Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

Ethanolic Extract of Averrhoa carambola Leaf Has an Anticancer Activity on Triple-Negative Breast Cancer Cells: An In Vitro Study

Background/Objectives: Averrhoa carambola, or star fruit, is a shrub known for its medicinal properties, especially due to bioactive metabolites identified in its roots and fruit with anti-cancer activity. However, the biological effects of its leaves remain unexplored. This study aimed to assess the effects of ethanolic extract from A. carambola leaves on triple-negative breast cancer (TNBC), an aggressive subtype lacking specific therapy. Methods: Phytochemical analysis and HPLC profile and additional cell line evaluation employing MDA-MB-231 were carried out. Results: Phytochemical screening revealed that the ethanolic extract was rich in flavonoids, saponins, and steroids, demonstrating an antioxidant capacity of 45%. 1H NMR analysis indicated the presence of flavonoids, terpenes, and glycoside-like compounds. Cell viability assays showed a concentration-dependent decrease in viability, with an IC50 value of 20.89 μg/mL at 48 h. Clonogenic assays indicated significant inhibition of replicative immortality, with only 2.63% survival at 15 μg/mL. Migration, assessed through a wound healing assay, was reduced to 3.06% at 100 μg/mL, with only 16.23% of cells remaining attached. An additive effect was observed when combining lower concentrations of the extract with doxorubicin, indicating potential synergy. Conclusions: These results suggest that the ethanolic extract of A. carambola leaves contains metabolites with anti-cancer activity against TNBC cells, supporting further research into their bioactive compounds and therapeutic potential.

AH-6809 mediated regulation of lung adenocarcinoma metastasis through NLRP7 and prognostic analysis of key metastasis-related genes

Introduction

Lung adenocarcinoma (LUAD) has become one of the leading causes of cancer-related deaths globally, with metastasis representing the most lethal stage of the disease. Despite significant advances in diagnostic and therapeutic strategies for LUAD, the mechanisms enabling cancer cells to breach the blood-brain barrier remain poorly understood. While genomic profiling has shed light on the nature of primary tumors, the genetic drivers and clinical relevance of LUAD metastasis are still largely unexplored.

Objectives

This study aims to investigate the genomic differences between brain-metastatic and non-brain-metastatic LUAD, identify potential prognostic biomarkers, and evaluate the efficacy of AH-6809 in modulating key molecular pathways involved in LUAD metastasis, with a focus on post-translational modifications (PTMs).

Methods

Genomic analyses were performed using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between brain-metastatic and non-metastatic LUAD samples were identified. Key gene modules were determined using Weighted Gene Co-expression Network Analysis (WGCNA), and their prognostic significance was assessed through Kaplan-Meier analysis. Cellular experiments, including CCK8 and qRT-PCR assays, were conducted to evaluate the anti-cancer effects of AH-6809 in LUAD cells. Apoptosis and inflammatory marker expression were assessed using immunofluorescence.

Results

Genomic analysis differentiated brain-metastatic from non-brain-metastatic LUAD and identified NLRP7, FIBCD1, and ELF5 as prognostic markers. AH-6809 significantly suppressed LUAD cell proliferation, promoted apoptosis, and modulated epithelial-mesenchymal transition (EMT) markers. These effects were reversed upon NLRP7 knockdown, highlighting its role in metastasis. Literature analysis further supported AH-6809's tumor-suppressive activity, particularly in NLRP7 knockdown cells, where it inhibited cell growth and facilitated apoptosis. AH-6809 was also found to affect SUMO1-mediated PTMs and downregulate EMT markers, including VIM and CDH2. NLRP7 knockdown partially reversed these effects. Immunofluorescence revealed enhanced apoptosis and inflammation in lung cancer cells, especially in NLRP7 knockdown cells treated with AH-6809. The regulatory mechanisms involve SUMO1-mediated post-translational modifications and NQO1. Further studies are required to elucidate the molecular mechanisms and assess the clinical potential of these findings.

Conclusion

These findings demonstrate the critical role of NLRP7 and associated genes in LUAD metastasis and suggest that AH-6809 holds promise as a potential therapeutic agent for brain-metastatic LUAD.

Identifying colorectal cancer subtypes and establishing a prognostic model using metabolic plasticity and ferroptosis genes

Metabolic plasticity and ferroptosis are essential for colorectal cancer (CRC) progression. The effects and prognostic value of metabolic plasticity- and ferroptosis-related genes (MPFRGs) in CRC remain unclear. We established a prognostic model for CRC patients by identifying important genes in metabolic plasticity and ferroptosis. Data of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus; MPFRG data were obtained from GeneCards and FerrDb. We performed functional (to explore differences between the two metabolic subtypes) and single-sample gene set (to assess the immune environment) enrichment analyses. Immunophenotype, tumor immunological dysfunction, and exclusion scores were assessed to determine patient immune responses. A least absolute shrinkage and selection operator-Cox regression model comprising 10 significant differentially expressed genes of metabolic plasticity and ferroptosis (MPFDEGs) was constructed using TCGA training cohort and validated using the GSE17536 and GSE39582 datasets. We established a nomogram comprising metabolic plasticity- and ferroptosis-based signatures, revealing the clinical application and potential molecular mechanisms underlying the role of MPFRGs in CRC. Our model (developed based on 10 MPFDEGs) is efficient for calculating the overall survival of CRC patients. Our findings provide new strategies for the clinical management and individualized treatment of these patients.

Comparative Analysis of Gene Expression Analysis Methods for RNA in Situ Hybridization Images

Gene expression analysis is pivotal in cancer research and clinical practice. Although traditional methods lack spatial context, RNA in situ hybridization (RNA-ISH) is a powerful technique that retains spatial tissue information. Here, RNAscope score, RT-droplet digital PCR, and automated QuantISH and QuPath were used for quantifying RNA-ISH expression values from formalin-fixed, paraffin-embedded samples. The methods were compared using high-grade serous ovarian carcinoma samples, focusing on CCNE1, WFDC2, and PPIB genes. The findings demonstrate good concordance between automated methods and RNAscope, with RT-droplet digital PCR showing less concordance. Additionally, QuantISH exhibits robust performance, even for low-expressed genes like CCNE1, showcasing its modular design and enhancing accessibility as a viable alternative for gene expression analysis.

Unbiased discovery of cancer pathways and therapeutics using Pathway Ensemble Tool and Benchmark

Correctly identifying perturbed biological pathways is a critical step in uncovering basic disease mechanisms and developing much-needed therapeutic strategies. However, whether current tools are optimal for unbiased discovery of relevant pathways remains unclear. Here, we create "Benchmark" to critically evaluate existing tools and find that most function sub-optimally. We thus develop the "Pathway Ensemble Tool" (PET), which outperforms existing methods. Deploying PET, we identify prognostic pathways across 12 cancer types. PET-identified prognostic pathways offer additional insights, with genes within these pathways serving as reliable biomarkers for clinical outcomes. Additionally, normalizing these pathways using drug repurposing strategies represents therapeutic opportunities. For example, the top predicted repurposed drug for bladder cancer, a CDK2/9 inhibitor, represses cell growth in vitro and in vivo. We anticipate that using Benchmark and PET for unbiased pathway discovery will offer additional insights into disease mechanisms across a spectrum of diseases, enabling biomarker discovery and therapeutic strategies.

Genomic and Epigenomic Biomarkers of Immune Checkpoint Immunotherapy Response in Melanoma: Current and Future Perspectives

Immune checkpoint inhibitors (ICIs) demonstrate durable responses, long-term survival benefits, and improved outcomes in cancer patients compared to chemotherapy. However, the majority of cancer patients do not respond to ICIs, and a high proportion of those patients who do respond to ICI therapy develop innate or acquired resistance to ICIs, limiting their clinical utility. The most studied predictive tissue biomarkers for ICI response are PD-L1 immunohistochemical expression, DNA mismatch repair deficiency, and tumour mutation burden, although these are weak predictors of ICI response. The identification of better predictive biomarkers remains an important goal to improve the identification of patients who would benefit from ICIs. Here, we review established and emerging biomarkers of ICI response, focusing on epigenomic and genomic alterations in cancer patients, which have the potential to help guide single-agent ICI immunotherapy or ICI immunotherapy in combination with other ICI immunotherapies or agents. We briefly review the current status of ICI response biomarkers, including investigational biomarkers, and we present insights into several emerging and promising epigenomic biomarker candidates, including current knowledge gaps in the context of ICI immunotherapy response in melanoma patients.

Potential Signature Therapeutic Biomarkers TOP2A, MAD2L1, and CDK1 in Colorectal Cancer: A Systems Biomedicine-Based Approach

Colorectal cancer is the third deadliest and fourth most diagnosed cancer. It is heterogeneously driven by varied mutations and mutagens, and thus, it is challenging for targeted therapy. The rapid advancement of high-throughput technology presents considerable opportunities for discovering new colon cancer biomarkers. In the present study, we have explored and identified the biomarkers based on molecular interactions. We curated cancer datasets that were not micro-dissected and performed gene expression analysis. The protein-protein interactions were curated, and a network was constructed for the up-regulated genes. The hub genes were analyzed using 12 different topological parameters. The correlation analysis selected TOP2A, CDK1, CCNB1, AURKA, and MAD2L1 as hub genes. Further, survival analysis was performed to determine the effectiveness of the hub gene on the patient's survival rate. Our findings explore various transcription factors such as E2F4, FOXM1, E2F6, MAX, and SIN3A, along with kinases CSNK2A1, MAPK14, CDK1, CDK4, and CDK2, as potential molecular signatures and aid researchers in understanding the pathophysiological mechanisms underlying CRC development and thus providing novel therapeutic and diagnostic recourse. Furthermore, investigating miRNAs, we focused on hsa-miR-215-5p, hsa-miR-192-5p, and hsa-miR-193b-3p due to their observed impact on a diverse set of colorectal cancer genes. Thereby, the current approach brings into light CRC- related genes at the RNA and protein levels that can potentially act as novel biomarkers opening doors to diagnostic and treatment purposes.

[A risk scoring model based on M2 macrophage-related genes for predicting prognosis of HBV-related hepatocellular carcinoma]

OBJECTIVE

To investigate the prognostic value of M2 macrophage-related genes (MRG) in hepatitis B virus (HBV)- related hepatocellular carcinoma (HCC).

METHODS

The transcriptome data of 73 patients with HBV-related HCC were obtained from TCGA database, and the MRG modules were identified by WGCNA. The MRG-based risk scoring model was constructed by LASSO regression analysis and validated using an external dataset. The correlation of the risk score with immune cell infiltration and drug sensitivity of HCC were analyzed with CIBERSORT and R. pRRophetic. The signaling pathways of the differential genes between the high- and low-risk groups were investigated using GSVA and GSEA enrichment analyses, and MRG expressions at the single cell level were validated using R.Seurat. The cell interaction intensity was analyzed by R.Cellchat to identify important cell types related to HCC progression. MRG expression levels were detected by RT-qPCR in THP-1 cells with HCC-conditioned medium-induced M2 polarization and in HBV-positive HCC cells.

RESULTS

A high M2 macrophage infiltration level was significantly correlated with a poor prognosis of HCC, and 5 hub MRG (VTN, GCLC, PARVB, TRIM27, and GMPR) were identified. The overall survival of HCC patients was significantly lower in the high-risk than in the low-risk group. The high- and the low-risk groups showed significant enrichment of M2 macrophages and na?ve B cells, respectively, and were sensitive to BI. 2536 and to AG. 014699, AKT. inhibitor. Ⅷ, AZD. 0530, AZD7762, and BMS. 708163, respectively. The proliferation-related and metabolism-related pathways were enriched in the high-risk group, where monocytes showed the most active cell interactions during HCC progression. VTN was significantly upregulated in HCC cell lines, while GCLC, PARVB, TRIM27, and GMPR were upregulated in M2 THP-1 cells.

CONCLUSION

The MRG-based risk scoring model can accurately predict the prognosis of HBV-related HCC and reveal the differences in tumor microenvironment to guide precision treatment of the patients.

Potential Immunohistochemical Biomarkers for Grading Oral Dysplasia: A Literature Review

Oral cancer is a prevalent global health issue, with significant morbidity and mortality rates. Despite available preventive measures, it remains one of the most common cancers, emphasising the need for improved diagnostic and prognostic tools. This review focuses on oral potentially malignant disorders (OPMDs), precursors to oral cancer, specifically emphasising oral epithelial dysplasia (OED). The World Health Organisation (WHO) provides a three-tier grading system for OED, and recent updates have expanded the criteria to enhance diagnostic precision. In the prognostic evaluation of OED, histological grading is presently regarded as the gold standard; however, its subjectivity and unreliability in anticipating malignant transformation or recurrence pose notable limitations. The primary objective is to investigate whether specific immunohistochemical biomarkers can enhance OED grading assessment according to the WHO classification. Biomarkers exhibit significant potential for comprehensive cancer risk evaluation, early detection, diagnosis, prognosis, and treatment optimisation. Technological advancements, including sequencing and nanotechnology, have expanded detection capabilities. Some analysed biomarkers are most frequently chosen, such as p53, Ki-67, cadherins/catenins, and other proteins used to differentiate OED grades. However, further research is needed to confirm these findings and discover new potential biomarkers for precise dysplasia grading and minimally invasive assessment of the risk of malignant transformation.

Advancements in Biotechnology and Stem Cell Therapies for Breast Cancer Patients

This comprehensive review article examines the integration of biotechnology and stem cell therapy in breast cancer diagnosis and treatment. It discusses the use of biotechnological tools such as liquid biopsies, genomic profiling, and imaging technologies for accurate diagnosis and monitoring of treatment response. Stem cell-based approaches, their role in modeling breast cancer progression, and their potential for breast reconstruction post-mastectomy are explored. The review highlights the importance of personalized treatment strategies that combine biotechnological tools and stem cell therapies. Ethical considerations, challenges in clinical translation, and regulatory frameworks are also addressed. The article concludes by emphasizing the potential of integrating biotechnology and stem cell therapy to improve breast cancer outcomes, highlighting the need for continued research and collaboration in this field.

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

BACKGROUND

Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without.

METHODS

The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression.

RESULTS

The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve.

CONCLUSION

These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.

N-of-one differential gene expression without control samples using a deep generative model

Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

Development of a Machine Learning-Based Prognostic Model for Hormone Receptor-Positive Breast Cancer Using Nine-Gene Expression Signature

Background

Determining the prognosis of hormone receptor positive (HR+) breast cancer (BC), which accounts for 80% of all BCs, is critical in improving survival outcomes. Stratifying individuals at high risk of BC-related mortality and improving prognosis has been the focus of research for over a decade. However, these tools are not universal as they are limited to clinical factors. We hypothesized that a new framework for predicting prognosis in HR+ BC patients can develop using artificial intelligence.

Methods

A total of 2,338 HR+ human epidermal growth factor receptor 2 negative (HER2-) BC cases were analyzed from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) cohorts. Groups were then divided into high- and low-risk categories utilizing a recurrence prediction model (RPM). An RPM was created by extracting nine prognosis-related genes from over 18,000 genes using a logistic progression model.

Results

Risk classification by RPM was significantly stratified in both the discovery cohort and validation cohort. In the time-dependent area under the curve analysis, there was some variation depending on the cohort, but accuracy was found to decline significantly after about 10 years. Cell cycle related gene sets, MYC, and PI3K-AKT-mTOR signaling were enriched in high-risk tumors by the Gene Set Enrichment Analysis. High-risk tumors were associated with high levels of immune cells from the lymphoid and myeloid lineage and immune cytolytic activity, as well as low levels of stem cells and stromal cells. High-risk tumors were also associated with poor therapeutic effects of chemotherapy and endocrine therapy.

Conclusions

This model was able to stratify prognosis in multiple cohorts. This is because the model reflects major BC therapeutic target pathways and tumor immune microenvironment and, further is supported by the therapeutic effect of chemotherapy and endocrine therapy.

Significance of tumor-infiltrating lymphocytes in tumor regression in breast cancer: A study in a tertiary care cancer center in South India

BACKGROUND

Tumor immunology plays a significant role in predicting tumor biology and how a tumor is going to respond to neoadjuvant chemotherapy (NACT). Tumor-infiltrating lymphocytes (TILs) are the easiest and by far the cheapest method of assessing tumor immunity. Many studies have suggested that TILs play an important role in tumor regression in breast cancer.

AIM

The aim of the current study was to determine significance of TILs in tumor regression in breast cancer.

MATERIALS AND METHODS

Patients with newly diagnosed and histologically proven breast cancer who were treated with both NACT and surgery in our institute were included in the study. TILs were assessed both before and after NACT, and were correlated with the relative amount of tumor regression and molecular subtypes based on the immunohistochemistry profile.

RESULTS

The study included 43 specimens of carcinoma breast in females. 42 cases were diagnosed with invasive carcinoma, no special type (NST), and one with lobular carcinoma. Pathological complete remission (pCR) was noted in 6 cases, partial remission (PRe) in 12 cases, and no response in 25 cases. TILs were noted before and after NACT in all cases and were correlated with other clinicopathological parameters.

CONCLUSION

The present study highlights that TILs play a vital role in tumor regression and can be included in routine reporting. It can provide an insight into tumor biology.

Liprin-α1 Expression in Tumor-Infiltrating Lymphocytes Associates with Improved Survival in Patients with HPV-Positive Oropharyngeal Squamous Cell Carcinoma

BACKGROUND

Liprin-α1 is a scaffold protein involved in cell adhesion, motility, and invasion in malignancies. Liprin-α1 inhibits the expression of metastatic suppressor CD82 in cancers such as oral carcinoma, and the expression of these proteins has been known to correlate negatively. The role of these proteins has not been previously studied in human papillomavirus (HPV)-related head and neck cancers. Our aim was to assess the clinical and prognostic role of liprin-α1 and CD82 in HPV-positive oropharyngeal squamous cell carcinoma (OPSCC) in comparison to HPV-negative OPSCC.

METHODS

The data included 139 OPSCC patients treated at the Helsinki University Hospital (HUS) during 2012-2016. Immunohistochemistry was utilized in HPV determination and in biomarker assays. Overall survival (OS) was used in the survival analysis.

RESULTS

Stronger expression of liprin-α1 in tumor-infiltrating lymphocytes (TILs) was linked to lower cancer stage (p < 0.001) and HPV positivity (p < 0.001). Additionally, we found an association between elevated expression of liprin-α1 and weak expression of CD82 in tumor cells (p = 0.029). In survival analysis, we found significant correlation between favorable OS and stronger expression of liprin-α1 in TILs among the whole patient cohort (p < 0.001) and among HPV-positive patients (p = 0.042).

CONCLUSIONS

Increased liprin-α1 expression in the TILs is associated with favorable prognosis in OPSCC, especially among HPV-positive patients.

Systematic review of comparative transcriptomic studies of cellular resistance to genotoxic stress

The development of resistance by tumor cells to various types of therapy is a significant problem that decreases the effectiveness of oncology treatments. For more than two decades, comparative transcriptomic studies of tumor cells with different sensitivities to ionizing radiation and chemotherapeutic agents have been conducted in order to identify the causes and mechanisms underlying this phenomenon. However, the results of such studies have little in common and often contradict each other. We have assumed that a systematic analysis of a large number of such studies will provide new knowledge about the mechanisms of development of therapeutic resistance in tumor cells. Our comparison of 123 differentially expressed gene (DEG) lists published in 98 papers suggests a very low degree of consistency between the study results. Grouping the data by type of genotoxic agent and tumor type did not increase the similarity. The most frequently overexpressed genes were found to be those encoding the transport protein ABCB1 and the antiviral defense protein IFITM1. We put forward a hypothesis that the role played by the overexpression of the latter in the development of resistance may be associated not only with the stimulation of proliferation, but also with the limitation of exosomal communication and, as a result, with a decrease in the bystander effect. Among down regulated DEGs, BNIP3 was observed most frequently. The expression of BNIP3, together with BNIP3L, is often suppressed in cells resistant to non-platinum genotoxic chemotherapeutic agents, whereas it is increased in cells resistant to ionizing radiation. These observations are likely to be mediated by the binary effects of these gene products on survival, and regulation of apoptosis and autophagy. The combined data also show that even such obvious mechanisms as inhibition of apoptosis and increase of proliferation are not universal but show multidirectional changes.

Combining the amplification refractory mutation system and high-resolution melting analysis for KRAS mutation detection in clinical samples

The success of personalized medicine depends on the discovery of biomarkers that allow oncologists to identify patients that will benefit from a particular targeted drug. Molecular tests are mostly performed using tumor samples, which may not be representative of the tumor's temporal and spatial heterogeneity. Liquid biopsies, and particularly the analysis of circulating tumor DNA, are emerging as an interesting means for diagnosis, prognosis, and predictive biomarker discovery. In this study, the amplification refractory mutation system (ARMS) coupled with high-resolution melting analysis (HRMA) was developed for detecting two of the most relevant KRAS mutations in codon 12. After optimization with commercial cancer cell lines, KRAS mutation screening was validated in tumor and plasma samples collected from patients with pancreatic ductal adenocarcinoma (PDAC), and the results were compared to those obtained by Sanger sequencing (SS) and droplet digital polymerase chain reaction (ddPCR). The developed ARMS-HRMA methodology stands out for its simplicity and reduced time to result when compared to both SS and ddPCR but showing high sensitivity and specificity for the detection of mutations in tumor and plasma samples. In fact, ARMS-HRMA scored 3 more mutations compared to SS (tumor samples T6, T7, and T12) and one more compared to ddPCR (tumor sample T7) in DNA extracted from tumors. For ctDNA from plasma samples, insufficient genetic material prevented the screening of all samples. Still, ARMS-HRMA allowed for scoring more mutations in comparison to SS and 1 more mutation in comparison to ddPCR (plasma sample P7). We propose that ARMS-HRMA might be used as a sensitive, specific, and simple method for the screening of low-level mutations in liquid biopsies, suitable for improving diagnosis and prognosis schemes.

Advances in point-of-care genetic testing for personalized medicine applications

Breakthroughs within the fields of genomics and bioinformatics have enabled the identification of numerous genetic biomarkers that reflect an individual's disease susceptibility, disease progression, and therapy responsiveness. The personalized medicine paradigm capitalizes on these breakthroughs by utilizing an individual's genetic profile to guide treatment selection, dosing, and preventative care. However, integration of personalized medicine into routine clinical practice has been limited-in part-by a dearth of widely deployable, timely, and cost-effective genetic analysis tools. Fortunately, the last several decades have been characterized by tremendous progress with respect to the development of molecular point-of-care tests (POCTs). Advances in microfluidic technologies, accompanied by improvements and innovations in amplification methods, have opened new doors to health monitoring at the point-of-care. While many of these technologies were developed with rapid infectious disease diagnostics in mind, they are well-suited for deployment as genetic testing platforms for personalized medicine applications. In the coming years, we expect that these innovations in molecular POCT technology will play a critical role in enabling widespread adoption of personalized medicine methods. In this work, we review the current and emerging generations of point-of-care molecular testing platforms and assess their applicability toward accelerating the personalized medicine paradigm.

Identification of New Key Genes and Their Association with Breast Cancer Occurrence and Poor Survival Using In Silico and In Vitro Methods

Breast cancer is one of the most prevalent types of cancer diagnosed globally and continues to have a significant impact on the global number of cancer deaths. Despite all efforts of epidemiological and experimental research, therapeutic concepts in cancer are still unsatisfactory. Gene expression datasets are widely used to discover the new biomarkers and molecular therapeutic targets in diseases. In the present study, we analyzed four datasets using R packages with accession number GSE29044, GSE42568, GSE89116, and GSE109169 retrieved from NCBI-GEO and differential expressed genes (DEGs) were identified. Protein-protein interaction (PPI) network was constructed to screen the key genes. Subsequently, the GO function and KEGG pathways were analyzed to determine the biological function of key genes. Expression profile of key genes was validated in MCF-7 and MDA-MB-231 human breast cancer cell lines using qRT-PCR. Overall expression level and stage wise expression pattern of key genes was determined by GEPIA. The bc-GenExMiner was used to compare expression level of genes among groups of patients with respect to age factor. OncoLnc was used to analyze the effect of expression levels of LAMA2, TIMP4, and TMTC1 on the survival of breast cancer patients. We identified nine key genes, of which COL11A1, MMP11, and COL10A1 were found up-regulated and PCOLCE2, LAMA2, TMTC1, ADAMTS5, TIMP4, and RSPO3 were found down-regulated. Similar expression pattern of seven among nine genes (except ADAMTS5 and RSPO3) was observed in MCF-7 and MDA-MB-231 cells. Further, we found that LAMA2, TMTC1, and TIMP4 were significantly expressed among different age groups of patients. LAMA2 and TIMP4 were found significantly associated and TMTC1 was found less correlated with breast cancer occurrence. We found that the expression level of LAMA2, TIMP4, and TMTC1 was abnormal in all TCGA tumors and significantly associated with poor survival.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes

This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (¹⁸F-FDG PET) image features of NSCLC patients. RNA-sequencing data and ¹⁸F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein-protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells

The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicated regulators were identified in the TRN and with multiple alignments of the TFs' target gene sequences. Six transcription factors (E2F3, FHL2, ETS1, KAT6B, TWIST1, and RUNX2) were identified in the GRN as essential regulators of gene expression in non-small-cell lung cancer (NSCLC) and related to the lung tumoral process. Our findings indicate that RUNX2 could be an important regulator of the lung cancer GRN through the formation of coregulatory complexes with other TFs related to the establishment and progression of lung cancer. Therefore, RUNX2 could become an essential biomarker for developing diagnostic tools and specific treatments against tumoral diseases in the lung after the experimental validation of its regulatory function.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

The Novel Diagnostic Techniques and Biomarkers of Canine Mammary Tumors

Canine mammary tumors (CMTs) are considered a serious clinical problem in older bitches. Due to the high malignancy rate and poor prognosis, an early diagnosis is essential. This article is a summary of novel diagnostic techniques as well as the main biomarkers of CMTs. So far, CMTs are detected only when changes in mammary glands are clinically visible and surgical removal of the mass is the only recommended treatment. Proper diagnostics of CMT is especially important as they represent a very diverse group of tumors and therefore different treatment approaches may be required. Recently, new diagnostic options appeared, like a new cytological grading system of CMTs or B-mode ultrasound, the Doppler technique, contrast-enhanced ultrasound, and real-time elastography, which may be useful in pre-surgical evaluation. However, in order to detect malignancies before macroscopic changes are visible, evaluation of serum and tissue biomarkers should be considered. Among them, we distinguish markers of the cell cycle, proliferation, apoptosis, metastatic potential and prognosis, hormone receptors, inflammatory and more recent: metabolomic, gene expression, miRNA, and transcriptome sequencing markers. The use of a couple of the above-mentioned markers together seems to be the most useful for the early diagnosis of neoplastic diseases as well as to evaluate response to treatment, presence of tumor progression, or further prognosis. Molecular aspects of tumors seem to be crucial for proper understanding of tumorigenesis and the application of individual treatment options.

Mass Spectrometry-Based Chemical Proteomics for Drug Target Discoveries

Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.

High mRNA Expression Levels of Heat Shock Protein Family B Member 2 (HSPB2) Are Associated with Breast Cancer Patients’ Relapse and Poor Survival

Small heat shock proteins (sHSPs) are ubiquitous ATP-independent chaperones that contribute to the maintenance of proteome integrity and functionality. Recent evidence suggests that sHSPs are ubiquitously expressed in numerous types of tumors and have been proposed to be implicated in oncogenesis and malignant progression. Heat shock protein family B member 2 (HSPB2) is a member of the sHSPs, which is found to be expressed, among others, in human breast cancer cell lines and constitutes an inhibitor of apical caspase activation in the extrinsic apoptotic pathway. In this study, we investigated the potential prognostic significance of HSPB2 mRNA expression levels in breast cancer, which represents the most frequent malignancy in females and one of the three most common cancer types worldwide. To this end, malignant breast tumors along with paired non-cancerous breast tissue specimens were used. HSPB2 expression levels were quantified in these two cohorts using a sensitive and accurate SYBR green-based quantitative real-time polymerase chain reaction (q-RT-PCR). Extensive biostatistical analyses were performed including Kaplan–Meier and Cox regression survival analyses for the assessment of the results. The significant downregulation of HSPB2 gene expression was revealed in breast tumors compared to their adjacent non-cancerous breast tissues. Notably, high HSPB2 mRNA expression predicts poor disease-free survival and overall survival of breast cancer patients. Multivariate Cox regression analysis revealed that HSPB2 mRNA overexpression is a significant predictor of poor prognosis in breast cancer, independent of other clinicopathological factors. In conclusion, high HSPB2 mRNA expression levels are associated with breast cancer patients’ relapse and poor survival.

DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer

BACKGROUND

Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements.

RESULTS

In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges.

CONCLUSIONS

Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM .

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Potential Biomarkers of Skin Melanoma Resistance to Targeted Therapy—Present State and Perspectives

Simple Summary

Around 5–10% of advanced melanoma patients progress early on anti-BRAF targeted therapy and 20–30% respond only with the stabilization of the disease. Presumably, these patients could benefit more from first-line immunotherapy. Resistance to BRAF/MEK inhibitors is generated by genetic and non-genetic factors inherent to a tumor or acquired during therapy. Some of them are well documented as a cause of treatment failure. They are potential predictive markers that could improve patients’ selection for both standard and also alternative therapy as some of them have therapeutic potential. Here, a summary of the most promising predictive and therapeutic targets is presented. This up-to-date knowledge may be useful for further study on implementing more accurate genetic/molecular tests in melanoma treatment.

Abstract

Melanoma is the most aggressive skin cancer, the number of which is increasing worldwide every year. It is completely curable in its early stage and fatal when spread to distant organs. In addition to new therapeutic strategies, biomarkers are an important element in the successful fight against this cancer. At present, biomarkers are mainly used in diagnostics. Some biological indicators also allow the estimation of the patient’s prognosis. Still, predictive markers are underrepresented in clinics. Currently, the only such indicator is the presence of the V600E mutation in the BRAF gene in cancer cells, which qualifies the patient for therapy with inhibitors of the MAPK pathway. The identification of response markers is particularly important given primary and acquired resistance to targeted therapies. Reliable predictive tests would enable the selection of patients who would have the best chance of benefiting from treatment. Here, up-to-date knowledge about the most promising genetic and non-genetic resistance-related factors is described. These are alterations in MAPK, PI3K/AKT, and RB signaling pathways, e.g., due to mutations in NRAS, RAC1, MAP2K1, MAP2K2, and NF1, but also other changes activating these pathways, such as the overexpression of HGF or EGFR. Most of them are also potential therapeutic targets and this issue is also addressed here.

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Background

Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment.

Methods

Gene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC).

Results

Consensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses.

Conclusions

The molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03393-9.

Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

Genome- and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution, but its characteristics and implication in cancer evolution and therapy remain largely unexplored. Here, we analyzed large-scale transcriptome/proteome profiles, such as The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and found that compared to normal tissues, different cancer types showed a convergent pattern toward using biosynthetically low-cost amino acids. Such a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids, termed energy cost per amino acid (ECPA). With this index, we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development, and revealed their reversed patterns. Finally, focusing on the liver, a tissue with a dramatic increase in ECPA during development, we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single genes. Our study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

Summary

Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes (WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31) for IBC and eight genes (NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM) for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base.

Availability and implementation

www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git.

Supplementary information

Supplementary data are available at https://github.com/skr1/Imagene.git.

Vincristine-Induced Peripheral Neuropathy in Childhood Acute Lymphoblastic Leukemia: Genetic Variation as a Potential Risk Factor

Vincristine (VCR) is the first-line chemotherapeutic medication often co-administered with other drugs to treat childhood acute lymphoblastic leukemia. Dose-dependent neurotoxicity is the main factor restricting VCR’s clinical application. VCR-induced peripheral neuropathy (VIPN) sometimes results in dose reduction or omission, leading to clinical complications or affecting the patient’s quality of life. With regard to the genetic basis of drug responses, preemptive pharmacogenomic testing and simultaneous blood level monitoring could be helpful for the transformation of various findings into individualized therapies. In this review, we discussed the potential associations between genetic variants in genes contributing to the pharmacokinetics/pharmacodynamics of VCR and VIPN incidence and severity in patients with acute lymphoblastic leukemia. Of note, genetic variants in the CEP72 gene have great potential to be translated into clinical practice. Such a genetic biomarker may help clinicians diagnose VIPN earlier. Besides, genetic variants in other genes, such as CYP3A5, ABCB1, ABCC1, ABCC2, TTPA, ACTG1, CAPG, SYNE2, SLC5A7, COCH, and MRPL47, have been reported to be associated with the VIPN, but more evidence is needed to validate the findings in the future. In fact, a variety of complex factors jointly determine the VIPN. In implementing precision medicine, the combination of genetic, environmental, and personal variables, along with therapeutic drug monitoring, will allow for a better understanding of the mechanisms of VIPN, improving the effectiveness of VCR treatment, reducing adverse reactions, and improving patients’ quality of life.

Conjoint analysis of circulating tumor cells and solid tumors for exploring potential prognostic markers and constructing a robust novel predictive signature for breast cancer

Background

Distance metastasis is the leading cause of death for breast cancer patients, and circulating tumor cells (CTCs) play a key role in cancer metastasis. There have been few studies on CTCs at the molecular level due to their rarity, and the heterogeneity of CTCs may provide special information for solid tumor analysis.

Methods

In this study, we used the gene expression and clinical information of single-cell RNA-seq data of CTCs of breast cancer and discovered a cluster of epithelial cells that had more aggressive characteristics. The differentially expressed genes (DEGs) between the identified epithelial cells cluster and others from single-CTCs were selected for further analysis in bulk sequence data of solid breast cancers.

Results

Eighteen genes closely related to the specific CTC epithelial phenotype and breast cancer patient prognosis were identified. Among these 18 genes, we selected the GARS gene, which has not been studied in breast cancer, for functional research and confirmed that it may be a potential oncogene in breast cancer. A risk score was established by the 18 genes, and a high-risk score was strongly associated with a high metastasis rate and poor survival prognosis in breast cancer. The high-risk score group was related to a defective immune infiltration environment in breast cancer, and the immune checkpoint therapy response rate was lower in this group. The drug-sensitive analysis shows that the high-risk score patients may be more sensitive to AKT-mTOR and the cyclin-dependent kinase (CDK) pathways drugs than low-risk score patients.

Conclusions

Our 18-gene risk score shows good prognostic and predictive values and might be a personalized prognostic marker or therapy guide marker in breast cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02415-8.

Quiescin Sulfhydryl Oxidase 2 Overexpression Predicts Poor Prognosis and Tumor Progression in Patients With Colorectal Cancer: A Study Based on Data Mining and Clinical Verification

Background: As a member of the atypical thiol oxidase family, quiescin sulfhydryl oxidase 2 (QSOX2) has been reported to play an important role in several biological processes, but the expression and function of QSOX2 in colorectal cancer (CRC) remains elusive. Methods: The difference of QSOX2 expression, and its relationship with clinicopathological features and prognosis in CRC, was analyzed by bioinformatic analysis and validated by clinical CRC specimen cohort. The functional characterization of QSOX2 was detected via in vitro and vivo experiments in CRC cell lines, while the potential signaling pathways were predicted by Gene Set Enrichment Analysis (GSEA). Results: Our data based on bioinformatical analysis and clinical validation demonstrated that the expression of QSOX2 in CRC tissues was significantly upregulated. Additionally, the chi-square test, logistic regression analysis, and Fisher's exact test showed that QSOX2 overexpression was significantly correlated with advanced clinicopathological parameters, such as pathological stage and lymph node metastasis. The Kaplan-Meier curves and univariate Cox regression model showed that QSOX2 overexpression predicts poor overall survival (OS) and disease-free survival (DFS) in CRC patients. More importantly, multivariate Cox regression model showed that QSOX2 overexpression could serve as an independent factor for CRC patients. In vitro and vivo data showed that the proliferation and metastasis ability of CRC cells were suppressed on condition of QSOX2 inhibition. In addition, GSEA showed that the QSOX2 high expression phenotype has enriched multiple potential cancer-related signaling pathways. Conclusion: QSOX2 overexpression is strongly associated with malignant progression and poor oncological outcomes in CRC. QSOX2 might act as a novel biomarker for prognosis prediction and a new target for biotherapy in CRC.

OncoDB: an interactive online database for analysis of gene expression and viral infection in cancer

Large-scale multi-omics datasets, most prominently from the TCGA consortium, have been made available to the public for systematic characterization of human cancers. However, to date, there is a lack of corresponding online resources to utilize these valuable data to study gene expression dysregulation and viral infection, two major causes for cancer development and progression. To address these unmet needs, we established OncoDB, an online database resource to explore abnormal patterns in gene expression as well as viral infection that are correlated to clinical features in cancer. Specifically, OncoDB integrated RNA-seq, DNA methylation, and related clinical data from over 10 000 cancer patients in the TCGA study as well as from normal tissues in the GTEx study. Another unique aspect of OncoDB is its focus on oncoviruses. By mining TCGA RNA-seq data, we have identified six major oncoviruses across cancer types and further correlated viral infection to changes in host gene expression and clinical outcomes. All the analysis results are integratively presented in OncoDB with a flexible web interface to search for data related to RNA expression, DNA methylation, viral infection, and clinical features of the cancer patients. OncoDB is freely accessible at http://oncodb.org.

Comprehensive Combined Proteomics and Genomics Analysis Identifies Prognostic Related Transcription Factors in Breast Cancer and Explores the Role of DMAP1 in Breast Cancer

Transcription factors (TFs) are important for regulating gene transcription and are the hallmark of many cancers. The identification of breast cancer TFs will help in developing new diagnostic and individualized cancer treatment tools. In this study, we used quantitative proteomic analyses of nuclear proteins and massive transcriptome data to identify enriched potential TFs and explore the possible role of the transcription factor DMAP1 in breast cancer. We identified 13 prognostic-related TFs and constructed their regulated genes, alternative splicing (AS) events, and splicing factor (SF) regulation networks. DMAP1 was reported less in breast cancer. The expression of DMAP1 decreased in breast cancer tumors compared with normal tissues. The poor prognosis of patients with low DMAP1 expression may relate to the activated PI3K/Akt signaling pathway, as well as other cancer-relevant pathways. This may be due to the low methylation and high expression of these pathway genes and the fact that such patients show more sensitivity to some PI3K/Akt signaling pathway inhibitors. The high expression of DMAP1 was correlated with low immune cell infiltration, and the response to immune checkpoint inhibitor treatment in patients with high DMAP1 expression was low. Our study identifies some transcription factors that are significant for breast cancer progression, which can be used as potential personalized prognostic markers in the future.

Gene expression signatures predict response to therapy with growth hormone

Recombinant human growth hormone (r-hGH) is used as a therapeutic agent for disorders of growth including growth hormone deficiency (GHD) and Turner syndrome (TS). Treatment is costly and current methods to model response are inexact. GHD (n = 71) and TS patients (n = 43) were recruited to study response to r-hGH over 5 years. Analysis was performed using 1219 genetic markers and baseline (pre-treatment) blood transcriptome. Random forest was used to determine predictive value of transcriptomic data associated with growth response. No genetic marker passed the stringency criteria for prediction. However, we identified an identical set of genes in both GHD and TS whose expression could be used to classify therapeutic response to r-hGH with a high accuracy (AUC > 0.9). Combining transcriptomic markers with clinical phenotype was shown to significantly reduce predictive error. This work could be translated into a single genomic test linked to a prediction algorithm to improve clinical management. Trial registration numbers: NCT00256126 and NCT00699855.

Inclusion of double helix structural oligonucleotide (STexS) results in an enhance of SNP specificity in PCR

Genetic mutations such as single nucleotide polymorphisms (SNP) are known as one of the most common forms which related to various genetic disorders and cancers. Among of the methods developed for efficient detection of such SNP, polymerase chain reaction (PCR) methods are widely used worldwide for its cost and viable advantages. However, the technique to discriminate small amounts of SNP mixed in abundant normal DNA is incomplete due to intrinsic technical problems of PCR such as amplification occurring even in 3’mismatched cases because of high enzyme activity of DNA polymerases. To overcome the issue, specifically designed PCR platform, STexS (SNP typing with excellent specificity) using double stranded oligonucleotides was implemented as a means to emphasize the amplification of SNP templates by decreasing unwanted amplification of 3’mismatched DNA copies. In this study, the results indicate several EGFR mutations were easily detected specifically utilizing the STexS platform. Further trials show the novel method works effectively to discriminate mutations in not only general allele specific (AS)-PCRs, but also amplification refractory mutation system (ARMS)-PCR. The STexS platform will give aid in PCRs targeting potential SNPs or genetically mutated biomarkers in human clinical samples.

Genomic and transcriptomic analyses reveal a tandem amplification unit of 11 genes and mutations in mismatch repair genes in methotrexate-resistant HT-29 cells

DHFR gene amplification is commonly present in methotrexate (MTX)-resistant colon cancer cells and acute lymphoblastic leukemia. In this study, we proposed an integrative framework to characterize the amplified region by using a combination of single-molecule real-time sequencing, next-generation optical mapping, and chromosome conformation capture (Hi-C). We identified an amplification unit spanning 11 genes, from the DHFR gene to the ATP6AP1L gene position, with high adjusted interaction frequencies on chromosome 5 (~2.2 Mbp) and a twenty-fold tandemly amplified region, and novel inversions at the start and end positions of the amplified region as well as frameshift insertions in most of the MSH and MLH genes were detected. These mutations might stimulate chromosomal breakage and cause the dysregulation of mismatch repair. Characterizing the tandem gene-amplified unit may be critical for identifying the mechanisms that trigger genomic rearrangements. These findings may provide new insight into the mechanisms underlying the amplification process and the evolution of drug resistance.

Sequencing a large region of DNA containing many surplus copies of genes linked to drug resistance in colon cancer cells may illuminate how these genomic rearrangements arise. Such regions of gene amplification are highly repetitive, making them impossible to sequence using ordinary methods, and little is known about how they are generated. Using advanced methods, Jeong-Sun Seo at Seoul National University Bundang Hospital in South Korea and co-workers sequenced a region of gene amplification in colon cancer cells. The amplified region was approximately 20 times the length of that in healthy cells and contained many copies of an eleven-gene segment, including a gene implicated in drug resistance. The region also contained mutations in chromosomal repair genes which would disrupt repair pathways. These results illuminate the genetic changes that lead to gene amplification and drug resistance in cancer cells.

Application of Thermodynamics and Protein–Protein Interaction Network Topology for Discovery of Potential New Treatments for Temporal Lobe Epilepsy

In this paper, we propose a bioinformatics-based method, which introduces thermodynamic measures and topological characteristics aimed to identify potential drug targets for pharmaco-resistant epileptic patients. We apply the Gibbs homology analysis to the protein–protein interaction network characteristic of temporal lobe epilepsy. With the identification of key proteins involved in the disease, particularly a number of ribosomal proteins, an assessment of their inhibitors is the next logical step. The results of our work offer a direction for future development of prospective therapeutic solutions for epilepsy patients, especially those who are not responding to the current standard of care.

Association of PARK-2 Non-synonyms Polymorphisms and Their In Silico Validation Among North Indian Colorectal Cancer Patients

PURPOSE

PARK2 is a potential tumour suppressor gene and its genetic alterations (regionic loss) are common across many human cancers. The association of PARK2 germline variations (SNPs) with Parkinson's has been shown, but their association in development and progression of cancer remains elusive. The aim of this study was to identify association of PARK2 polymorphisms (rs1801474, rs1801334) with colorectal cancer in a case control study design.

METHODS

This case control study included a total of 650 genetically unrelated subjects comprising 300 colorectal cancer cases and 350 healthy controls belonging to North Indian. Both SNPs were analyzed using the PCR-RFLP assay. Statistical analysis for describing risk and association was performed using SPSS-17 software. Structural deviations due to non- synonymous substitutions (S167N and D394N) were analyzed using MD simulations.

RESULTS

The genotype distributions of both the SNPs were in Hardy-Weinberg equilibrium. For both the polymorphisms, the allelic model showed statistically significant risk with OR ~ 1.3. Many of the associations remained significant even after Bonferroni correction (P < 0.00125). The result suggested that both S167N and D394N were deviated from wild type and structures and were stable after 5 ns. The average value of RMSD for backbone atoms was calculated from 5 to 10 ns molecular dynamics simulation data.

CONCLUSION

In conclusion, our study revealed a significant association of PARK2 SNPs with colorectal cancer as well as their relations with other clinical parameters highlighting their contribution towards colorectal cancer susceptibility in North Indian population.

Three-Dimensional Printing for Cancer Applications: Research Landscape and Technologies

As a variety of novel technologies, 3D printing has been considerably applied in the field of health care, including cancer treatment. With its fast prototyping nature, 3D printing could transform basic oncology discoveries to clinical use quickly, speed up and even revolutionise the whole drug discovery and development process. This literature review provides insight into the up-to-date applications of 3D printing on cancer research and treatment, from fundamental research and drug discovery to drug development and clinical applications. These include 3D printing of anticancer pharmaceutics, 3D-bioprinted cancer cell models and customised nonbiological medical devices. Finally, the challenges of 3D printing for cancer applications are elaborated, and the future of 3D-printed medical applications is envisioned.

Predictive Value of EGFR-PI3K-AKT-mTOR-Pathway Inhibitor Biomarkers for Head and Neck Squamous Cell Carcinoma: A Systematic Review

BACKGROUND

Understanding molecular pathogenesis of head and neck squamous cell carcinomas (HNSCC) has considerably improved in the last decades. As a result, novel therapeutic strategies have evolved, amongst which are epidermal growth factor receptor (EGFR)-targeted therapies. With the exception of cetuximab, targeted therapies for HNSCC have not yet been introduced into clinical practice. One important aspect of new treatment regimes in clinical practice is presence of robust biomarkers predictive for therapy response.

METHODS

We performed a systematic search in PubMed, Embase and the Cochrane library. Articles were included if they investigated a biomarker for targeted therapy in the EGFR-PI3K-AKT-mTOR-pathway.

RESULTS

Of 83 included articles, 52 were preclinical and 33 were clinical studies (two studies contained both a preclinical and a clinical part). We classified EGFR pathway inhibitor types and investigated the type of biomarker (biomarker on epigenetic, DNA, mRNA or protein level).

CONCLUSION

Several EGFR-PI3K-AKT-mTOR-pathway inhibitor biomarkers have been researched for HNSCC but few of the investigated biomarkers have been adequately confirmed in clinical trials. A more systematic approach is needed to discover proper biomarkers as stratifying patients is essential to prevent unnecessary costs and side effects.

Crosstalk between MMP-13, CD44, and TWIST1 and its role in regulation of EMT in patients with esophageal squamous cell carcinoma

Matrix metalloproteinases (MMPs) play key roles in epithelial-mesenchymal transition (EMT) for the development of cancer cell invasion and metastasis. MMP-13 is an extracellular matrix (ECM)-degrading enzyme that plays crucial roles in angiogenesis, cell cycle regulation, niche maintenance, and transforming squamous epithelial cells in various tissues. CD44, a transmembrane glycoprotein expressed on esophageal tumor cells, is required for EMT induction and invasion in esophageal squamous cell carcinoma (ESCC). The transcription factor TWIST1, as EMT and stemness marker, regulates MMPs expression and is identified as the downstream target of CD44. In this study, we aimed to investigate the probable interplay between the expression of key genes contributing to ESCC development, including MMP-13, TWIST1, and CD44 with clinical features for introducing novel diagnostic and therapeutic targets in the disease. The gene expression profiling of MMP-13, TWIST1, and CD44 was performed using quantitative real-time PCR in tumor tissues from 50 ESCC patients compared to corresponding margin non-tumoral tissues. Significant overexpression of MMP-13, CD44S, CD44V3, CD44V6, and TWIST1 were observed in 74%, 36%, 44%, 44%, and 52% of ESCC tumor samples, respectively. Overexpression of MMP-13 was associated with stage of tumor progression, metastasis, and tumor location (P < 0.05). There was a significant correlation between TWIST1 overexpression and grade (P < 0.05). Furthermore, overexpression of CD44 variants was associated with stage of tumor progression, grade, tumor invasion, and location (P < 0.05). The results indicated the significant correlation between concomitant expression of MMP-13/TWIST1, TWIST1/CD44, and CD44/MMP-13 with each other in a variety of clinicopathological traits, including depth of tumor invasion, tumor location, stage of tumor, and lymph node involvement in ESCC tissue samples (P < 0.05). Collectively, our results indicate that the TWIST1-CD44-MMP-13 axis is involved in tumor aggressiveness, proposing these genes as regulators of EMT, diagnostic markers, and therapeutic targets in ESCC.

Tie-2, G-CSF, and Leptin as Promising Diagnostic Biomarkers for Endometrial Cancer: A Pilot Study

Preoperative determination of the extent of endometrial cancer (EC) would avoid the complications associated with radical surgery. Screening of patients' plasma biomarkers might enable a more precise diagnosis of EC and a tailored treatment approach. This prospective case-control monocentric pilot study included 76 postmenopausal women (38 endometrioid EC patients and 38 control patients with benign gynecological conditions), and 37 angiogenic factors (AFs) were investigated as potential biomarkers for EC. AF concentrations in preoperative plasma samples were measured using Luminex xMAP™ multiplexing technology. The plasma levels of sTie-2 and G-CSF were significantly lower in EC compared to control patients, whereas the plasma levels of leptin were significantly higher in EC patients. Neuropilin-1 plasma levels were significantly higher in patients with type 2 EC (grade 3) compared to patients with lower grade cancer or controls. Follistatin levels were significantly higher in patients with lymphovascular invasion, and IL-8 plasma levels were significantly higher in patients with metastases. If validated, the plasma concentrations of the indicated AFs could represent an important additional diagnostic tool for the early detection and characterization of EC. This could guide the decision-making on the extent of surgery. Further studies with larger patient numbers are currently ongoing.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.