Predictive and prognostic molecular markers for cancer medicine

Hypermethylation of the Gene Body in SRCIN1 Is Involved in Breast Cancer Cell Proliferation and Is a Potential Blood-Based Biomarker for Early Detection and a Poor Prognosis

Breast cancer is a leading cause of cancer mortality in women worldwide. Using the Infinium MethylationEPIC BeadChip, we analyzed plasma sample methylation to identify the SRCIN1 gene in breast cancer patients. We assessed SRCIN1-related roles and pathways for their biomarker potential. To verify the methylation status, quantitative methylation-specific PCR (qMSP) was performed on genomic DNA and circulating cell-free DNA samples, and mRNA expression analysis was performed using RT‒qPCR. The results were validated in a Western population; for this analysis, the samples included plasma samples from breast cancer patients from the USA and from The Cancer Genome Atlas (TCGA) cohort. To study the SRCIN1 pathway, we conducted cell viability assays, gene manipulation and RNA sequencing. SRCIN1 hypermethylation was identified in 61.8% of breast cancer tissues from Taiwanese patients, exhibiting specificity to this malignancy. Furthermore, its presence correlated significantly with unfavorable 5-year overall survival outcomes. The levels of methylated SRCIN1 in the blood of patients from Taiwan and the USA correlated with the stage of breast cancer. The proportion of patients with high methylation levels increased from 0% in healthy individuals to 63.6% in Stage 0, 80% in Stage I and 82.6% in Stage II, with a sensitivity of 78.5%, an accuracy of 90.3% and a specificity of 100%. SRCIN1 hypermethylation was significantly correlated with increased SRCIN1 mRNA expression (p < 0.001). Knockdown of SRCIN1 decreased the viability of breast cancer cells. SRCIN1 silencing resulted in the downregulation of ESR1, BCL2 and various cyclin protein expressions. SRCIN1 hypermethylation in the blood may serve as a noninvasive biomarker, facilitating early detection and prognosis evaluation, and SRCIN1-targeted therapies could be used in combination regimens for breast cancer patients.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics

INTRODUCTION

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment.

CONTENT

In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack.

SUMMARY AND OUTLOOK

We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Pleural Uptake Patterns in F18Fluorodeoxyglucose-Positron Emission Tomography (FDG-PET) Scans Improve the Identification of Malignant Pleural Effusions

BACKGROUND

The confirmation of malignant pleural effusions (MPE) requires an invasive procedure. Diagnosis can be difficult and may require repeated thoracentesis or biopsies. F18Fluorodeoxyglucose-Positron Emission Tomography (FDG-PET) can characterize the extent of malignant involvement in areas of increased uptake. Patterns of uptake in the pleura may be sufficient to obviate the need for further invasive procedures.

METHODS

This is a retrospective review of patients with confirmed malignancy and suspected MPE. Patients who underwent diagnostic thoracentesis with cytology and contemporaneous FDG-PET were identified for analysis. Some underwent confirmatory pleural biopsy. The uptake pattern on FDG-PET underwent blinded review and was categorized based on the pattern of uptake.

RESULTS

One hundred consecutive patients with confirmed malignancy, suspected MPE and corresponding FDG-PET scans were reviewed. MPE was confirmed in 70 patients with positive pleural fluid cytology or tissue pathology. Of the remaining patients, 15 had negative cytopathology, 14 had atypical cells and 1 had reactive cells. Positive uptake on FDG-PET was noted in 76 patients. The concordance of malignant histology and positive FDG-PET occurred in 58 of 76 patients (76%). Combining histologically confirmed MPE with atypical cytology, positive pleural FDG-PET uptake had a positive predictive value of 91% for MPE. An encasement pattern had a 100% PPV for malignancy.

CONCLUSION

Positive FDG-PET pleural uptake represents an excellent method to identify MPE, especially in patients with an encasement pattern. This may eliminate the need for additional invasive procedures in some patients, even when initial pleural cytology is negative.

Silibinin: an inhibitor for a high-expressed BCL-2A1/BFL1 protein, linked with poor prognosis in breast cancer

Breast cancer (BC) accounts for 30% of all diagnosed cases of cancer in women and remains a leading cause of cancer-related deaths among women worldwide. The current study looks for a protein from the anti-apoptotic/pro-survival BCL-2 family whose overexpression reduces survivability in BC patients and a potential inhibitor for the protein. We found BCL-2A1/BFL1 protein with high expression linked to low survivability in BC. The protein shows prognosis in 8 out of 29 categories, whereas no other family member manifests this property. Out of 7379 compounds, three small molecules (CHEMBL9509, CHEMBL2104550 and CHEMBL3545011) form an H-bond with BCL-2A1/BFL1 protein's unique residue Cys55. Of the three small molecules, we found CHEMBL9509 (Silibinin) to be a potent inhibitor. The compound forms a stable H-bond with the residue Cys55 with the lowest binding energy compared to the other two compounds. It remains stable in the BH3 binding region for more than 100 ns, whereas the other two detach from the region. Additionally, the compound is found to be better than Venetoclax and Nematoclax. We firmly believe in the compound CHEMBL9509 potency to halt BC's progression by inhibiting the BCL-2A1/BFL1 protein, increasing patients' survivability.Communicated by Ramaswamy H. Sarma.

Prognostic significance of lactate dehydrogenase and its impact on the outcomes of gastric cancer: a systematic review and meta-analysis

Background

The prognostic significance of lactate dehydrogenase (LDH) and its impact on the outcomes of gastric cancer (GC) is still unclear. We assessed the link between the levels of LDH and the overall survival (OS) and disease-free survival (DFS) in GC patients.

Methods

A comprehensive search (both electronic and manual) was carried out in PubMed via MEDLINE, Web of Science (WoS), Experta Medical Database (Embase), and CENTRAL (Cochrane Library) databases for citations that evaluated the strength of association between LDH cut-off levels and OS and/or DFS in GC. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and heterogeneity was assessed.

Results

Eighteen studies with 5328 patients were included in our review. The overall pooled HR for OS was 1.48 (95% CI: 1.22-1.80) with high heterogeneity (I2 = 86%). Subgroup analyses showed that the link between LDH and OS was more prominent in Caucasian (HR 1.50 95% CI [0.80, 2.81], p=0.21) than in Asian cohorts (HR, 1.51 95% CI [1.21, 1.87], p=0.002). No significant overall association between LDH and OS (HR = 1.12, 95% CI: 0.76-1.65, p = 0.58) was found. Similar subgroup analyses results were reported for the association between LDH and DFS.

Conclusion

In patients with GC, elevated LDH levels may correlate with worse OS and DFS, but the association is not significant. LDH is a significant predictor of OS but not of DFS. Further studies with larger sample sizes and more standardized criteria for defining elevated LDH levels are needed to confirm our findings.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42023412449.

Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics

BACKGROUND

To investigate the correlation between computed tomography (CT) radiomic characteristics and key genes for cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC).

METHODS

The region of interest was annotated at the edge of the primary tumor on enhanced CT images from 140 patients with OSCC and obtained radiomic features. Ribonucleic acid (RNA) sequencing was performed on pathological sections from 20 patients. the DESeq software package was used to compare differential gene expression between groups. Weighted gene co-expression network analysis was used to construct co-expressed gene modules, and the KEGG and GO databases were used for pathway enrichment analysis of key gene modules. Finally, Pearson correlation coefficients were calculated between key genes of enriched pathways and radiomic features.

RESULTS

Four hundred and eighty radiomic features were extracted from enhanced CT images of 140 patients; seven of these correlated significantly with cervical LNM in OSCC (p < 0.01). A total of 3527 differentially expressed RNAs were screened from RNA sequencing data of 20 cases. original_glrlm_RunVariance showed significant positive correlation with most long noncoding RNAs.

CONCLUSIONS

OSCC cervical LNM is related to the salivary hair bump signaling pathway and biological process. Original_glrlm_RunVariance correlated with LNM and most differentially expressed long noncoding RNAs.

Molecular pathology of colorectal cancer: The Saudi situation in perspective

Colorectal cancer (CRC) is one of the most common cancers worldwide, and one of the most common causes of cancer deaths. In recent times, significant advancements have been made in elucidating the molecular alterations of the disease, and the results have been an improved understanding of CRC biology, as well as the discovery of biomarkers of diagnostic, prognostic, and therapeutic significance. In this review, an evaluation is carried out of the molecular pathology research of CRC emanating from Saudi Arabia. The verdict is that the data on the molecular alterations in CRC from Saudi patients is at best modest. This dearth of molecular pathology data is aptly reflected in the paucity of molecular markers recommended for testing by the Saudi National Cancer Centre guidelines for CRC management. Large scale multi-institutional and multiregional translational studies are required to generate molecular data that would inform diagnostic, prognostic, and risk-stratification guidelines for Saudi CRC patients.

Chemo Markers as Biomarkers in Septic Shock: A Comprehensive Review of Their Utility and Clinical Applications

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, often leading to septic shock. Early diagnosis and prompt intervention are crucial for improving patient outcomes. Chemo markers, which are measurable biological substances associated with the pathophysiology of septic shock, have emerged as potential biomarkers for the identification, risk stratification, and management of this condition. This comprehensive review aims to thoroughly evaluate the utility and clinical applications of chemo markers in septic shock. The review begins by discussing the criteria for ideal chemo markers, including specificity, sensitivity, dynamic range, stability, non-invasiveness, and prognostic value. These characteristics ensure accurate diagnosis, early detection, effective monitoring, and prediction of clinical outcomes. Furthermore, the review explores the role of chemo markers in monitoring treatment response and disease progression, highlighting their ability to serve as objective indicators for assessing the effectiveness of interventions and making timely adjustments in management strategies. Moreover, the prognostic value of chemo markers in predicting outcomes is discussed, emphasizing their association with mortality, hospital stays, and the development of complications. Integration of chemo markers into prognostic models or scoring systems enhances risk stratification and informs therapeutic decisions. The review also delves into recent advances in chemo marker research and technology, emphasizing the potential for discovering novel chemo markers with enhanced diagnostic and prognostic capabilities. It highlights the use of high-throughput proteomics, genomics, and transcriptomics in identifying specific molecular signatures associated with septic shock. This contributes to a deeper understanding of the complex immune and inflammatory responses involved. In conclusion, chemo markers have emerged as valuable biomarkers in septic shock, offering potential utility in diagnosis, risk stratification, treatment monitoring, and prediction of outcomes. Continued research, validation, and integration into clinical practice are necessary to fully realize their potential in improving patient care and outcomes in septic shock.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.

Changes in circulating tumor DNA and outcomes in solid tumors treated with immune checkpoint inhibitors: a systematic review

BACKGROUND

Quantification of circulating tumor DNA (ctDNA) levels is a reliable prognostic tool in several malignancies. Dynamic changes in ctDNA levels in response to treatment may also provide prognostic information. Here, we explore the value of changes in ctDNA levels in response to immune checkpoint inhibitors (ICIs).

METHODS

We searched MEDLINE (host: PubMed) for trials of ICIs in advanced solid tumors in which outcomes were reported based on change in ctDNA levels. ctDNA reduction was defined as reported in individual trials. Typically, this was either >50% reduction or a reduction to undetectable levels. We extracted HRs and related 95% CIs and/or p values comparing ctDNA reduction versus no reduction for progression-free survival (PFS) and/or overall survival (OS). Data were then pooled in a meta-analysis. Variation in effect size was examined using subgroup analyses.

RESULTS

Eighteen trials were included in the meta-analysis. ctDNA levels were detectable in all participants in all studies prior to initiation of ICIs. A reduction in ctDNA measured 6-16 weeks after starting treatment was associated with significantly better PFS (HR 0.20; 95% CI, 0.14 to 0.28; p<0.001). Similarly, OS was superior in patients with reduced ctDNA levels (HR 0.18; 95% CI, 0.12 to 0.26; p<0.001). The results were consistent across all disease sites, lines of treatment, magnitude of change (to undetectable vs >50% reduction) and whether treatment exposure comprised single or combination ICIs.

CONCLUSIONS

In advanced solid tumors, a reduction in ctDNA levels in response to ICIs is associated with substantial improvements in outcome. ctDNA change is an early response biomarker which may allow for de-escalation of cross-sectional imaging in patients receiving ICIs or support treatment de-escalation strategies.

SUMMER: a Mendelian randomization interactive server to systematically evaluate the causal effects of risk factors and circulating biomarkers on pan-cancer survival

Genome-wide association studies (GWASs) underlying case-control design have uncovered hundreds of genetic loci involved in tumorigenesis and provided rich resources for identifying risk factors and biomarkers associated with cancer susceptibility. However, the application of GWAS in determining the genetic architecture of cancer survival remains unestablished. Here, we systematically evaluated genetic effects at the genome-wide level on cancer survival that included overall survival (OS) and cancer-specific survival (CSS), leveraging data deposited in the UK Biobank cohort of a total of 19 628 incident patients across 17 cancer types. Furthermore, we assessed the causal effects of risk factors and circulating biomarkers on cancer prognosis via a Mendelian randomization (MR) analytic framework, which integrated cancer survival GWAS dataset, along with phenome-wide association study (PheWAS) and blood genome-wide gene expression/DNA methylation quantitative trait loci (eQTL/meQTL) datasets. On average, more than 10 traits, 700 genes, and 4,500 CpG sites were prone to cancer prognosis. Finally, we developed a user-friendly online database, SUrvival related cancer Multi-omics database via MEndelian Randomization (SUMMER; http://njmu-edu.cn:3838/SUMMER/), to help users query, browse, and download cancer survival results. In conclusion, SUMMER provides an important resource to assist the research community in understanding the genetic mechanisms of cancer survival.

Overview of Artificial Intelligence in Breast Cancer Medical Imaging

The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of "omics" promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients' cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.

DNA-enabled fluorescent-based nanosensors monitoring tumor-related RNA toward advanced cancer diagnosis: A review

As a burgeoning non-invasive indicator for reproducible cancer diagnosis, tumor-related biomarkers have a wide range of applications in early cancer screening, efficacy monitoring, and prognosis predicting. Accurate and efficient biomarker determination, therefore, is of great importance to prevent cancer progression at an early stage, thus reducing the disease burden on the entire population, and facilitating advanced therapies for cancer. During the last few years, various DNA structure-based fluorescent probes have established a versatile platform for biological measurements, due to their inherent biocompatibility, excellent capacity to recognize nucleic and non-nucleic acid targets, obvious accessibility to synthesis as well as chemical modification, and the ease of interfacing with signal amplification protocols. After decades of research, DNA fluorescent probe technology for detecting tumor-related mRNAs has gradually grown to maturity, especially the advent of fluorescent nanoprobes has taken the process to a new level. Here, a systematic introduction to recent trends and advances focusing on various nanomaterials-related DNA fluorescent probes and the physicochemical properties of various involved nanomaterials (such as AuNP, GO, MnO2, SiO2, AuNR, etc.) are also presented in detail. Further, the strengths and weaknesses of existing probes were described and their progress in the detection of tumor-related mRNAs was illustrated. Also, the salient challenges were discussed later, with a few potential solutions.

Synthesis, Characterization, and Antimicrobial and Antiproliferative Effects of CuO-TiO2-Chitosan-Escin Nanocomposites on Human Leukemic MOLT4 Cells

Nanocomposites comprised of CuO-TiO2-chitosan-escin, which has adjustable physicochemical properties, provide a solution for therapeutic selectivity in cancer treatment. By controlling the intrinsic signaling primarily through the mitochondrial signaling pathway, we desired nanocomposites with enhanced anticancer activity by containing CuO-TiO2-chitosan-escin. The metal oxides CuO and TiO2, the natural polymer chitosan, and a phytochemical compound escin were combined to form CuO-TiO2-chitosan-escin nanocomposites. The synthesized nanocomposites were confirmed and characterized using FTIR spectroscopy, TEM, and UV-Vis absorption spectroscopy. A human leukemia cell line (MOLT-4) was used to assess the efficacy and selectivity of nanocomposites. Based on a cytotoxicity study, CuO-TiO2-chitosan-escin nanocomposites had inhibition concentrations (IC50) of 13.68, 8.9, and 7.14 µg/mL against human T lymphoblast cells after 24, 48, and 72 h of incubation, respectively. Compared with untreated MOLT-4 cells, CuO-TiO2-chitosan-escin nanocomposite-treated cells significantly increased (p < 0.05) caspase-3, -8, and -9 and decreased the levels of antioxidant enzymes GR, SOD, and GSH. Furthermore, MDA for lipid peroxidase and ROS levels significantly increased (p < 0.05) in the treated cells than in the untreated cells. Remarkably, CuO-TiO2-chitosan-escin nanocomposite-mediated control of cell cycles were mainly achieved through the activation of caspase-3, -8, and -9.

RNA-Seq transcriptome analysis reveals Maackia amurensis leukoagglutinin has antitumor activity in human anaplastic thyroid cancer cells

Background

Lectins are carbohydrate-binding molecules that can bind specifically to the sugar residues of glycoconjugates and are found in almost all organisms. Plant lectins subjected to many studies reported exhibiting anti-cancer activity. This study aimed to investigate the possible molecular mechanisms of Maackia amurensis leukoagglutinin II (MAL-II) treated ATCCs.

Methods and results

We tested the effects of MAL-II, which is isolated from Amur seeds, on cancerous features of 8505C human anaplastic thyroid cancer cells (ATCCs) on a large scale using RNA-Seq. Transcriptome analysis was performed using Illumina next-generation sequencing technology by using cDNA libraries obtained from total RNA isolates of ATCCs treated with 0.25 µM MAL-II for 24 h. Gene ontology and pathway enrichment analysis were performed for the systematic analysis of gene functions. Moreover, we validated RNA-Seq findings using qPCR. Our results showed that many cancer-related genes such as TENM4, STIM2, SYT12, PIEZO2, ABCG1, SPNS2, ARRB1, and IRX5 were downregulated and many anticancer genes such as HSPA6, G0S2, TNFAIP3, GEM, GADD45G, RND1, SERPINB2, and IL24 were upregulated. Also, pathway enrichment analysis showed that differentially expressed genes were found to be associated with Ras, p53, and apoptosis signaling pathways, which are some important signal transduction pathways in development, proliferation, stem cell control, and carcinogenesis.

Conclusion

Collectively, our results show that MAL-II treatment reveals significant antitumor activity by changing the expression of many cancer-related genes and implies that MAL-II treatment might be a potential candidate molecule to inhibit the malignancy of human anaplastic thyroid cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-022-07759-6.

Self-propelled micro/nanobots: A new insight into precisely targeting cancerous cells through intelligent and deep cancer penetration

Cancer overlooks are globally one of the most dangerous and life-threatening tribulations. While significant advances have been made in the targeted delivery of anti-cancer medications over the last few years, several challenges, such as low efficacy and strong toxic effects, remain to be addressed. Micro/nanomotors have been thoroughly studied for both effective cancer detection and treatment, as demonstrated by significant advancements in the architecture of smart and functional micro/nanomotor biomedical systems. Able to self-propelled within fluid media, micro/nanomotors have attractive vehicles to maximize the efficacy of tumor delivery. Here, we present the current developments in the delivery, detection, and imaging-guided treatment of micro/nanomotors in the clinical field, including cancer-related specific targeted drug delivery, and then discuss the barriers and difficulties encountered by micro/nanomotors throughout the medical process. Furthermore, this paper addresses the potential growth of micro/nanomotors for medical applications, and sets out the current drawbacks and future research directions for more advancement.

Non-Exosomal and Exosome-Derived miRNAs as Promising Biomarkers in Canine Mammary Cancer

Canine mammary cancer (CMC), similar to human breast cancer (HBC) in many aspects, is the most common neoplasm associated with significant mortality in female dogs. Due to the limited therapy options, biomarkers are highly desirable for early clinical diagnosis or cancer progression monitoring. Since the discovery of microRNAs (miRNAs or miRs) as post-transcriptional gene regulators, they have become attractive biomarkers in oncological research. Except for intracellular miRNAs and cell-free miRNAs, exosome-derived miRNAs (exomiRs) have drawn much attention in recent years as biomarkers for cancer detection. Analysis of exosomes represents a non-invasive, pain-free, time- and money-saving alternative to conventional tissue biopsy. The purpose of this review is to provide a summary of miRNAs that come from non-exosomal sources (canine mammary tumor, mammary tumor cell lines or canine blood serum) and from exosomes as promising biomarkers of CMC based on the current literature. As is discussed, some of the miRNAs postulated as diagnostic or prognostic biomarkers in CMC were also altered in HBC (such as miR-21, miR-29b, miR-141, miR-429, miR-200c, miR-497, miR-210, miR-96, miR-18a, miR19b, miR-20b, miR-93, miR-101, miR-105a, miR-130a, miR-200c, miR-340, miR-486), which may be considered as potential disease-specific biomarkers in both CMC and HBC.

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

Bioinformatics-based identification of miRNAs, mRNA, and regulatory signaling pathways involved in esophageal squamous cell carcinoma

Aim

The current study analyzed the miRNA microarray dataset (GSE66274) and gene expression microarray dataset (GSE38129) with similar samples to achieve a better understanding of miRNA-mRNA interactions.

Background

The most common form of esophageal cancer is esophageal squamous cell carcinoma (ESCC). While, miRNAs are well recognized as having a critical regulatory role in human cancer, their responsibilities and mechanisms of miRNA-mRNA in ESCC are unknown.

Methods

Differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were identified using the LIMMA package in R. In total, 478 DEmRNA (224 upregulated and 254 downregulated) and 39 DEmiRNA (15 upregulated and 24 downregulated) were screened. The RNAInter database analyzed miRNA-mRNA interactions; then, the miRNA-mRNA network was visualized by Cytoscape software. ClusterProfiler packages were used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEmRNA as targets of DEmiRNAs.

Results

KEGG pathway analysis indicated that the p53 signaling pathway, ECM-receptor interaction, and AGE-RAGE signaling pathway were significant. Cellular response to amino acid stimulus, negative regulation of apoptotic signaling pathway, and endoderm formation were most prevalent in the biological process category. Additionally, the collagen-containing extracellular matrix, actomyosin complex collagen trimers, basement membrane, and extracellular matrix structural constituent were more enriched.

Conclusion

Overall, the present survey provides evidence that could support the prognosis of esophageal tumors in the future.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Immunohistochemical Evaluation of PARP and Caspase-3 as Prognostic Markers in Prostate Carcinomas

Prostate cancer is the second most common neoplasm among men, with a high mortality rate in advanced stages. Poly (ADP-ribose) polymerase (PARP) plays an important role in repair to DNA damage, being associated with resistance to tumor cell death. Conversely, Caspase-3 is a crucial mediator of programmed cell death, being highly expressed in apoptotic cells. The aim of the present study was to characterize the expression of PARP and Caspase-3 by immunohistochemistry in patients with advanced prostate cancer. PARP and Caspase-3 were independently correlated to patients' evolution, in accordance with the classification of prognostic groups. The increase in PARP expression was positively correlated with tumor patients with poor prognosis (P < 0.0001). In contrast, a decrease in Caspase-3 expression was identified in patients with poor prognosis, when compared with prostate cancer patients with good prognosis (P = 0.0007). Numerically, 92.3% of patients previously classified with poor prognosis showed higher PARP expression, while 93.75% of patients previously classified with good prognosis showed higher levels of Caspase-3. We conclude that PARP and Caspase-3 are potential prognostic markers for prostate cancer patients with different prognosis.

A call to action: molecular pathology in Brazil

Background

Adoption of molecular pathology in Brazil is currently very limited. Of note, there are no programs for training new molecular pathologists in the country; thus, documents compiling nationally applicable information on molecular pathology are few.

Methods

A selected panel of Brazilian experts in fields related to molecular pathology were provided with a series of relevant questions to address prior to the multi-day conference. Within this conference, each narrative was discussed and edited by the entire group, through numerous drafts and rounds of discussion until a consensus was achieved.

Results

The panel proposes specific and realistic recommendations for implementing molecular pathology in cancer care in Brazil. In creating these recommendations, the authors strived to address all barriers to the widespread use and impediments to access mentioned previously within this manuscript.

Conclusion

This manuscript provides a review of molecular pathology principles as well as the current state of molecular pathology in Brazil. Additionally, the panel proposes practical and actionable recommendations for the implementation of molecular pathology throughout the country in order to increase awareness of the importance molecular pathology in Brazil.

Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review

OBJECTIVES

Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes.

METHODS

Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020. Pertinent articles detailing the development and accuracy of machine learning prediction models for oral cavity cancer outcomes were selected in a two-stage process. Quality assessment was conducted using the Quality in Prognosis Studies (QUIPS) tool and results of base studies were qualitatively synthesized by all authors. Outcomes of interest were malignant transformation of precancer lesions, cervical lymph node metastasis, as well as treatment response, and prognosis of oral cavity cancer.

RESULTS

Twenty-seven articles out of 950 citations identified from electronic and manual searching were included in this study. Five studies had low bias concerns on the QUIPS tool. Prediction of malignant transformation, cervical lymph node metastasis, treatment response, and prognosis were reported in three, six, eight, and eleven articles respectively. Accuracy of these learning models on the internal or external validation sets ranged from 0.85 to 0.97 for malignant transformation prediction, 0.78-0.91 for cervical lymph node metastasis prediction, 0.64-1.00 for treatment response prediction, and 0.71-0.99 for prognosis prediction. In general, most trained algorithms predicting these outcomes performed better than alternate methods of prediction. We also found that models including molecular markers in training data had better accuracy estimates for malignant transformation, treatment response, and prognosis prediction.

CONCLUSION

Machine learning algorithms have a satisfactory to excellent accuracy for predicting three of four oral cavity cancer outcomes i.e., malignant transformation, nodal metastasis, and prognosis. However, considering the training approach of many available classifiers, these models may not be streamlined enough for clinical application currently.

A graphical, interactive and GPU-enabled workflow to process long-read sequencing data

Background

Long-read sequencing has great promise in enabling portable, rapid molecular-assisted cancer diagnoses. A key challenge in democratizing long-read sequencing technology in the biomedical and clinical community is the lack of graphical bioinformatics software tools which can efficiently process the raw nanopore reads, support graphical output and interactive visualizations for interpretations of results. Another obstacle is that high performance software tools for long-read sequencing data analyses often leverage graphics processing units (GPU), which is challenging and time-consuming to configure, especially on the cloud.

Results

We present a graphical cloud-enabled workflow for fast, interactive analysis of nanopore sequencing data using GPUs. Users customize parameters, monitor execution and visualize results through an accessible graphical interface. The workflow and its components are completely containerized to ensure reproducibility and facilitate installation of the GPU-enabled software. We also provide an Amazon Machine Image (AMI) with all software and drivers pre-installed for GPU computing on the cloud. Most importantly, we demonstrate the potential of applying our software tools to reduce the turnaround time of cancer diagnostics by generating blood cancer (NB4, K562, ME1, 238 MV4;11) cell line Nanopore data using the Flongle adapter. We observe a 29x speedup and a 93x reduction in costs for the rate-limiting basecalling step in the analysis of blood cancer cell line data.

Conclusions

Our interactive and efficient software tools will make analyses of Nanopore data using GPU and cloud computing accessible to biomedical and clinical scientists, thus facilitating the adoption of cost effective, fast, portable and real-time long-read sequencing.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07927-1.

Copy number alterations identify a smoking-associated expression signature predictive of poor outcome in head and neck squamous cell carcinoma

Cigarette smoking is a risk factor for the development of head and neck squamous cell carcinoma (HNSCC), partially due to tobacco-induced large-scale chromosomal copy-number alterations (CNAs). Identifying CNAs caused by smoking is essential in determining how gene expression from such regions impact tumor progression and patient outcome. We utilized The Cancer Genome Atlas (TCGA) whole genome sequencing data for HNSCC to directly identify amplified or deleted genes correlating with smoking pack-year based on linear modeling. Internal cross-validation identified 35 CNAs that significantly correlated with patient smoking, independent of human papillomavirus (HPV) status. The most abundant CNAs were chromosome 11q13.3-q14.4 amplification and 9p23.1/9p24.1 deletion. Evaluation of patient amplicons reveals four different patterns of 11q13 gene amplification in HNSCC resulting from breakage-fusion-bridge (BFB) events. . Predictive modeling identified 16 genes from these regions that denote poorer overall and disease-free survival with increased pack-year use, constituting a smoking-associated expression signature (SAES). Patients with altered expression of signature genes have increased risk of death and enhanced cervical lymph node involvement. The identified SAES can be utilized as a novel predictor of increased disease aggressiveness and poor outcome in smoking-associated HNSCC.

Transcript annotation tool (TransAT): an R package for retrieving annotations for transcript-specific genetic variants

BACKGROUND

An individual's genetics play a role in how RNA transcripts are generated from DNA and consequently in their translation into protein. Transcriptional and translational profiling of patients furnishes the information that a specific marker is present; however, it fails to provide evidence whether the marker correlates with response to a therapeutic agent. A comparative analysis of the frequency of genetic variants, such as single nucleotide polymorphisms (SNPs), in diseased and general populations can identify pathogenic variants in individual patients. This is in part because SNPs have considerable effects on protein function and gene expression when they occur in coding regions and regulatory sequences, respectively. Therefore, a tool that can help users to obtain the allele frequency for a corresponding transcript is the need of the day. Several annotation tools such as SNPnexus and VariED are publicly available; however, none of them can use transcript IDs as input and provide the corresponding genomic positions of variants.

RESULTS

In this study, we developed an R package, called transcript annotation tool (TransAT), that provides (i) SNP ID and genomic position for a user-provided transcript ID from patients, and (ii) allele frequencies for the SNPs from publicly available global populations. All data elements are extracted, collected, and displayed in an easily downloadable format in two simple command lines. TransAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It is available at https://github.com/ShihChingYu/TransAT and can be downloaded and installed using devtools::install_github("ShihChingYu/TransAT", force=T) on the R execution page. Thereafter, all functions can be executed by loading the package into R with library(TransAT).

CONCLUSIONS

TransAT is a novel tool that seamlessly provides genetic annotations for queried transcripts. Such easily obtainable information would be greatly advantageous for physicians, assisting them to make individualized decisions about specific drug treatments. Moreover, allele frequencies from user-chosen global ethnic populations will highlight the importance of ethnicity and its effect on patient pathogenicity.

Single nucleotide polymorphisms as the efficient prognostic markers in breast cancer

BACKGROUND

Breast cancer (BC) is known as the most common malignancy in women. Environmental and genetic factors are associated with BC progression. Genetic polymorphisms have been reported as important risk factors of BC prognosis and drug response. Main body: Therefore, in the present review, we have summarized all single nucleotide polymorphisms (SNPs) which have been significantly associated with drug response in BC patients around the world. We have also categorized the reported SNPs based on their related genes functions to clarify the molecular biology of drug responses in BC.

CONCLUSION

The majority of SNPs were reported in detoxifying enzymes, which introduced such genes as the main genetic risk factors during BC drug responses. This review paves the way for introducing a prognostic panel of SNPs for the BC patients in the world.

Oncogenic and Tumor Suppressive Components of the Cell Cycle in Breast Cancer Progression and Prognosis

Cancer, a disease of inappropriate cell proliferation, is strongly interconnected with the cell cycle. All cancers consist of an abnormal accumulation of neoplastic cells, which are propagated toward uncontrolled cell division and proliferation in response to mitogenic signals. Mitogenic stimuli include genetic and epigenetic changes in cell cycle regulatory genes and other genes which regulate the cell cycle. This suggests that multiple, distinct pathways of genetic alterations lead to cancer development. Products of both oncogenes (including cyclin-dependent kinase (CDKs) and cyclins) and tumor suppressor genes (including cyclin-dependent kinase inhibitors) regulate cell cycle machinery and promote or suppress cell cycle progression, respectively. The identification of cyclins and CDKs help to explain and understand the molecular mechanisms of cell cycle machinery. During breast cancer tumorigenesis, cyclins A, B, C, D1, and E; cyclin-dependent kinase (CDKs); and CDK-inhibitor proteins p16, p21, p27, and p53 are known to play significant roles in cell cycle control and are tightly regulated in normal breast epithelial cells. Following mitogenic stimuli, these components are deregulated, which promotes neoplastic transformation of breast epithelial cells. Multiple studies implicate the roles of both types of components-oncogenic CDKs and cyclins, along with tumor-suppressing cyclin-dependent inhibitors-in breast cancer initiation and progression. Numerous clinical studies have confirmed that there is a prognostic significance for screening for these described components, regarding patient outcomes and their responses to therapy. The aim of this review article is to summarize the roles of oncogenic and tumor-suppressive components of the cell cycle in breast cancer progression and prognosis.

Intrinsic and extrinsic pathways of apoptosis: Role in cancer development and prognosis

Apoptosis, also named programmed cell death, is a fundament process required for morphogenetic homeostasis during early development and in pathophysiological conditions. It is come into existence in 1972 by work of Kerr, Wyllie and Currie and later on investigated during the research on development of the C. elegans. Trigger by several stimuli, apoptosis is necessary during the embryonic development and aging as homeostatic mechanism to control the cell population and also play a key role as defense mechanism against the immune responses and elimination of damaged cells. Cancer, a genetic disease, is a growing burden on the health and economy of both developing and developed countries. Every year there is tremendously increasing in the number of new cancer cases and mortality rate. Although, there is a significant improvement have been made in biotechnological and bioinformatic fields however, the therapeutic advantages and cancer etiology is still under explored. Several studies determined the deregulation of different apoptotic components during the cancer development and progression. Apoptosis relies on activation of distinct signaling pathways that are often deregulated in cancer. Thus, exploring the single or more than one apoptotic component underlying their expression in carcinogenesis could help to track the disease progression. Current book chapter will provide the several evidences supporting the use of different apoptotic components as prognosis and prediction markers in various human cancer types.

Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics

BACKGROUND

The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures.

METHOD

Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively.

RESULTS

Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics.

CONCLUSION

This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.

MicroRNAs as Modulators of Oral Tumorigenesis-A Focused Review

Oral cancers constitute the majority of head and neck tumors, with a relatively high incidence and poor survival rate in developing countries. While the five-year survival rates of the oral cancer patients have increased to 65%, the overall survival for advanced stages has been at 27% for the past ten years, emphasizing the necessity for further understanding the etiology of the disease, diagnosis, and formulating possible novel treatment regimens. MicroRNAs (miRNAs), a family of small non-coding RNA, have emerged as master modulators of gene expression in various cellular and biological process. Aberrant expression of these dynamic molecules has been associated with many human diseases, including oral cancers. The deregulated miRNAs have been shown to control various oncogenic processes, including sustaining proliferative signaling, evading growth suppressors, resisting cell death activating invasion and metastasis, and inducing angiogenesis. Hence, the aberrant expression of miRNAs associated with oral cancers, makes them potential candidates for the investigation of functional markers, which will aid in the differential diagnosis, prognosis, and development of novel therapeutic regimens. This review presents a holistic insight into our understanding of the role of miRNAs in regulating various hallmarks of oral tumorigenesis.

A review of breast cancer pathology reports in Nigeria

Background

Diagnosis and treatment of cancer rely heavily on imaging, histopathology and molecular information. Incomplete or missing tumour information can hinder the delivery of high-quality care in oncology practice, especially in resource-limited countries. To evaluate the completeness of histopathology reporting in a real-world setting and identify areas for future cancer care delivery research efforts, we retrospectively analysed reports from patients diagnosed with breast cancer who received care at a high-volume oncology department at a hospital in Lagos, Nigeria.

Methods

Demographic, institutional and histopathology characteristics were retrospectively obtained from 1,001 patient records from 2007 to 2016. Completeness was defined as reporting five tumour features (tumour histology, tumour grade, laterality, oestrogen receptor (ER) or progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2)) for biopsy specimens and seven tumour features (tumour size, tumour histology, tumour grade, laterality, ER/PR, HER2 and lymph node involvement) for surgical specimens.

Results

The mean age of patients was 48.6 ± 11.7 years with a predominantly female population (99.3%). A majority of pathologic reports were produced after 2011, and two-thirds of the reports originated from centres or labs within Lagos, Nigeria (67.7%). Most reports documented primary site (98.0%) and specimen type (85.0%) while other characteristics were less often recorded. This led to substantial variation in reporting between biopsy (13.4%) and surgical (6.1%) specimens for an overall low pathology report completeness <10%.

Conclusion

The majority of patient records analysed lacked complete documentation of breast cancer histopathological characteristics commonly used in oncology practice. Our study highlights a need to identify and address the contributing factors for incomplete histopathological reporting in Nigeria and will guide future clinical programmatic developments.

Detecting survival-associated biomarkers from heterogeneous populations

Detection of prognostic factors associated with patients' survival outcome helps gain insights into a disease and guide treatment decisions. The rapid advancement of high-throughput technologies has yielded plentiful genomic biomarkers as candidate prognostic factors, but most are of limited use in clinical application. As the price of the technology drops over time, many genomic studies are conducted to explore a common scientific question in different cohorts to identify more reproducible and credible biomarkers. However, new challenges arise from heterogeneity in study populations and designs when jointly analyzing the multiple studies. For example, patients from different cohorts show different demographic characteristics and risk profiles. Existing high-dimensional variable selection methods for survival analysis, however, are restricted to single study analysis. We propose a novel Cox model based two-stage variable selection method called "Cox-TOTEM" to detect survival-associated biomarkers common in multiple genomic studies. Simulations showed our method greatly improved the sensitivity of variable selection as compared to the separate applications of existing methods to each study, especially when the signals are weak or when the studies are heterogeneous. An application of our method to TCGA transcriptomic data identified essential survival associated genes related to the common disease mechanism of five Pan-Gynecologic cancers.

Extracellular RNAs as potential biomarkers for cancer

The discovery that all cells secrete extracellular vesicles (EVs) to shuttle proteins and nucleic acids to recipient cells suggested they play an important role in intercellular communication. EVs are widely distributed in many body fluids, including blood, cerebrospinal fluid, urine and saliva. Exosomes are nano-sized EVs of endosomal origin that regulate many pathophysiological processes including immune responses, inflammation, tumour growth, and infection. Healthy individuals release exosomes with a cargo of different RNA, DNA, and protein contents into the circulation, which can be measured non-invasively as biomarkers of healthy and diseased states. Cancer-derived exosomes carry a unique set of DNA, RNA, protein and lipid reflecting the stage of tumour progression, and may serve as diagnostic and prognostic biomarkers for various cancers. However, many gaps in knowledge and technical challenges in EVs and extracellular RNA (exRNA) biology, such as mechanisms of EV biogenesis and uptake, exRNA cargo selection, and exRNA detection remain. The NIH Common Fund-supported exRNA Communication Consortium was launched in 2013 to address major scientific challenges in this field. This review focuses on scientific highlights in biomarker discovery of exosome-based exRNA in cancer and its possible clinical application as cancer biomarkers.

Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections

In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.

[Multimodal, multiparametric and genetic breast imaging]

CLINICAL/METHODOLOGICAL ISSUE

Multiparametric magnetic resonance imaging (MRI) aims to visualize and quantify biological, physiological and pathological processes at the cellular and molecular level and provides valuable information about key processes in cancer development and progression. "Omics" strategies (genomics, transcriptomics, proteomics, metabolomics) have many uses in oncology.

STANDARD RADIOLOGICAL METHODS

Multiparametric MRI of the breast currently includes T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI (DCE-MRI) METHODOLOGICAL INNOVATIONS: Additional parameters such as proton magetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), blood oxygen level-dependent (BOLD), hyperpolarized (HP) MRI or lipid MRS are currently being developed and are being evaluated in breast cancer diagnostics.

ACHIEVEMENTS

Radiogenomics is a new direction in medical science that has been made possible by significant advances in imaging and image analysis methods, as well as the development of techniques to extract and correlate various imaging parameters with "omics" data. The aim of radiogenomics is to correlate imaging characteristics (phenotypes) with gene expression patterns, gene mutations and other genome-associated properties and is the evolution of the correlation between radiology and pathology from the anatomical-histological to the molecular level. Quantitative and qualitative imaging biomarkers provide insights into the complex tumor biology. Initial results suggest that radiogemics will play an important role in the diagnosis, prognosis, and treatment of breast cancer.

PRACTICAL RECOMMENDATIONS

This article provides an overview of the current state of radiogenomics of the breast and future applications and challenges.

Ultrasensitive electrochemical determination of the cancer biomarker protein sPD-L1 based on a BMS-8-modified gold electrode

This work describes the modification of a gold electrode with the BMS-8 compound that interacts with the Programmed Death-Ligand 1 (PD-L1), an immune checkpoint protein. The results show that we can confirm the presence of the sPD-L1 in the concentration range of 10^-18 to 10^-8 M using electrochemical impedance spectroscopy (EIS) with a limit of detection (LOD) of 1.87 × 10^-14 M for PD-L1 (S/N = 3.3) and at a concentration of 10^-14 M via cyclic voltammetry (CV). Additionally, high-resolution X-ray photoelectron spectroscopy (XPS), contact angle, and surface free energy measurements were applied to confirm the functionalization of the electrode. We investigated the selectivity of the electrode for other proteins: Programmed Death-1 (PD-1), cluster of differentiation 160 (CD160), and B- and T-lymphocyte attenuator (BTLA) at concentrations of 10^-8 M. Differentiation between PD-L1 and PD-1 was achieved based on the analysis of the capacitance effect frequency dispersion at the surface of the modified Au electrode with BMS-8 after incubation at various concentrations of PD-L1 and PD-1 proteins in the range of 10^-18 to 10^-8 M. Significant differences were observed in the heterogeneity of PD-L1 and PD-1. The results of the quasi-capacitance studies demonstrate that BMS-8 strongly and specifically interacts with the PD-L1 protein.

Artificial intelligence technologies empowering identification of novel diagnostic molecular markers in gastric cancer

In recent clinical practice the molecular diagnostics have been significantly empowered and upgraded by the use of Artificial Intelligence and its assisted technologies. The use of Machine leaning and Deep Learning Neural network architectures have brought in a new dimension in clinical oncological research and development. These algorithm based software system with enhanced digital image analysis have emerged into a new branch of digital pathology and contributed immensely towards precision medicine and personal diagnostics. In India, gastric cancer is one of the most common cancers in males as well as in females. Various molecular biomarkers are associated with gastric cancer development and progression of which HER2 protein, a transmembrane tyrosine kinase (TK) receptor of epidermal growth factor receptors (EGFRs) family is of prime importance. The EGF receptor expression in gastric cancer is linked with its prognostics and theragnostics. These expressions are assessed by immunohistochemistry (IHC) and molecular techniques such as Fluorescence in-situ hybridization (FISH), as per recommendations for HER2 targeted immunotherapy. These have motivated the software giants like Google Inc. to produce innovative state of art technologies mimicking human traits such as learning and problem solving skill sets. This field is still under development and is slowly evolving and capturing global importance in recent times. A literature search on PubMed was performed to access updated information for this manuscript.

Role of hormone receptors and HER2 as prospective molecular markers for breast cancer: An update

This review provides an updated account on the current methods, principles and mechanism of action of therapies for the detection of molecular markers of therapeutic importance in the prognosis of breast cancer progression and recurrence, which includes estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor2 (HER2). Indeed, hormone-receptors namely, ER, PR, proto-oncogene HER2 are the basic molecular markers that are recognized and established prognostic factors and predictors of response, for therapeutic practice. These markers can be detected by using immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), which are established, faster and cost effective detection methods. These molecular markers along with clinicopathological prognostic parameters give the best prediction of the prognosis of cancer recurrence and progress. Finally, hormone receptors and HER2 as molecular markers are of prime therapeutic importance and have the capability to take part in future drug development techniques.

Bioinformatics-Based Identification of a circRNA-miRNA-mRNA Axis in Esophageal Squamous Cell Carcinomas

Background

Esophageal squamous cell carcinoma (ESCC) has a poor prognosis due to the lack of early disease symptoms. Using bioinformatics tools, this study aimed to discover differentially expressed nonprotein-coding RNAs and genes with potential prognostic relevance in ESCC.

Methods

Two microRNAs (miRNAs) and one circular RNA (circRNA) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression of miRNAs (DEMs) and circRNAs (DECs) was, respectively, identified in ESCC tissue and compared to adjacent healthy tissue. Further analysis was performed using the miRNA microarray datasets, where miRTarBase was used to predict which messenger RNAs (mRNAs) was present. This was followed by protein-protein interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses. Moreover, cytoHubba and UALCAN were used to predict the important nodes and perform patient survival analysis, respectively. The miRNA-associated circRNAs were predicted using the ENCORI website. The interaction between DECs and the predicted circRNAs was also determined. A circRNA-miRNA-mRNA axis was constructed.

Results

Associated with RAP1B and circ_0052867, two miRNAs (miR-133b and miR-139-5p) were identified as being differentially expressed and downregulated across the two datasets. Finally, the circ_0052867/miR-139-5p/RAP1B regulatory axis was established.

Conclusion

This study provides support for the possible mechanisms of disease progression in ESCC.

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

BACKGROUND

This study intended to determine important genes related to the prognosis and recurrence of breast cancer.

METHODS

Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed.

RESULTS

Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors.

CONCLUSION

ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

An Overview of Promising Biomarkers in Cancer Screening and Detection

Applications of biomarkers have been proved in oncology screening, diagnosis, predicting response to treatment as well as monitoring the progress of the disease. Considering the crucial role played by them during different disease stages, it is extremely important to evaluate, validate, and assess them to incorporate them into routine clinical care. In this review, the role of few most promising and successfully used biomarkers in cancer detection, i.e. PD-L1, E-Cadherin, TP53, Exosomes, cfDNA, EGFR, mTOR with regard to their structure, mode of action, and reports signifying their pathological significance, are addressed. Also, an overview of some successfully used biomarkers for cancer medicine has been presented. The study also summarizes biomarker-driven personalized cancer therapy i.e., approved targets and indications, as per the US FDA. The review also highlights the increasingly prominent role of biomarkers in drug development at all stages, with particular reference to clinical trials. The increasing utility of biomarkers in clinical trials is clearly evident from the trend shown, wherein ~55 percent of all oncology clinical trials in 2019 were seen to involve biomarkers, as opposed to ~ 15 percent in 2001, which clearly proves the essence and applicability of biomarkers for synergizing clinical information with tumor progression. Still, there are significant challenges in the implementation of these possibilities with strong evidence in cost-- effective manner.

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value ~10-8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value ~10-6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value ~10-9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-value ~10-15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

Gene expression profiling of glioblastoma cell lines depending on TP53 status after tumor-treating fields (TTFields) treatment

Glioblastoma is frequently associated with TP53 mutation, which is linked to a worse prognosis and response to conventional treatments (chemoradiotherapy). Therefore, targeting TP53 is a promising strategy to overcome this poor therapeutic response. Tumor-treating fields (TTFields) are a recently approved treatment for newly diagnosed glioblastoma, which involves direct application of low-intensity, intermediate-frequency alternating electric fields to the tumor, thereby offering a local tumor-killing effect. However, the influence of TP53 mutation status on the effectiveness of TTFields is controversial. Here, we identified the key gene signatures and pathways associated with TTFields in four glioblastoma cell lines varying in TP53 mutation status using gene profiling and functional annotation. Overall, genes associated with the cell cycle, cell death, and immune response were significantly altered by TTFields regardless of TP53 status. TTFields appeared to exert enhanced anti-cancer effects by altering the immune system in the inflammatory environment and regulating cell cycle- and cell death-related genes, but the precise genes influenced vary according to TP53 status. These results should facilitate detailed mechanistic studies on the molecular basis of TTFields to further develop this modality as combination therapy, which can improve the therapeutic effect and minimize side effects of chemoradiotherapy.

A Survey Exploring Personalised Medicine amongst Radiography Academics within the United Kingdom

Introduction

This article explores the application of personalized medicine (PM) within the academic environment in the United Kingdom. There is a growing acceptance of the utility of PM in health care and the role medical imaging can play. In response, this article explored the views and opinions of diagnostic and therapeutic academics and the utilization of PM in education.

Methods

This study primarily adopted a quantitative approach using an online survey. However, participants were also encouraged to provide qualitative comments in response to open-ended questions. The survey was distributed to radiography and radiotherapy academic departments, which received a response rate of 29%.

Results

The findings identify some important considerations. On the one hand some participants reported teaching PM on their programmes (24%, n = 16), whereas 30% (n = 20) did not. Importantly, the remaining academics (46%) were either unsure or did not know what PM was. This finding, coincided with qualitative commentary, highlights some discrepancies linked to knowledge and understanding of PM within higher education and highlights areas where academics may need additional support.

Conclusion

This article concludes by recognizing the challenges of delivering PM by some academics. It is noted that although the findings cannot be fully generalized, it does highlight fragmented understanding of PM among academic staff. This is important to reflect upon following the increasing requirements for radiography to become “more personalized.”