ALKBH5 was overexpressed in multiple cancer types and promoted the development and progression of cancers through several mechanisms including regulation of the tumor-infiltration of immune cells. Our study shows that ALKBH5 is a promising prognostic and immunotherapeutic biomarker in some malignant tumors.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.