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Akt is a serine-threonine kinase that has an important role in transducing survival signals. Akt also regulates a number of proteins involved in the apoptotic process. To find new Akt interactors, we performed a two-hybrid screening in yeast using full-length Akt cDNA as bait and a human cDNA heart library as prey. Among 200 clones obtained, two of them were identified as coding for the c-FLIPL protein. c-FLIPL is an endogenous inhibitor of death receptor-induced apoptosis through the caspase-8 pathway. Using co-immunoprecipitation experiments of either transfected or endogenous proteins, we confirmed the interaction between Akt and c-FLIPL. Furthermore, we observed that c-FLIPL overexpression interferes with Gsk3-β phosphorylation levels. Moreover, through its effects on Gsk3β, c-FLIPL overexpression in cancer cells induced resistance to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). This effect was mediated by the regulation of p27Kip1 and caspase-3 expression. These results indicate the existence of a new mechanism of resistance to TRAIL in cancer cells, and unexpected functions of c-FLIPL. [ABSTRACT FROM AUTHOR]

To date, only 13 studies have described patients with large overlapping deletions of 10p11.2-p12. These individuals shared a common phenotype characterized by intellectual disability, developmental delay, distinct facial dysmorphic features, abnormal behaviour, visual impairment, cardiac malformation, and cryptorchidism in males. Molecular cytogenetic analysis revealed that the deletion in this chromosomal region shares a common smallest region of overlap (SRO) of 80 kb, which contains only the WAC gene (WW-domain-containing adaptor with coiled coil). In this clinical case report, we report a 5-year-old girl, born from non-consanguineous parents, with a 10p11.22p11.21 microdeletion. She presents clinical features that overlap with other patients described in the literature, such as dysmorphic traits, speech delay, and behavioural abnormalities (hyperactivity), even though the WAC gene is not involved in the microdeletion. Our results are the first to highlight that the deletion described here represents a contiguous gene syndrome that is enough to explain the distinct phenotype but partially overlaps with the previous cases reported in the literature, even though the same genes are not involved. In particular, in this study, we speculate about the role of the WAC gene that seems to be associated with normal motor development. In fact, we found that our patient is the only one described in the literature with a large deletion in the 10p11.22p11.21 region without the involvement of the WAC gene deletion, and, interestingly, the patient did not have motor delay.

Bladder cancer is the 10th most common cancer type worldwide. Cystoscopy represents the gold standard for bladder cancer diagnosis, but this procedure is invasive and painful, hence the need to identify new biomarkers through noninvasive procedures. microRNAs (miRNAs) are considered to be promising diagnostic molecules, because they are very stable in biological fluids (including urine) and easily detectable. This systematic review analyses the power of urine miRNAs as bladder cancer diagnostic markers. We conducted this systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 293 records related to miRNAs and their diagnostic significance in BC were retrieved from the PubMed and Embase databases. A systematic search of the literature was performed, and a total of 25 articles (N = 4054 participants) were identified and reviewed. Although many of the selected studies were of high scientific quality, the results proved to be quite heterogeneous, because we did not identify a univocal consensus for a specific miRNA signature but only isolated the signatures. We did not identify a univocal consensus for a specific diagnostic miRNA signature but only isolated the signatures, some of them with better diagnostic power compared to the others.

This study monitored the anti-spike-receptor-binding domain (RBD) and neutralizing antibodies induced by the Pfizer/BioNTech mRNA BNT162b2 vaccine in a cohort of 163 healthcare workers aged ≤60 years. We have taken advantage of two study groups, both of whom received the first two doses in the same time window, but Group 1 (54 HCWs) received the third dose 2 months before Group 2 (68 HCWs) did. The cohorts were monitored from the 12th day after the first vaccine dose up to 1 month after the third vaccine dose for a total of eight time points and about 1 year of surveillance (T1 = 12 days after the first dose; T2 = 10 days after the second dose; T3 = 1 month after the second dose; T4 = 3 months after the second dose; T5 = 4 months after the second dose; T6 = 5 months after the second dose; T7 = 7 months after the second dose; T8 = 1 month after the third dose for Group 1; T8* = 9 months after the second dose for Group 2; T9 = 1 month after the third dose for Group 2). The mean value of anti-spike antibodies decreased faster over time, but at T7, its decline was significantly slowed (T7 vs. T8*). After the third dose, the anti-spike titer rose about 34-fold (T7 vs. T8 and T8* vs. T9) and the booster improved the anti-spike titer by about three times compared with that of the second dose (T3 vs. T8 and T3 vs. T9), and no difference was noted between the two groups. The neutralizing titer was evaluated at T3, T7, T8, and T9. Anti-spike and neutralizing antibodies were found to be strongly correlated (r 2 = 0.980; p < 0.001). At T3, 70% of the participants had a neutralizing antibody titer >91% of total anti-spike antibodies that increased to 90% after the third dose (T8 and T9). However, when the anti-spike titer reached its lowest value (T7), the neutralizing antibody levels decreased even further, representing only 44% of total anti-spike antibodies (p < 0.0001). Our findings show that the third vaccine dose improves the humoral response, but the wane of the anti-spike and neutralizing antibody titers over time is more marked in the neutralizing antibodies., Competing Interests: Author SS is employed by Synlab Lazio Srl. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Franzese, Coppola, Silva, Santini, Cinquanta, Ottomano, Salvatore and Incoronato.)

Background: Neuroendocrine neoplasms (NENs) represent a heterogeneous class of rare tumors with increasing incidence. They are characterized by the ability to secrete peptide hormones and biogenic amines but other reliable biomarkers are lacking, making diagnosis and identification of the primary site very challenging. While in some NENs, such as the pancreatic ones, next generation sequencing technologies allowed the identification of new molecular hallmarks, our knowledge of the molecular profile of NENs from other anatomical sites is still poor., Methods: Starting from the concept that NENs from different organs may be clinically and genetically correlated, we applied a multi-omics approach by combining multigene panel testing, CGH-array, transcriptome and miRNome profiling and computational analyses, with the aim to highlight common molecular and functional signatures of gastroenteropancreatic (GEP)-NENs and medullary thyroid carcinomas (MTCs) that could aid diagnosis, prognosis and therapy., Results: By comparing genomic and transcriptional profiles, ATM-dependent signaling emerged among the most significant pathways at multiple levels, involving gene variations and miRNA-mediated regulation, thus representing a novel putative druggable pathway in these cancer types. Moreover, a set of circulating miRNAs was also selected as possible diagnostic/prognostic biomarkers useful for clinical management of NENs., Conclusions: These findings depict a complex molecular and functional landscape of NENs, shedding light on novel therapeutic targets and disease biomarkers to be exploited., (© 2022. The Author(s).)

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93-95% CI 0.917-0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

Background: Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge., Results: Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients' clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas., Conclusion: The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated., Competing Interests: The authors have declared that no competing interests exist.

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman's correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein-protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein-protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein-protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Pane, Affinito, Zanfardino, Castaldo, Incoronato, Salvatore and Franzese.)

Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein-protein interaction modules based on "hub genes", called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity.

There is an unmet need for novel non-invasive prognostic molecular tumour markers for breast cancer (BC). Accumulating evidence shows that miR-155 plays a pivotal role in tumorigenesis. Generally, miR-155 is considered an oncogenic miRNA promoting tumour growth, angiogenesis and aggressiveness of BC. Therefore, many researchers have focused on its use as a prognostic biomarker and therapeutic target. However, its prognostic value for BC patients remains controversial. To address this issue, the present systematic review aims to summarize the available evidence and give a picture of a prognostic significance of miR-155 in BC pathology. All eligible studies were searched on PubMed and EMBASE databases through various search strategies. Starting from 289 potential eligible records, data were examined from 28 studies, comparing tissue and circulating miR-155 expression levels with clinicopathological features and survival rates in BC patients. We discuss the pitfalls and challenges that need to be assessed to understand the power of miR-155 to respond to real clinical needs, highlighting the consistency, robustness or lack of results obtained to sate in translating this molecule to clinical practice. Our paper suggests that the prognostic role of miR-155 in the management of BC needs to be further verified.

Background: Aberrant expression of microRNAs (miR) has been proposed as non-invasive biomarkers for breast cancers. The aim of this study was to analyse the miR-622 level in the plasma and in tissues of breast cancer patients and to explore the role of miR-622 and its target, the NUAK1 kinase, in this context., Methods: miR-622 expression was analysed in plasma and in tissues samples of breast cancer patients by q-RT-PCR. Bioinformatics programs, luciferase assay, public dataset analysis and functional experiments were used to uncover the role of miR-622 and its target in breast cancer cells., Results: miR-622 is downregulated in plasma and in tissues of breast cancer patients respect to healthy controls and its downregulation is significantly associated with advanced grade and high Ki67 level. Modulation of miR-622 affects the motility phenotype of breast cancer cells. NUAK1 kinase is a functional target of miR-622, it is associated with poor clinical outcomes of breast cancer patients and is inversely correlated with miR-622 level., Conclusions: miR-622/NUAK1 axis is deregulated in breast cancer patients and affects the motility phenotype of breast cancer cells. Importantly, miR-622 and NUAK1 hold promises as biomarkers and as targets for breast cancers.

Leukocytes are major cellular components of the inflammatory and immune response systems. After their generation in the bone marrow from hematopoietic stem cells, they maturate as granulocytes (neutrophils, eosinophils, and basophils), monocytes, and lymphocytes. The abnormal accumulation and proliferation of immature blood cells (blasts) lead to severe and widespread diseases such as leukemia. We have recently shown that KCTD15, a member of the potassium channel tetramerization domain containing protein family (KCTD), is remarkably upregulated in leukemic B-cells. Here, we extend our investigation by monitoring the KCTD15 expression levels in circulating lymphocytes, monocytes, and granulocytes, as well as in leukemia cells. Significant differences in the expression level of KCTD15 were detected in normal lymphocytes, monocytes, and granulocytes. Interestingly, we also found overexpression of the protein following leukemic transformation in the case of myeloid cell lineage. Indeed, KCTD15 was found to be upregulated in K562 and NB4 cells, as well as in HL-60 cell lines. This in vitro finding was corroborated by the analysis of KCTD15 mRNA of acute myeloid leukemia (AML) patients reported in the Microarray Innovations in Leukemia (MILE) dataset. Collectively, the present data open interesting perspectives for understanding the maturation process of leukocytes and for the diagnosis/therapy of acute leukemias.

The goal of this review is to provide an overview of the studies aimed at integrating imaging parameters with molecular biomarkers for improving breast cancer patient's diagnosis and prognosis. The use of diagnostic imaging to extract quantitative parameters related to the morphology, metabolism, and functionality of tumors, as well as their correlation with cancer tissue biomarkers is an emerging research topic. Thanks to the development of imaging biobanks and the technological tools required for extraction of imaging parameters including radiomic features, it is possible to integrate imaging markers with genetic data. This new field of study represents the evolution of radiology-pathology correlation from an anatomic-histologic level to a genetic level, which paves new interesting perspectives for breast cancer management.

Leukemic cells originate from the malignant transformation of undifferentiated myeloid/lymphoid hematopoietic progenitors normally residing in bone marrow. As the precise molecular mechanisms underlying this heterogeneous disease are yet to be disclosed, the identification and the validation of novel actors in leukemia is of extreme importance. Here, we show that KCTD15, a member of the emerging class of KCTD ((K)potassium Channel Tetramerization Domain containing) proteins, is strongly upregulated in patients affected by B-cell type acute lymphoblastic leukemia (B-ALL) and in continuous cell lines (RS4;11, REH, TOM-1, SEM) derived from this form of childhood leukemia. Interestingly, KCTD15 downregulation induces apoptosis and cell death suggesting that it has a role in cellular homeostasis and proliferation. In addition, stimulation of normal lymphocytes with the pokeweed mitogen leads to increased KCTD15 levels in a fashion comparable to those observed in proliferating leukemic cells. In this way, the role of KCTD15 is likely not confined to the B-ALL pathological state and extends to activation and proliferation of normal lymphocytes. Collectively, data here presented indicate that KCTD15 is an important and hitherto unidentified player in childhood lymphoid leukemia, and its study could open a new scenario for the identification of altered and still unknown molecular pathways in leukemia.

The effective management of patients with breast cancer (BC) depends on the early diagnosis of the disease. Currently, BC diagnosis is based on diagnostic imaging and biopsy, while the use of non-invasive circulating biomarkers for diagnosis remains an unmet need. Among the plethora of proposed non-invasive biomarkers, circulating microRNAs (miRNAs) have been considered promising diagnostic molecules because they are very stable in biological fluids and easily detectable. Although the discovery of miRNAs has opened a new avenue for their clinical application, the clinical translatability of these molecules remains unclear. This review analyses the role of circulating miRNAs as BC diagnostic biomarkers and focuses on two essential requirements to evaluate their clinical validity: i) Specificity and ii) consistent expression between the blood and tissue. These two issues were analyzed in depth using the Human miRNA Disease Database (HMDD v3.0) and the free search engine PubMed. One hundred and sixty three BC-associated miRNAs were selected and analyzed for their specificity among all human pathologies that shared deregulation (291) and consistent expression in the bloodstream and the tissue. In addition, we provide an overview of the current clinical trials examining miRNAs in BC. In conclusion, we highlight pitfalls in the translatability of circulating miRNAs into clinical practice due to the lack of specificity and a consistent expression pattern between the tissue and blood., Competing Interests: Authors have no conflicts of interest to declare.

The aim of this study was to identify new disease-related circulating miRNAs with high diagnostic accuracy for breast cancer (BC) and to correlate their deregulation with the morpho-functional characteristics of the tumour, as assessed in vivo by positron emission tomography/magnetic resonance (PET/MR) imaging. A total of 77 untreated female BC patients underwent same-day PET/MR and blood collection, and 78 healthy donors were recruited as negative controls. The expression profile of 84 human miRNAs was screened by using miRNA PCR arrays and validated by real-time PCR. The validated miRNAs were correlated with the quantitative imaging parameters extracted from the primary BC samples. Circulating miR-125b-5p and miR-143-3p were upregulated in BC plasma and able to discriminate BC patients from healthy subjects (miR-125-5p area under the receiver operating characteristic ROC curve (AUC) = 0.85 and miR-143-3p AUC = 0.80). Circulating CA15-3, a soluble form of the transmembrane glycoprotein Mucin 1 (MUC-1) that is upregulated in epithelial cancer cells of different origins, was combined with miR-125b-5p and improved the diagnostic accuracy from 70% (CA15-3 alone) to 89% (CA15-3 plus miR-125b-5p). MiR-143-3p showed a strong and significant correlation with the stage of the disease, apparent diffusion coefficient (ADC mean ), reverse efflux volume transfer constant (Kep mean ) and maximum standardized uptake value (SUV max ), and it might represent a biomarker of tumour aggressiveness. Similarly, miR-125b-5p was correlated with stage and grade 2 but inversely correlated with the forward volume transfer constant (Ktrans mean ) and proliferation index (Ki67), suggesting a potential role as a biomarker of a relatively more favourable prognosis. In situ hybridization (ISH) experiments revealed that miR-143-3p was expressed in endothelial tumour cells, miR-125-5p in cancer-associated fibroblasts, and neither in epithelial tumour cells. Our results suggested that miR-125-5p and miR-143-3p are potential biomarkers for the risk stratification of BC, and Kaplan-Maier plots confirmed this hypothesis. In addition, the combined use of miR-125-b-5p and CA15-3 enhanced the diagnostic accuracy up to 89%. This is the first study that correlates circulating miRNAs with in vivo quantified tumour biology through PET/MR biomarkers. This integration elucidates the link between the plasmatic increase in these two potential circulating biomarkers and the biology of untreated BC. In conclusion, while miR-143-3b and miR-125b-5p provide valuable information for prognosis, a combination of miR-125b-5p with the tumour marker CA15-3 improves sensitivity for BC detection, which warrants consideration by further validation studies.

Background: The aim of the present review is to discuss how the promising field of biobanking can support health care research strategies. As the concept has evolved over time, biobanks have grown from simple biological sample repositories to complex and dynamic units belonging to large infrastructure networks, such as the Pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). Biobanks were established to support scientific knowledge. Different professional figures with varied expertise collaborate to obtain and collect biological and clinical data from human subjects. At same time biobanks preserve the human and legal rights of each person that offers biomaterial for research., Methods: A literature review was conducted in April 2019 from the online database PubMed, accessed through the Bibliosan platform. Four primary topics related to biobanking will be discussed: (i) evolution, (ii) bioethical issues, (iii) organization, and (iv) imaging., Results: Most biobanks were founded as local units to support specific research projects, so they evolved in a decentralized manner. The consequence is an urgent needing for procedure harmonization regarding sample collection, processing, and storage. Considering the involvement of biomaterials obtained from human beings, different ethical issues such as the informed consent model, sample ownership, veto rights, and biobank sustainability are debated. In the face of these methodological and ethical challenges, international organizations such as BBMRI play a key role in supporting biobanking activities. Finally, a unique development is the creation of imaging biobanks that support the translation of imaging biomarkers (identified using a radiomic approach) into clinical practice by ensuring standardization of data acquisition and analysis, accredited technical validation, and transparent sharing of biological and clinical data., Conclusion: Modern biobanks permit large-scale analysis for individuation of specific diseases biomarkers starting from biological or digital material (i.e., bioimages) with well-annotated clinical and biological data. These features are essential for improving personalized medical approaches, where effective biomarker identification is a critical step for disease diagnosis and prognosis.