Cancer Molecular Biomarkers Research Articles

AI-Guided Cancer Therapy for Patients with Coexisting Migraines.

Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein.

APOBEC3C is a novel target for the immune treatment of lower-grade gliomas

ABSTRACT Background Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C (A3C) has been identified as a cancer molecular biomarker in the past decade. However, the practical role of A3C in lower-grade gliomas (LGGs) in improving the clinical outcome remains unclear. This study aims to discuss the function of A3C in immunotherapy in LGGs. Methods The RNA-Sequencing (RNA-seq) and corresponding clinical data were extracted from UCSC Xena and the results were verified in the Chinese Glioma Genome Atlas (CGGA). Weighted gene co-expression network analysis (WGCNA) was used for screening A3C-related genes. Comprehensive bioinformation analyses were performed and multiple levels of expression, survival rate, and biological functions were assessed to explore the functions of A3C. Results A3C expression was significantly higher in LGGs than in normal tissues but lower than in glioblastoma (GBM), indicating its role as an independent prognosis predictor for LGGs. Twenty-eight A3C-related genes were found with WGCNA for unsupervised clustering analysis and three modification patterns with different outcomes and immune cell infiltration were identified. A3C and the A3C score were also correlated with immune cell infiltration and the expression of immune checkpoints. In addition, the A3C score was correlated with increased sensitivity to chemotherapy. Single-cell RNA (scRNA) analysis indicated that A3C most probably expresses on immune cells, such as T cells, B cells and macrophage. Conclusions A3C is an immune-related prognostic biomarker in LGGs. Developing drugs to block A3C could enhance the efficiency of immunotherapy and improve disease survival. Abbreviation: A3C: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C; LGGs: lower-grade gliomas; CGGA: Chinese Glioma Genome Atlas; WGCNA: Weighted gene co-expression network analysis; scRNA: Single-cell RNA; HGG: higher-grade glioma; OS: overall survival; TME: tumor microenvironment; KM: Kaplan-Meier; PFI: progression-free interval; IDH: isocitrate dehydrogenase; ROC: receiver operating characteristic; GS: gene significance; MM: module membership; TIMER: Tumor IMmune Estimation Resource; GSVA: gene set variation analysis; ssGSEA: single-sample gene-set enrichment analysis; PCA: principal component analysis; AUC: area under ROC curve; HAVCR2: hepatitis A virus cellular receptor 2; PDCD1: programmed cell death 1; PDCD1LG2: PDCD1 ligand 2; PTPRC: protein tyrosine phosphatase receptor type C; ACC: Adrenocortical carcinoma; BLCA: Bladder Urothelial Carcinoma;BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOLCholangiocarcinoma; COADColon adenocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and Neck squamous cell carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach adenocarcinoma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma

Synuclein Proteins in Cancer Development and Progression.

Synucleins are a family of small, soluble proteins mainly expressed in neural tissue and in certain tumors. Since their discovery, tens of thousands of scientific reports have been published about this family of proteins as they are associated with severe human diseases. Although the physiological function of these proteins is still elusive, their relationship with neurodegeneration and cancer has been clearly described over the years. In this review, we summarize data connecting synucleins and cancer, going from the structural description of these molecules to their involvement in tumor-related processes, and discuss the putative use of these proteins as cancer molecular biomarkers.

The roles and implications of RNA m6A modification in cancer.

N6-Methyladenosine (m6A), the most prevalent internal modification in eukaryotic mRNA, has been extensively and increasingly studied over the past decade. Dysregulation of RNA m6A modification and its associated machinery, including writers, erasers and readers, is frequently observed in various cancer types, and the dysregulation profiles might serve as diagnostic, prognostic and/or predictive biomarkers. Dysregulated m6A modifiers have been shown to function as oncoproteins or tumour suppressors with essential roles in cancer initiation, progression, metastasis, metabolism, therapy resistance and immune evasion as well as in cancer stem cell self-renewal and the tumour microenvironment, highlighting the therapeutic potential of targeting the dysregulated m6A machinery for cancer treatment. In this Review, we discuss the mechanisms by which m6A modifiers determine the fate of target RNAs and thereby influence protein expression, molecular pathways and cell phenotypes. We also describe the state-of-the-art methodologies for mapping global m6A epitranscriptomes in cancer. We further summarize discoveries regarding the dysregulation of m6A modifiers and modifications in cancer, their pathological roles, and the underlying molecular mechanisms. Finally, we discuss m6A-related prognostic and predictive molecular biomarkers in cancer as well as the development of small-molecule inhibitors targeting oncogenic m6A modifiers and their activity in preclinical models.

Extracellular Vesicles and Their Zeta Potential as Future Markers Associated with Nutrition and Molecular Biomarkers in Breast Cancer

A nutritional intervention promotes the loss of body and visceral fat while maintaining muscle mass in breast cancer patients. Extracellular vesicles (EVs) and their characteristics can be potential biomarkers of disease. Here, we explore the changes in the Zeta potential of EVs; the content of miRNA-30, miRNA-145, and miRNA-155; and their association with body composition and biomarkers of metabolic risk in breast cancer patients, before and 6 months after a nutritional intervention. Clinicopathological data (HER2neu, estrogen receptor, and Ki67), anthropometric and body composition data, and plasma samples were available from a previous study. Plasma EVs were isolated and characterized in 16 patients. The expression of miRNA-30, miRNA-145, and miRNA-155 was analyzed. The Zeta potential was associated with HER2neu (β = 2.1; p = 0.00), Ki67 (β = -1.39; p = 0.007), estrogen positive (β = 1.57; p = 0.01), weight (β = -0.09; p = 0.00), and visceral fat (β = 0.004; p = 0.00). miRNA-30 was associated with LDL (β = -0.012; p = 0.01) and HDL (β = -0.02; p = 0.05). miRNA-155 was associated with visceral fat (β = -0.0007; p = 0.05) and Ki67 (β = -0.47; p = 0.04). Our results reveal significant associations between the expression of miRNA-30 and miRNA-155 and the Zeta potential of the EVs with biomarkers of metabolic risk and disease prognosis in women with breast cancer; particularly, the Zeta potential of EVs can be a new biomarker sensitive to changes in the nutritional status and breast cancer progression.

Colorectal Cancer Liver Metastases: Genomics and Biomarkers with Focus on Local Therapies.

Molecular cancer biomarkers help personalize treatment, predict oncologic outcomes, and identify patients who can benefit from specific targeted therapies. Colorectal cancer (CRC) is the third-most common cancer, with the liver being the most frequent visceral metastatic site. KRAS, NRAS, BRAF V600E Mutations, DNA Mismatch Repair Deficiency/Microsatellite Instability Status, HER2 Amplification, and NTRK Fusions are NCCN approved and actionable molecular biomarkers for colorectal cancer. Additional biomarkers are also described and can be helpful in different image-guided hepatic directed therapies specifically for CRLM. For example, tumors maintaining the Ki-67 proliferation marker after thermal ablation have been particularly resilient to ablation. Ablation margin was also shown to be an important factor in predicting local recurrence, with a ≥10 mm minimal ablation margin being required to attain local tumor control, especially for patients with mutant KRAS CRLM.

Electrochemical ELASA: improving early cancer detection and monitoring.

The discovery of new molecular biomarkers of cancer during the last decades and the development of new diagnostic devices exploiting those have significantly contributed to the clinical analysis of cancer and to improve the outcomes. Among those, liquid biopsy sensors exploiting aptamers for the detection of cancer biomarkers in body fluids are useful and accurate tools for a fast and inexpensive non-invasive screening of population. The incorporation of aptamers in electrochemical sandwich biosensors using enzyme labels, a so-called ELASA, has demonstrated its utility to improve the detection schemes. In this review, we overview the existing ELASA assays for numerous cancer biomarkers as alternatives to the traditional ELISA and discuss their possibilities to reach the market, currently dominated by optical immunoassays.

Junctional adhesion molecule-A (JAM-A) in gynecological cancers: Current state of knowledge

Junctional adhesion molecule-A (JAM-A), also known as the F11 receptor (F11R), is one of the tight junction components. JAM-A is a transmembrane glycoprotein that regulates many cellular processes, i.e., angiogenesis, leukocyte transendothelial migration, intercellular permeability, epithelial-to-mesenchymal transition, and platelet activation. Of note, it is involved in the pathogenesis of various cancer types, including gynecological cancers. Only a few studies are available about this cancer type. Observed aberrant JAM-A expression in gynecological cancers correlates with poor patient prognosis. To the best of our knowledge, conflicting JAM-A roles in various cancer types suggest that its involvement is complex and tumor-type specific. The underlying molecular mechanisms and pathways responsible for JAM-A functions were not fully elucidated and need to be identified. Finding appropriate novel molecular cancer biomarkers may reduce observed very high mortality rates and could contribute to personalized treatment development. The main aim of the present viewpoint article is to report the current knowledge about JAM-A participation in gynecological malignancies.

Predictive immunocytochemistry in non-small cell lung cancer cytological samples

Non-small cell lung cancer (NSCLC) molecular biomarker testing is obligatory for determining therapy. The aim of this study was to compare immunocytochemistry (ICC) results of NSCLC predictive biomarkers between bronchoscopic and non-bronchoscopic type of cytology samples. This study included archive records of 1109 predictive ICC results (ALK, ROS1, and PD-L1). The ICC was done on bronchoscopic, and non-bronchoscopic NSCLC samples prepared as cytological smears and cytospins, using Dako EnVisionTM FLEX detection visualization system. The ALK, ROS1, and PD-L1 distribution between bronchoscopic and non-bronchoscopic samples was analysed. The future perspective of cytology in precision medicine was reconsidered. The obtained positive results of ALK, ROS1, and PD-L1 ICC were in concordance with the previously observed range. There was no statistically significant difference in ALK, ROS1, and PD-L1 ICC distribution between the bronchoscopic and non-bronchoscopic groups of samples (p=0.730). The comparison of PD-L1 expression, and, separately PD-L1 ≥50% expression, between two groups of samples showed no statistically significant difference (p=0.236; p=0.436). Bronchoscopic and non-bronchoscopic samples prepared as cytological smears and cytospins are a suitable, but underutilized resource for ALK, ROS1, and PD-L1 biomarker analysis. The implementation of optimized predictive immunocytochemistry assays to provide rapid and reliable results for limited tumour samples is necessary.

The Relationship Between Cervical Cancer Severity and miRNA-205 Expression in Bali

Cervical cancer is the one of leading cause of death for women worldwide. It occurs due to infection with the human papilloma virus (HPV) which attacks the cervix in the female reproductive organs. The detecting cervical cancer method is by using pap smears and liquid based cytology (LBC) and vaccinations. However, both methods are limited in detecting cervical cancer quickly and accurately. This study aims to find potential biomarkers that will be used to quickly detect the development of cervical cancer. Large-scale studies have shown that microRNA-205 (miRNA) regulation has a very important role in various types of cancer including cervical cancer. The extraction and synthesis of cDNA from 26 preparations of cervical cancer paraffin blocks were measured by quantitative real time PCR to evaluate the expression of miRNA-205 as a biomarker of cervical cancer in cervical cancer samples. Statistical results showed that there was a significant difference in the expression of miRNA-205 which was relatively higher in cervical cancer patients (p<0.05) miRNA-205. In addition, the increased expression of miRNA-205 is associated with an increase in the severity of cervical cancer. We conclude that miRNA-205 is a potential candidate for cervical cancer molecular biomarker.

Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer.

Early detection of cancer facilitates treatment and improves patient survival. We hypothesized that molecular biomarkers of cancer could be rationally predicted based on even partial knowledge of transcriptional regulation, functional pathways and gene co-expression networks. To test our data mining approach, we focused on breast cancer, as one of the best-studied models of this disease. We were particularly interested to check whether such a ‘guilt by association’ approach would lead to pan-cancer markers generally known in the field or whether molecular subtype-specific ‘seed’ markers will yield subtype-specific extended sets of breast cancer markers. The key challenge of this investigation was to utilize a small number of well-characterized, largely intracellular, breast cancer-related proteins to uncover similarly regulated and functionally related genes and proteins with the view to predicting a much-expanded range of disease markers, especially that of extracellular molecular markers, potentially suitable for the early non-invasive detection of the disease. We selected 23 previously characterized proteins specific to three major molecular subtypes of breast cancer and analyzed their established transcription factor networks, their known metabolic and functional pathways and the existing experimentally derived protein co-expression data. Having started with largely intracellular and transmembrane marker ‘seeds’ we predicted the existence of as many as 150 novel biomarker genes to be associated with the selected three major molecular sub-types of breast cancer all coding for extracellularly targeted or secreted proteins and therefore being potentially most suitable for molecular diagnosis of the disease. Of the 150 such predicted protein markers, 114 were predicted to be linked through the combination of regulatory networks to basal breast cancer, 48 to luminal and 7 to Her2-positive breast cancer. The reported approach to mining molecular markers is not limited to breast cancer and therefore offers a widely applicable strategy of biomarker mining.

Molecular Biomarkers in Cancer

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.

Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.

Comparing miR-16 and miR-1228 as an optimal endogenous control for quantification of circulating microRNAs in colorectal cancer patients

ABSTRACTObjectives:Circulating microRNAs (miRNAs) have been discovered to play a novel role in intercellular communication and cancer biology. They are emerging candidates for noninvasive molecular biomarkers of cancer and other diseases. However, current translational researches have been limited by the lack of consensus on the optimal endogenous control of circulating miRNAs quantitation. In this study, we compared two promising miRNAs, miR-1228 and miR-16, as an endogenous control. The effects of normalizers on the relative quantification of circulating miR-31 in plasma samples of colorectal cancer (CRC) were also assessed.Materials and Methods:The cel-miR-39 was a spiked-in RNA used as an external control and added to plasma samples before RNA extraction. Quantitative real-time polymerase chain reaction technology was used to analyze the expression levels of circulating miRNAs in plasma samples of 4 healthy controls and 14 CRC patients. The expression stability of the candidate controls was compared by Ct analysis and NormFinder algorithms.Results:There was no significant difference in expression level of miR-16 and miR-1228 between healthy control group and before or after therapy of CRC patient groups. The expression of miR-1228 has smaller the range Ct values (28.25-25.64)compared with those of miR-16 (24.91-20.34). The stability value of miR-1228 (0.102) is lower than that of miR-16 (0.350). The expression of miR-1228 endogenous reference candidate has lower stability value and smaller the range Ct values compared with those in miR-16. According to the range Ct values and stability value, miR-1228 is better than miR-16 as endogenous control in CRC patients. There are significant differences in circulating miR-31 expression between healthy control and CRC patients when miR-1228 was used to standardize miR-31 expression.Conclusions:miR-1228 is recommended as a better endogenous control in quantification of circulating miRNAs in CRC patients.

Novel molecular biomarkers of cancer therapy-induced cardiotoxicity in adult population: a scoping review.

AimCancer treatments are associated with cardiotoxic events that predispose to cardiac pathology and compromise the survival of patients, making necessary the identification of new molecular biomarkers to detect cardiotoxicity. This scoping review aims to identify the available evidence on novel molecular biomarkers associated with cardiotoxicity in the adult population undergoing cancer therapy.Methods and resultsThe databases Medline, Web of Science, Scopus, and Embase were screened for the identification of published studies until 23 August 2020, searching for novel molecular biomarkers reported in cancer therapy‐related cardiac dysfunction in adult patients. A total of 42 studies that met the eligibility criteria were included. Fourteen studies reported 44 new protein biomarkers, 18 studies reported 57 new single nucleotide polymorphism biomarkers, and 11 studies reported 171 new gene expression profiles associated with cardiotoxicity. Data were extracted for 272 novel molecular biomarkers reported and evaluated in 7084 cancer patients, of which only 13 were identified in more than one study (MPO, sST2, GDF‐15, TGF‐B1, rs1056892, rs1883112, rs4673, rs13058338, rs1695, miR‐1, miR‐25‐3p, miR‐34a‐5p, and miR‐423‐5p), showing values for area under the curve > 0.73 (range 0.74–0.85), odds ratio 0.26–7.17, and hazard ratio 1.28–1.80.ConclusionsMultiple studies presented a significant number of novel molecular biomarkers as promising predictors for risk assessment of cardiac dysfunction related to cancer therapy, but the characteristics of the studies carried out and the determinations applied do not allow suggesting the clinical use of these molecular biomarkers in the assessment of cancer therapy‐induced cardiotoxicity.

Who's Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy.

Simple SummaryNormal-to-malignant transition in human cells is still a poorly understood process, as well as the mechanisms leading to cancer invasion and metastasis. An in-depth characterisation and understanding of the genetic and chemical variations underlying carcinogenesis and cancer progression are paramount for improving diagnosis and design better therapeutic strategies, aiming at a better prognosis for oncology patients. The present study reports the application of spectroscopic methods (micro-Raman and infrared) for identifying specific biomarkers of malignancy in several types of human breast cancer cells. The results thus obtained led to an accurate discrimination between the highly metastatic mammary tumours (triple-negative) and the less aggressive ones (non-triple negative), as well as among triple-negative subtypes.(1) Breast cancer is presently the leading cause of death in women worldwide. This study aims at identifying molecular biomarkers of cancer in human breast cancer cells, in order to differentiate highly aggressive triple-negative from non-triple-negative cancers, as well as distinct triple-negative subtypes, which is currently an unmet clinical need paramount for an improved patient care. (2) Raman and FTIR (Fourier transform infrared) microspectroscopy state-of-the-art techniques were applied, as highly sensitive, specific and non-invasive methods for probing heterogeneous biological samples such as human cells. (3) Particular biochemical features of malignancy were unveiled based on the cells’ vibrational signature, upon principal component analysis of the data. This enabled discrimination between TNBC (triple-negative breast cancer) and non-TNBC, TNBC MSL (mesenchymal stem cell-like) and TNBC BL1 (basal-like 1) and TNBC BL1 highly metastatic and low-metastatic cell lines. This specific differentiation between distinct TNBC subtypes—mesenchymal from basal-like, and basal-like 1 with high-metastatic potential from basal-like 1 with low-metastatic potential—is a pioneer result, of potential high impact in cancer diagnosis and treatment.

Non-coding RNAs as liquid biopsy biomarkers in cancer.

Although non-coding RNAs have long been considered as non-functional "junk" RNAs, accumulating evidence in the past decade indicates that they play a critical role in pathogenesis of various cancers. In addition to their biological significance, the recognition that their expression levels are frequently dysregulated in multiple cancers have fueled the interest for exploiting their clinical potential as cancer biomarkers. In particular, microRNAs (miRNAs), a subclass of small non-coding RNAs that epigenetically modulate gene-transcription, have become one of the most well-studied substrates for the development of liquid biopsy biomarkers for cancer patients. The emergence of high-throughput sequencing technologies has enabled comprehensive molecular characterisation of various non-coding RNA expression profiles in multiple cancers. Furthermore, technological advances for quantifying lowly expressed RNAs in the circulation have facilitated robust identification of previously unrecognised and undetectable biomarkers in cancer patients. Here we summarise the latest progress on the utilisation of non-coding RNAs as non-invasive cancer biomarkers. We evaluated the suitability of multiple non-coding RNA types as blood-based cancer biomarkers and examined the impact of recent technological breakthroughs on the development of non-invasive molecular biomarkers in cancer.

Pan-cancer analysis of non-coding transcripts reveals the prognostic onco-lncRNA HOXA10-AS in gliomas.

Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in cancer and powerful biomarkers; however, most remain uncharacterized. Here, we analyze 5,592 prognostic lncRNAs in 9,446 cancers of 30 types using machine learning. We identify 166 lncRNAs whose expression correlates with survival and improves the accuracy of common clinical variables, molecular features, and cancer subtypes. Prognostic lncRNAs are often characterized by switch-like expression patterns. In low-grade gliomas, HOXA10-AS activation is a robust marker of poor prognosis that complements IDH1/2 mutations, as validated in another retrospective cohort, and correlates with developmental pathways in tumor transcriptomes. Loss- and gain-of-function studies in patient-derived glioma cells, organoids, and xenograft models identify HOXA10-AS as a potent onco-lncRNA that regulates cell proliferation, contact inhibition, invasion, Hippo signaling, and mitotic and neuro-developmental pathways. Our study underscores the pan-cancer potential of the non-coding transcriptome for identifying biomarkers and regulators of cancer progression.

Spatial omics and multiplexed imaging to explore cancer biology.

Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

Expression of miR-34a and miR-15b during the progression of cervical cancer in a murine model expressing the HPV16 E7 oncoprotein.

The high-risk human papillomavirus (HR-HPV) E7 oncoprotein appears to be a major determinant for cell immortalization and transformation altering critical processes such as cell proliferation, apoptosis, and immune response. This oncoprotein plays an essential role in cervical carcinogenesis, but other cofactors such as long-term use of hormonal contraceptives are necessary to modulate the risk of cervical cancer (CC). The role of HR-HPVs in the alteration of microRNA (miRNA) levels in persistent viral infections currently remains unclear. The aim of this study was to evaluate the miR-34a and miR-15b expression levels in the murine HPV16K14E7 (K14E7) transgenic model after chronic estrogen (E2) treatment and their involvement in CC. Interestingly, results showed that, although miR-34a expression is elevated by the HPVE7 oncogene, this expression was downregulated in the presence of both the E7 oncoprotein and chronic E2 in cervical carcinoma. On the other hand, miR-15b expression was upregulated along cervical carcinogenesis mainly by the effect of E2. These different changes in the expression levels of miR-34a and miR-15b along cervical carcinogenesis conduced to low apoptosis levels, high cell proliferation and finally, to cancerous cervical tissue development. In this work, we also determined the relative mRNA expression of Cyclin E2 (Ccne2), Cyclin A2 (Ccna2), and B cell lymphoma 2 (Bcl-2) (target genes of miR-34a and miR-15b); Sirtuin 1 (Sirt1), Cmyc, and Bax (miR-34a target genes); and p21/WAF1 (mir15b target gene) and the H-ras oncogene. Given the modifications in the expression levels of miR-34a and miR-15b during the development of cervical cancer, it will be useful to carry out further investigation to confirm them as molecular biomarkers of cancer.