Electrochemical biosensors for early detection of breast cancer.

Breast cancer continues to be a significant contributor to global cancer deaths, particularly among women. This highlights the critical role of early detection and treatment in boosting survival rates. While conventional diagnostic methods like mammograms, biopsies, ultrasounds, and MRIs are valuable tools, limitations exist in terms of cost, invasiveness, and the requirement for specialized equipment and trained personnel. Recent shifts towards biosensor technologies offer a promising alternative for monitoring biological processes and providing accurate health diagnostics in a cost-effective, non-invasive manner. These biosensors are particularly advantageous for early detection of primary tumors, metastases, and recurrent diseases, contributing to more effective breast cancer management. The integration of biosensor technology into medical devices has led to the development of low-cost, adaptable, and efficient diagnostic tools. In this framework, electrochemical screening platforms have garnered significant attention due to their selectivity, affordability, and ease of result interpretation. The current review discusses various breast cancer biomarkers and the potential of electrochemical biosensors to revolutionize early cancer detection, making provision for new diagnostic platforms and personalized healthcare solutions.

Circular RNAs in laryngeal cancer.

Laryngeal cancer remains a significant global health concern, with poor prognosis for advanced-stage disease highlighting the need for novel diagnostic, prognostic, and therapeutic approaches. Circular RNAs (circRNAs), a class of covalently closed non-coding RNAs, have emerged as important regulators of gene expression and cellular processes in various cancers, including laryngeal cancer. This review summarizes the current understanding of circRNAs in laryngeal cancer, covering their biogenesis, regulatory mechanisms, and potential clinical applications. We explore the diverse functions of circRNAs, including their roles as miRNA sponges, protein interactors, and direct mRNA regulators, and their influence on key cellular processes such as proliferation, invasion, and metastasis. The review highlights promising circRNAs as diagnostic and prognostic biomarkers, as well as potential therapeutic targets. We also outline current strategies for circRNA modulation, including suppression techniques like RNA interference and CRISPR/Cas systems, and overexpression methods using vectors and synthetic circRNAs.

Exploring the interplay between bisphenol A exposure, the immune microenvironment and hepatocellular carcinoma progression.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a formidable challenge in oncology, with its pathogenesis and progression influenced by myriad factors. Among them, the pervasive organic synthetic compound, bisphenol A (BPA), previously linked with various adverse health effects, has been speculated to play a role. This study endeavors to elucidate the complex interplay between BPA, the immune microenvironment of HCC, and the broader molecular landscape of this malignancy. METHODS: A comprehensive analysis was undertaken using data procured from both The Cancer Genome Atlas and the Comparative Toxicogenomics Database. Rigorous differential expression analyses were executed, supplemented by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. In addition, single-sample gene set enrichment analysis, gene set enrichment analysis and gene set variation analysis were employed to reveal potential molecular links and insights. Immune infiltration patterns were delineated, and a series of in vitro experiments on HCC cells were conducted to directly assess the impact of BPA exposure. RESULTS: Our findings unveiled a diverse array of active immune cells and functions within HCC. Distinct correlations emerged between high-immune-related scores, established markers of the tumor microenvironment and the expression of immune checkpoint genes. A significant discovery was the identification of key genes simultaneously associated with immune-related pathways and BPA exposure. Leveraging these genes, a prognostic model was crafted, offering predictive insights into HCC patient outcomes. Intriguingly, in vitro studies suggested that BPA exposure could promote proliferation in HCC cells. CONCLUSION: This research underscores the multifaceted nature of HCC's immune microenvironment and sheds light on BPA's potential modulatory effects therein. The constructed prognostic model, if validated further, could serve as a robust tool for risk stratification in HCC, potentially guiding therapeutic strategies. Furthermore, the implications of the findings for immunotherapy are profound, suggesting new avenues for enhancing treatment efficacy. As the battle against HCC continues, understanding of environmental modulators like BPA becomes increasingly pivotal.

Single-cell RNA sequencing reveals microenvironmental infiltration in non-small cell lung cancer with different responses to immunotherapy.

BACKGROUND: Immunotherapy represents a groundbreaking and monumental achievement in the field of cancer therapy, marking a significant advancement in fighting against this devastating disease. Lung cancer has showed consistent clinical improvements in response to immunotherapy treatments, yet, it is undeniable that challenges such as limited response rates acquire resistance, and the unclear fundamental mechanisms were inevitable problems. METHODS: The cellular composition was defined and distinguished through single-cell RNA sequencing (scRNA-seq) analysis of MPR (major pathologic response) and NMPR (non-major pathologic response) samples in GSE207422, including four primary MPR samples and eight primary NMPR samples. RESULTS: We found obvious difference in CD8+ T cell population between MPR and NMPR samples, with high expression of TYMS, RRM2, and BIRC5 in NPMR samples. Meanwhile, the proportion of macrophages and tumor epithelial cells infiltration increased in the NMPR samples. We discovered biomarkers (ACTN4, ATF3, BRD2, CDKN1A, and CHMP4B) in epithelial cells which were potentially represented worse outcomes. CONCLUSIONS: By exploring the difference of tumor microenvironment (TME) in samples with different corresponding degrees of neoadjuvant immunotherapy, this research introduces a number of novel biomarkers for predicting the response of treatment and a theoretical basis for overcoming immunotherapy resistance.

The distribution of the extrachromosomal DNA molecules in early lung cancer.

Lung cancer (LC) is a highly lethal cancer worldwide. Research on the distribution and nature of extrachromosomal DNA molecules (EcDNAm) in early LC is scarce. In this study, after removing linear DNA and mitochondrial circular DNA, EcDNAm were extracted from two paired LC tissue samples and amplified using rolling circle amplification. High throughput extrachromosomal DNA (EcDNA) or RNA sequencing and bioinformatics analysis were subsequently utilized to explore the distribution and nature of the EcDNAm. Additionally, to elucidate the role of oncogenes with large EcDNAm sizes, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed. The RNA sequencing results revealed significant differences in certain genes between tumors and corresponding normal samples. At the same time, slight distinctions were observed between relapsed and non-relapsed tumor samples. The nature of the EcDNAm was compared between LC samples and matched normal samples. There was a tendency for the number of EcDNAm with longer size (EcDNA) and its containing driver oncogenes to be higher in cancer samples. Enrichment analysis of the cancer samples revealed enrichment in biological processes, such as positive regulation of protein localization, axon development, and in-utero embryonic development. This study highlights the universal distribution and characteristics of EcDNAm in early LC. Moreover, our work fills the investigation of the EcDNAm gap and future studies should focus on the application of EcDNA as a potential biomarker in patients with early LC.

Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma.

BACKGROUND: The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. METHODS: We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). RESULTS: The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. CONCLUSIONS: Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Multibiomarker panels in liquid biopsy for early detection of pancreatic cancer - a comprehensive review.

Pancreatic ductal adenocarcinoma (PDAC) is frequently detected in late stages, which leads to limited therapeutic options and a dismal overall survival rate. To date, no robust method for the detection of early-stage PDAC that can be used for targeted screening approaches is available. Liquid biopsy allows the minimally invasive collection of body fluids (typically peripheral blood) and the subsequent analysis of circulating tumor cells or tumor-associated molecules such as nucleic acids, proteins, or metabolites that may be useful for the early diagnosis of PDAC. Single biomarkers may lack sensitivity and/or specificity to reliably detect PDAC, while combinations of these circulating biomarkers in multimarker panels may improve the sensitivity and specificity of blood test-based diagnosis. In this narrative review, we present an overview of different liquid biopsy biomarkers for the early diagnosis of PDAC and discuss the validity of multimarker panels.

Circulating tumor cells: from new biological insights to clinical practice.

The primary reason for high mortality rates among cancer patients is metastasis, where tumor cells migrate through the bloodstream from the original site to other parts of the body. Recent advancements in technology have significantly enhanced our comprehension of the mechanisms behind the bloodborne spread of circulating tumor cells (CTCs). One critical process, DNA methylation, regulates gene expression and chromosome stability, thus maintaining dynamic equilibrium in the body. Global hypomethylation and locus-specific hypermethylation are examples of changes in DNA methylation patterns that are pivotal to carcinogenesis. This comprehensive review first provides an overview of the various processes that contribute to the formation of CTCs, including epithelial-mesenchymal transition (EMT), immune surveillance, and colonization. We then conduct an in-depth analysis of how modifications in DNA methylation within CTCs impact each of these critical stages during CTC dissemination. Furthermore, we explored potential clinical implications of changes in DNA methylation in CTCs for patients with cancer. By understanding these epigenetic modifications, we can gain insights into the metastatic process and identify new biomarkers for early detection, prognosis, and targeted therapies. This review aims to bridge the gap between basic research and clinical application, highlighting the significance of DNA methylation in the context of cancer metastasis and offering new avenues for improving patient outcomes.

Epithelial­derived head and neck squamous tumourigenesis (Review).

Head and neck squamous cell carcinomas (HNSCCs), a heterogeneous group of cancers that arise from the mucosal epithelia cells in the head and neck areas, present great challenges in diagnosis, treatment and prognosis due to their complex aetiology and various clinical manifestations. Several factors, including smoking, alcohol consumption, oncogenic genes, growth factors, Epstein­Barr virus and human papillomavirus infections can contribute to HNSCC development. The unpredictable tumour microenvironment adds to the complexity of managing HNSCC. Despite significant advances in therapies, the prediction of outcome after treatment for patients with HNSCC remains poor, and the 5­year overall survival rate is low due to late diagnosis. Early detection greatly increases the chances of successful treatment. The present review aimed to bring together the latest findings related to the molecular mechanisms of HNSCC carcinogenesis and progression. Comprehensive genomic, transcriptomic, metabolomic, microbiome and proteomic analyses allow researchers to identify important biological markers such as genetic alterations, gene expression signatures and protein markers that drive HNSCC tumours. These biomarkers associated with the stages of initiation, progression and metastasis of cancer are useful in the management of patients with cancer in order to improve their life expectancy and quality of life.

The challenge of molecular selection in liver-limited metastatic colorectal cancer for surgical resection: a systematic review and meta-analysis in the context of current and future approaches.

Objectives: Treatment of metastatic colorectal cancer (mCRC) includes resection of liver metastases (LM), however, no validated biomarker identifies patients most likely to benefit from this procedure. This meta-analysis aimed to assess the impact of the most relevant molecular alterations in cancer-related genes of CRC (i.e., RAS, BRAF, SMAD4, PIK3CA) as prognostic markers of survival and disease recurrence in patients with mCRC surgically treated by LM resection. Methods: A systematic literature review was performed to identify studies reporting data regarding survival and/or recurrence in patients that underwent complete liver resection for CRC LM, stratified according to RAS, BRAF, PIK3CA, and SMAD4 mutational status. Hazard ratios (HRs) from multivariate analyses were pooled in the meta-analysis and various adjustment strategies for confounding factors were combined. The search was conducted in numerous databases, including MEDLINE (PubMed), Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCO host), and WHO Global Index Medicus, through March 18th, 2022. Meta-analyses, editorials, letters to the editor, case reports, studies on other primary cancers, studies with primary metastatic sites other than the liver, studies lacking specific oncological outcome variables or genetic data, non-English language studies, and studies omitting residual disease data from liver metastasectomy were excluded. The remaining 47 studies were summarized in a descriptive table which outlines the key characteristics of each study and final results were graphically presented. Results: RAS mutation status was negatively associated with overall survival (OS) (HR, 1.68; 95% CI, 1.54-1.84) and recurrence free survival (RFS) (HR, 1.46; 95% CI, 1.33-1.61). A negative association was also found for BRAF regarding OS (HR, 2.64; 95% CI, 2.15-3.24) and RFS (HR, 1.89; 95% CI, 1.32-2.73) and SMAD4 regarding OS (HR, 1.93; 95% CI, 1.56-2.38) and RFS (HR, 1.95; 95% CI, 1.31-2.91). For PIK3CA only three studies were eligible and no significant association with either OS or RFS could be highlighted. Conclusion: RAS, BRAF, and SMAD4 are negatively associated with OS and RFS in patients undergoing curative liver metastasectomy from colorectal cancer. No conclusion can be drawn for PIK3CA due to the limited literature availability. These data support the integration of RAS, BRAF, and SMAD4 mutational status in the surgical decision-making for colorectal liver metastasis. Nevertheless, we have to consider several limitations, the major ones being the pooling of results from studies that evaluated patient outcomes as either disease-free survival (DFS) or RFS; the inclusion of patients with minimal residual disease and unconsidered potential confounding factors, such as variability in resectability definitions, chemotherapy use, and a potential interaction between biological markers and pre- and post-resection pharmacological treatments.

An aptamer-guided fluorescence polarisation platform for extracellular vesicle liquid biopsy.

The translation of discoveries on extracellular vesicle (EV) based cancer biomarkers to personalised precision oncology requires the development of robust, sensitive and specific assays that are amenable to adoption in the clinical laboratory. Whilst a variety of elegant approaches for EV liquid biopsy have been developed, most of them remain as research prototypes due to the requirement of a high level of microfabrication and/or sophisticated instruments. Hence, this study is set to develop a simple DNA aptamer-enabled and fluorescence polarisation-based homogenous assay that eliminates the need to separate unbound detection ligands from the bound species for EV detection. High specificity is achieved by immobilising EVs with one set of antibodies and subsequently detecting them with a DNA aptamer targeting a distinct EV biomarker. This two-pronged strategy ensures the removal of most, if not all, non-EV substances in the input biofluids, including soluble proteins, protein aggregates or non-vesicular particles, prior to quantifying biomarker-positive EVs. A limit of detection of 5.0 × 106 EVs/mL was achieved with a linear quantification range of 5.0 × 108 to 2.0 × 1010 EVs/mL. Facilitated by a multiple parametric analysis strategy, this aptamer-guided fluorescence polarisation assay was capable of distinguishing EVs from three different types of solid cancer cells based on quantitative differences in the levels of the same sets of biomarkers on EVs. Given the simplicity of the method and its ease of implementation in automated clinical biochemistry analysers, this assay could be exploited for future EV-based continuous and real-time monitoring of the emergence of new macro- or micro-metastasis, cancer progression as well as the response to treatment throughout different stages of cancer management in the clinic.

Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA.

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection.

Elucidating the role of tumor-associated ALOX5+ mast cells with transformative function in cervical cancer progression via single-cell RNA sequencing.

Background: Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship between mast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cell multi-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients' prognoses and quality of life. Method: The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations. Result: The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies. Conclusions: We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5+MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC.

Identification of PI3K-AKT Pathway-Related Genes and Construction of Prognostic Prediction Model for ccRCC.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC), the predominate histological type of renal cell carcinoma (RCC), has been extensively studied, with poor prognosis as the stage increases. Research findings consistently indicated that the PI3K-Akt pathway is commonly dysregulated across various cancer types, including ccRCC. Targeting the PI3K-Akt pathway held promise as a potential therapeutic approach for treating ccRCC. Development and validation of PI3K-Akt pathway-related genes related biomarkers can enhance healthcare management of patients with ccRCC. PURPOSE: This study aimed to identify the key genes in the PI3K-Akt pathway associated with the diagnosis and prognosis of CCRCC using data mining from the Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO) datasets. METHODS: The purpose of this study is to use bioinformatics methods to screen data sets and clinicopathological characteristics associated with ccRCC patients. The exhibited significantly differential expressed genes (DEGs) associated with the PI3K-Akt pathway were examined by KEGG. In addition, Kaplan-Meier (KM) analysis used to estimate the survival function of the differential genes by using the UALCAN database and graphPad Prism 9.0. And exploring the association between the expression levels of the selected genes and the survival status and time of patients with ccRCC based on SPSS22.0. Finally, a multigene prognostic model was constructed to assess the prognostic risk of ccRCC patients. RESULTS: A total of 911 genes with common highly expressed were selected based on the GEO and TCGA databases. According to the KEGG pathway analysis, there were 42 genes enriched in PI3K-Akt signalling pathway. And seven of highly expressed genes were linked to a poor prognosis in ccRCC. And a multigene prognostic model was established based on IL2RG, EFNA3, and MTCP1 synergistic expression might be utilized to predict the survival of ccRCC patients. CONCLUSIONS: Three PI3K-Akt pathway-related genes may be helpful to identify the prognosis and molecular characteristics of ccRCC patients and to improve therapeutic regimens, and these risk characteristics might be further applied in the clinic.

Preservation of cfRNA in cytological supernatants for cfDNA & cfRNA double detection in non-small cell lung cancer patients.

BACKGROUD: Supernatants from various cytological samples, including body cavity effusion, sputum, bronchoalveolar lavage fluid (BALF), and needle aspiration, have been validated for detecting genetic alterations using cell-free DNA (cfDNA) in patients with non-small cell lung cancer (NSCLC). However, the sensitivity of fusion variations detection remains challenging. The protection of cell-free RNA (cfRNA) is critical for resolving the issue. METHODS: A protective solution (PS) was applied for preserving cfRNA in cytological supernatant (CS), and the quality of protected cfRNA was assessed by cycle threshold (CT) values from reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, we collected an additional set of malignant cytological and matched tumor samples from 84 NSCLC patients, cfDNA & cfRNA extraction and double detection for driver gene mutations was validated using the multi-gene mutations detection by RT-qPCR. RESULTS: Under the optimal protection system, 91.0% (101/111) of cfRNA were protected effectively. Among the 84 NSCLC patient samples, seven cytological samples failed the tests. In comparison with tumor samples, the overall sensitivity and specificity of detecting driver genes of supernatant cfDNA and cfRNA were 93.8% (74/77) and 100% (77/77), respectively. Notably, when focusing exclusively on patients with fusion gene changes, both sensitivity and specificity reached 100% (11/11) for EML4-ALK, ROS1, RET fusions, and MET ex14 skipping. CONCLUSION: These findings suggest that cfDNA & cfRNA extraction and double detection strategy recommended in this study improve the accuracy of driver genes mutations test, especially for RNA-based assay.

Identification of Biomarkers Associated With Paget's Disease of Bone and Bone Metastasis From Breast Cancer Patients.

BACKGROUND: The bone is among the most frequently chosen sites for the metastatic spread of breast cancer. The prediction of biomarkers for BM (Bone Metastasis) and PDB (Paget's disease of bone) initiated from breast cancer could be critically important in categorizing individuals with a higher risk and providing targeted treatment for PDB and BM. AIMS: This research aims to investigate the common key candidate biomarkers that contribute to BM-BCa (Bone metastasis of breast cancer) and PDB by employing network decomposition and functional enrichment studies. METHODS AND RESULTS: This research analyzed high-throughput transcriptome sequencing (RNA-Seq). For this work, the dataset (GSE121677) was downloaded from GEO (Gene Expression Omnibus), and DEGs were identified using Galaxy and R script 4.3. Using STRING (Search Tool for the Retrieval of Interacting Genes), high-throughput research created a protein-protein interaction network (PPIN). The BM-PDB-interactome was created using Cytoscape 3.9.1 and PDB biomarkers, with the top 3% DEGs from BM-BCa. Functional Enrichment Analysis (Funrich 3.1.3) and DAVID 6.8 performed functional and gene set enrichment analysis (GSEA) of putatively essential biomarkers. TCGA (The Cancer Genome Atlas) validated the discovered genes. Based on our research, we identified 1262 DEGs; among these DEGs, 431 genes were upregulated, and 831 genes were downregulated. During the third growth of the interactome, 20 more genes were pinned to the BM-PDB interactome. RAC2, PIAS1, EP300, EIF2S1, and LRP6 are among the additional 25% of genes identified to interact with the BM-PDB interactome. To corroborate the findings of the research presented, additional functional and gene set enrichment analyses have been performed. CONCLUSION: Of the five reported genes (RAC2, PIAS1, EP300, EIF2S1, and LRP6), RAC2 was identified to function as the common key potential biomarker in the BM-PDB interactome analysis and validated by TCGA in the study presented.

Autoantibodies in cancer: a systematic review of their clinical role in the most prevalent cancers.

Although blood autoantibodies were initially associated with autoimmune diseases, multiple evidence have been accumulated showing their presence in many types of cancer. This has opened their use in clinics, since cancer autoantibodies might be useful for early detection, prognosis, and monitoring of cancer patients. In this review, we discuss the different techniques available for their discovery and validation. Additionally, we discuss here in detail those autoantibody panels verified in at least two different reports that should be more likely to be specific of each of the four most incident cancers. We also report the recent developed kits for breast and lung cancer detection mostly based on autoantibodies and the identification of novel therapeutic targets because of the screening of the cancer humoral immune response. Finally, we discuss unsolved issues that still need to be addressed for the implementation of cancer autoantibodies in clinical routine for cancer diagnosis, prognosis, and/or monitoring.

An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies.

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

LIG1 is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing.

Background: Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the precise involvement of LIG1 in BLCA remains elusive. This pioneering investigation delves into the uncharted territory of LIG1's impact on BLCA. Our primary objective is to elucidate the intricate interplay between LIG1 and BLCA, alongside exploring its correlation with various clinicopathological factors. Methods: We retrieved gene expression data of para-carcinoma tissues and bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data were processed using the "Seurat" package. Differential expression analysis was then performed with the "Limma" package. The construction of scale-free gene co-expression networks was achieved using the "WGCNA" package. Subsequently, a Venn diagram was utilized to extract genes from the positively correlated modules identified by WGCNA and intersect them with differentially expressed genes (DEGs), isolating the overlapping genes. The "STRINGdb" package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted KEGG and GO enrichment analyses to uncover the regulatory mechanisms and biological functions associated with the hub genes. A machine-learning diagnostic model was established using the R package "mlr3verse." Mutation profiles between the LIG1^high and LIG1^low groups were visualized using the BEST website. Survival analyses within the LIG1^high and LIG1^low groups were performed using the BEST website and the GENT2 website. Finally, a series of functional experiments were executed to validate the functional role of LIG1 in BLCA. Results: Our investigation revealed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correlating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1's involvement in critical function such as the DNA replication, cellular senescence, cell cycle and the p53 signalling pathway. Notably, the mutational landscape of BLCA varied significantly between LIG1high and LIG1low groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immune regulation within the BLCA microenvironment, thereby impacting prognosis. Subsequent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples. Conclusions: Our research demonstrates that LIG1 plays a crucial role in promoting bladder cancer malignant progression by heightening proliferation, invasion, EMT, and other key functions, thereby serving as a potential risk biomarker.