Cancer Diagnostic Biomarkers Research Articles

Machine-learning diagnostics of breast cancer using piRNA biomarkers

Background and objectives Prior studies have shown that small non-coding RNAs (sncRNAs) are associated with cancer occurrence or development. Recently, a newly discovered class of small ncRNAs known as PIWI-interacting RNAs (piRNAs) have been found to play a vital role in physiological processes and cancer initiation. This study aims to utilize piRNAs as innovative, noninvasive diagnostic biomarkers for breast cancer. Our objective is to develop computational methods that leverage piRNA attributes for breast cancer prediction and its application in diagnostics. Methods We created a set of piRNA sequence descriptors using information extracted from the piRNA sequences. To ensure accuracy, we found a path to convert non-standard piRNA to standard names to enable precise identification of these sequences. Using these descriptors, we applied machine-learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) to a dataset of piRNA to assess the predictive accuracy of the following classifiers: Logistic Regression model, Sequential Minimal Optimization (SMO), Random Forest classifier, and Logistic Model Tree (LMT). Furthermore, we performed Shapley additive explanations (SHAP) Analysis to understand which descriptors were the most relevant to the prediction accuracy. The ML models were then validated on an independent dataset to evaluate their effectiveness in predicting breast cancer. Results The top three performing classifiers in WEKA were Logistic Regression, SMO, and LMT. The Logistic Regression model achieved an accuracy of 90.7% in predicting breast cancer, while SMO and LMT attained 89.7% and 85.65%, respectively. Conclusions Our study demonstrates the effectiveness of using ML-based piRNA classifiers in diagnosing breast cancer and contributes to the growing body of evidence supporting piRNAs as biomarkers in cancer diagnosis. However, additional research is needed to validate these findings and further assess the clinical applicability of this approach.

Supersulfide metabolome of exhaled breath condensate applied as diagnostic biomarkers for esophageal cancer.

Early detection of esophageal cancer is essential for esophagogastroduodenoscopy and histopathological diagnosis. However, endoscopic examinations are sometimes invasive, which limits their clinical application and compliance, and traditional blood tumor markers are unsuitable for cancer screening. The current study aimed to evaluate the usefulness of sulfur metabolites as new biomarkers for esophageal cancer using blood samples and exhaled breath condensate (EBC), which can be readily obtained and is non-invasive. We collected EBC and plasma samples from 50 patients with esophageal cancer and 30 healthy controls. Sulfur metabolome analysis using tandem mass spectrometry was performed to compare the metabolic profile between the two groups. Supersulfide metabolic profiles were different between the two cohorts. Supersulfide metabolome analysis showed that cysteine hydropersulfide (CysSSH) and homocysteine hydropersulfide (HomoCysSSH) were increased in the plasma of patients with esophageal cancer. Elevated levels of HomoCysSSH could distinguish patients with esophageal cancer from healthy subjects (area under the curve [AUC]: 0.93, sensitivity: 89%, specificity: 96%). Interestingly, we also detected an elevation of supersulfides in the EBC analysis. CysSSH levels significantly increased in the EBC recovered from patients with esophageal cancer (AUC: 0.71, sensitivity: 60%, specificity: 96%). In addition, the observed level was correlated with that of HomoCysSSH in the plasma (r = 0.27). Supersulfides, such as CysSSH and HomoCysSSH, are potential biomarkers for detecting esophageal cancer. CysSSH from EBC may serve as a valuable non-invasive biomarker with similar detection ability but with superior precision and convenience compared with the currently available blood biomarkers.

MicroRNA-155 as Biomarker and Its Diagnostic Value in Breast Cancer: A Systematic Review.

The investigation of microRNAs (miRNAs) for the purpose of identifying biomarkers and new treatments for breast cancer has been gaining traction from scientists in recent years. Of all the miRNAs, miR-155 has been reportedly involved in breast cancer development as it regulates various cellular processes such as glucose uptake, proliferation, metastasis, and migration. Various efforts have been done towards researching miR-155 as a biomarker in breast cancer; however, the results were varied. The objective of the current systematic review is to compile and summarize information regarding miR-155 as a potential diagnostic biomarker for breast cancer. All eligible studies were found from SCOPUS and PubMed databases. Out of the 376 potential eligible records, only 26 original articles were selected for further assessment according to inclusion and exclusion criteria. The expressions of miR-155 in serum, plasma, biopsy, urine, nipple aspirate fluid, serum exosomes, and peripheral blood mononuclear cells were recorded and analyzed. Besides that, the expression of miR-155 was also correlated to clinicopathological features in breast cancer patients. The area under the curve (AUC) values from receiver operating characteristic (ROC) analysis used to evaluate diagnostic sensitivity and specificity of miR-155 as a diagnostic biomarker were also recorded. The limitations such as the small sampling size, the unemployment of internal controls for quantitative real-time polymerase chain reaction (RT-qPCR), and inconsistency of sensitivity as well as specificity values of miR-155 as a biomarker have been discussed. The present study proposed that miR-155 is a good diagnostic biomarker for breast cancer; however, further clinical research is required to assess the validity of miR-155 as a potential biomarker to translate the research outcomes into clinical practice.

A comprehensive multi-omics study reveals potential prognostic and diagnostic biomarkers for colorectal cancer

A comprehensive multi-omics study reveals potential prognostic and diagnostic biomarkers for colorectal cancer

Cuproptosis Regulation by Long Noncoding RNAs: Mechanistic Insights and Clinical Implications in Cancer.

Although survival rates have been improved in recent years, the prognosis of many cancer types remains inadequate, mostly owing to treatment resistance. Moreover, there is a continued need for exploring novel and reliable tumor markers to achieve accurate diagnosis. Understanding the molecular complexity of cancer allows for the development of more effective and personalized treatments and facilitates the discovery of biomarkers that surpass traditional ones and assist in cancer diagnosis and monitoring disease progression and response to treatment. Recent studies exploring the complexity of cancer biology have identified a new form of cell death, known as cuproptosis, which is driven by the accumulation of copper and subsequent stress induced by dysregulation of copper homeostasis. Increased copper level enables cancer cells to maintain their accelerated growth rates and metastatic potential, yet these cells can evade cuproptosis. Long noncoding RNAs (lncRNAs) have been recognized for their pivotal role in different hallmarks of cancer, including resistance to cell death. They have been found to be implicated in controlling copper balance and cuproptosis. Besides, lncRNAs associated with cuproptosis pathway have demonstrated their potential as diagnostic and prognostic cancer biomarkers as well as indicators of treatment response. Our review aims to summarize recent studies focusing on the intricate relationship between lncRNAs and cuproptosis and explore the mechanisms by which lncRNAs can modulate copper homeostasis and regulate cuproptosis pathway. We also highlight recent discoveries concerning the role of cuproptosis-related lncRNAs in diagnosis, prognosis, and therapy of different types of cancer. By elucidating the significance of cuproptosis-related lncRNAs, this review provides insights into how these lncRNAs can be used to develop new therapeutic strategies to improve treatment outcomes.

Identification of Novel Diagnostic Biomarkers in Triple-Negative Breast Cancer Through Analysis of Polymorphic SNPs and APA Events

Background: As a subtype of breast cancer, triple-negative breast cancer (TNBC) exhibits unique pathological phenotypes and severe morbidity trends. New evidence suggests that aberrant alternative polyadenylation (APA) events can be regulated by single nucleotide polymorphisms (SNPs) and are associated with breast cancer. The study aimed to identify the APA-associated susceptibility SNP in TNBC, which may be useful in screening and treatment. Methods: The RNA sequencing data is from 285 tumor tissues and 66 normal tissues of TNBC patients, accessed from the NCBI dataset FUSCCTNBC (Accession: PRJNA486023). We analyzed gene expression levels, APA events, and APA-associated SNPs, and explored their relationships and influences on TNBC. Results: Our study revealed significant differences in both gene expression and APA events between tumor and normal tissues of TNBC patients. The differentially expressed genes are enriched in protein transcription, folding, localization, and targeting. apaQTL analysis indicated significant associations between APA events of genes and SNPs. We found that the APA event of the transmembrane p24 trafficking protein 9 (TMED9) is highly related to the SNP rs3749822, where the G allele would decrease the Poly-A length of TMED9 and increase its expression level. Conclusion: The study elucidates the significant association between SNP rs3749822 and the APA event of the TMED9 gene, as well as their influences on TNBC, highlighting the susceptibility of SNP rs3749822 allele G for TNBC. Our findings provide new directions for further exploration of SNPs affecting APA events, aiding in identifying disease-susceptible populations.

Evaluation of the role of circular RNA (circ-FAF1) as a diagnostic biomarker for breast cancer in a cohort of Egyptian breast cancer patients.

The identification of circulating potential biomarkers may help earlier diagnosis of breast cancer, which is critical for effective treatment and better disease outcomes. We aimed to study the role of circ-FAF1 as a diagnostic biomarker in female breast cancer using peripheral blood samples of these patients, and to investigate the relation between circ-FAF1 and different clinicopathological features of the included patients. This case-control study enrolled 60 female breast cancer patients and 60 age-matched healthy control subjects. For all study subjects, serum samples were collected for RNA extraction followed by reverse transcription and quantitative real time polymerase chain reaction for circ-FAF1 relative expression level. Serum circ-FAF1 was significantly downregulated in the studied patients compared to control subjects (p < 0.001). Low expression level of circ-FAF1 was significantly associated with presence of lymph node spread (p < 0.001), positive metastasis (p = 0.002), estrogen receptor negativity (p < 0.001), HER2 positivity (p < 0.001), and it was moderately correlated with higher Ki-67 index (r=-0.429, p = 0.001). However, circulating circ-FAF1 level had no statistically significant correlation with progesterone receptor status (p = 0.053), tumor histopathological type (p = 0.895) and histological grade (p = 0.369). Using ROC curve, serum circ-FAF1 had an AUC of 0.885, 81.67% diagnostic sensitivity and 76.67% diagnostic specificity. Serum level of circ-FAF1 is a promising biomarker that can help in diagnosis of breast cancer. The low expression in breast cancer was correlated with lymph node spread, presence of metastasis, and with histopathological parameters suggestive of a worse outcome in these patients.

Long Intergenic Non-Coding RNAs and BRCA1 in Breast Cancer Pathogenesis: Neighboring Companions or Nemeses?

Breast cancer is one of the leading causes of mortality among women, primarily due to its complex molecular landscape and heterogeneous nature. The tendency of breast cancer patients to develop metastases poses significant challenges in clinical management. Notably, mutations in the breast cancer gene 1 (BRCA1) significantly elevate breast cancer risk. The current research endeavors employ diverse molecular approaches, including RNA, DNA, and protein studies, to explore avenues for the early diagnosis and treatment of breast cancer. Recent attention has shifted towards long non-coding RNAs (lncRNAs) as promising diagnostic, prognostic, and therapeutic targets in the multifaceted progression of breast cancer. Among these, long intergenic non-coding RNAs (lincRNAs), a specific class of lncRNAs, play critical roles in regulating various aspects of tumorigenesis, including cell proliferation, apoptosis, epigenetic modulation, tumor invasion, and metastasis. Their distinctive expression patterns in cellular and tissue contexts underscore their importance in breast cancer development and progression. Harnessing lincRNAs’ sensitivity and precision as diagnostic, therapeutic, and prognostic markers holds significant promise for the clinical management of breast cancer. However, the potential of lincRNAs remains relatively underexplored, particularly in the context of BRCA1-mutated breast cancer and other clinicopathological parameters such as receptor status and patient survival. Consequently, there is an urgent need for comprehensive investigations into novel diagnostic and prognostic breast cancer biomarkers. This review examines the roles of lincRNAs associated with BRCA1 in the landscape of breast cancer, highlighting the potential avenues for future research and clinical applications.

Size-Dependent Separation of Extracellular Vesicle Subtypes with Exodisc Enabling Proteomic Analysis in Prostate Cancer.

Extracellular vesicles (EVs) are emerging as crucial biomarkers in cancer diagnostics and therapeutics with their heterogeneity presenting both challenges and opportunities in prostate cancer research. However, existing methods for isolating and characterizing EV subtypes have been limited by inefficient separation and inadequate proteomic analysis. Here we show an optimized centrifugal microfluidic device, Exodisc, that efficiently isolates large quantities of EV subtypes from particle-enriched medium, enabling comprehensive proteomic analysis of small (EV-S, 20-200 nm) and large (EV-L, >200 nm) EVs. Using this device, we successfully separated EV-S and EV-L from prostate cancer cell lines LNCaP and PC3. Mass spectrometry-based proteomics revealed that EV proteins reflect parental cell characteristics more than EV size, with EV-L demonstrating increased expression of PSMA-correlated proteins. Our optimized protocol addresses challenges in EV isolation and characterization, providing a more effective method for studying cellular and molecular mechanisms of specific EV subtypes. This study extends the potential use of EVs as a liquid biopsy for cancer theranostics, paving the way for more precise isolation of EV subtypes and potentially leading to improved biomarker discovery and the development of personalized treatments.

Significance of N-terminal pro-B-type natriuretic peptide levels in lung cancer

High blood levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) have been shown in various malignancies. In lung cancer, the importance of NT-proBNP is not clear. In this study, we aimed to investigate the significance of the correlation of NT-proBNP levels in lung cancer with tumor stage, tumor diameter, histopathology, and specific sites of mediastinal metastasis: lymphadenopathy; pericardial, cardiac, major vessel, other mediastinal organ or lymphatic involvement/invasion. A total of 105 lung cancer and 120 control patients (chronic obstructive lung disease, interstitial lung disease, pulmonary thromboembolism, and pneumonia; 30/subgroup) with measured NT-proBNP levels were included retrospectively. Demographics, comorbidities, and echocardiographic findings in all patients, as well as histologic subtype, diameter, stage, and radiologic and/or pathologic mediastinal involvement/invasion of the tumor to the mediastinum in patients with lung cancer, were studied with regards to blood NT-proBNP levels. When lung cancer and control groups were compared globally or as subgroups with comorbidities, NT-proBNP levels did not show meaningful differences. However, NT-proBNP levels were determined to be 249 pg/mL and 88 pg/mL in lung cancer (n=68) and control subgroups (n=58) without comorbidities, respectively (p=0.001). Among lung cancer patients without comorbidities and those with cardiac, pericardial, major vascular, or other mediastinal involvement/invasion (lymphadenopathy, lymphatic, or other organ invasion) (n=27), the NT-proBNP level was 303 pg/mL, whereas it was 166 pg/mL in those without these mediastinal invasions (n=41) (p=0.031). There is a need for much larger, randomized studies to obtain evidence for the potential role of NT-proBNP as a helpful diagnostic biomarker for lung cancer. Clinical suspicion of malignancy may be posed if high NT-proBNP levels cannot be explained by all other risk factors and disorders or diseases. Furthermore, pericardial, cardiac, major vessel, or other mediastinal invasion/involvement should be sought when high NT-proBNP levels are determined in lung cancer patients without any comorbidities or risk factors for high NT-proBNP levels.

Exploring the potential of BOLA3-DT as a diagnostic biomarker in prostate cancer.

Exploring the potential of BOLA3-DT as a diagnostic biomarker in prostate cancer. Expression of the lncRNA BOLA3-DT was analyzed between normal and tumor samples in the GDC TCGA PRAD (Genomic Data Commons: The Cancer Genome Atlas Prostate Adenocarcinoma Collection) dataset. Disease progression-related clinicopathological parameters such as serum PSA level (ng/ml) and Gleason score were associated with the expression of BOLA3-DT using the same GDC TCGA PRAD dataset. To validate these findings, the expression of BOLA3-DT was checked in our sample set of 15 PCa (prostate cancer) and 15 BPH (benign hypertrophy of the prostate) patients. In the GDC TCGA PRAD dataset, the expression of the lncRNA BOLA3-DT was significantly downregulated in prostate cancer tissue samples (n = 492) compared to adjacent normal (n = 52; p < 0.0001), and, there was a significant negative correlation between the expression of the lncRNA BOLA3-DT and the serum PSA level (p < 0.01). However, no significant association was found between the lncRNA BOLA3-DT expression and the Gleason score (p > 0.05). In this study, it was found that BOLA3-DT was downregulated in PCa tissue samples compared to BPH samples (p > 0.05). In the GDC TCGA PRAD dataset, it was revealed that BOLA3-DT could serve as an excellent diagnostic marker with a sensitivity of 86.9% and a specificity of 84.6% (AUC-0.916). LncRNA BOLA3-DT, a novel long non-coding RNA, was found to be downregulated in prostate cancer. The expression of the lncRNA BOLA3-DT can serve as a diagnostic marker in prostate cancer.

Dynamine 3 as a Diagnostic and Prognostic Biomarker in Pancreatic Cancer: Implications for Early Detection and Targeted Therapy

ABSTRACT Background Dynamins are defined as a group of molecules with GTPase activity that play a role in the formation of endocytic vesicles and Golgi apparatus. Among them, DNM3 has gained recognition in oncology for its tumor suppressor role. Based on this, the aim of this study is to investigate the effects of the DNM3 gene in patients diagnosed with pancreatic cancer using bioinformatics databases. Materials and Methods For differential gene expression analysis, TCGA TARGET GTEx study on the UCSC Xena, GSE196009, GSE211398, GSE151580 datasets on The Gene Expression Omnibus (GEO) were utilized; for the analysis of changes in gene expression according to stages GEPIA2 were utilized; for the analysis of changes in gene expression according to clinical and pathological characteristics, UALCAN was employed; for Overall Survival (OS) analysis, Kaplan-Meier Plotter was used; for gene alteration analysis, cBioPortal was utilized; for immune cell infiltration analysis, Tumor Immune Estimation Resource (TIMER) and TIMER2.0 were employed; for protein-protein interaction and gene enrichment analyses, STRING was used; for enrichment analyses Enrichr was used; for Gene Set Correlation Enrichment Analysis Gscore was used on GSE15471; for essentiality of DNM3 gene in pancratic cancer cell lines DepMap was used; and for the detection of miRNAs, miRDB was utilized; ENCORI was used for gene-miRNA correlation and miRNA prognosis analyses. Results In the pancreatic adenocarcinoma (PAAD) cohort, DNM3 gene expression was higher in tumor samples, and there was no significant difference in expression among cancer stages. High levels of DNM3 gene expression were associated with longer OS in PAAD. A weak positive correlation was observed between DNM3 gene expression and B-Cell and CD + T Cell infiltrations, while a moderate positive correlation was found with CD8+ T Cell, Macrophage, Neutrophil, and Dendritic Cell infiltrations in TIMER. NK cell by QUANTISEQ, CD 4+ T Cell by TIMER, T cell regulatory (Tregs) by CIBERSORT-ABS infiltrations were positively associated with DNM3 gene expression and decreased risk in prognosis. Common lymphoid progenitor by XCELL and MDSC by TIDE infiltrations were negatively associated with DNM3 gene expression and increased risk of prognosis. Macrophage M1 by QUANTISEQ was positively associated with DNM3 gene expression and increased risk in prognosis. DNM3 gene appears to be associated with various pathways related to inflammation and the immune system. Amplification of the DNM3 gene was detected in 5 out of 175 patients. Enrichment was observed in pathways such as bacterial invasion of epithelial cells, endocytosis, endocrine and other factor-regulated calcium reabsorption, synaptic vesicle cycle, and phospholipase D signaling pathway. According to Gscore, DNM3 gene was associated with Fc epsilon RI signaling pathway, HALLMARK MTORC1 SIGNALING, HALLMARK EPITHELIAL MESENCHYMAL TRANSITION gene sets. According to ENCORI, DNM3 gene was negatively correlated with hsa-miR-203a-3p and increased expression of this miRNA was associated with adverse prognosis in PAAD. Conclusions The DNM3 gene may play a tumor suppressor role in pancreatic cancer, similar to its role in other malignancies. The contribution of immune cells may also be significant in this effect. However, in vitro studies are needed to elucidate the mechanisms triggered in pancreatic cancer.

Deciphering the potential ability of DExD/H-box helicase 60 (DDX60) on the proliferation, diagnostic and prognostic biomarker in pancreatic cancer: a research based on silico, RNA-seq and molecular biology experiment

BackgroundPancreatic cancer is one of the most malignant abdominal tumors. DDX60 has been shown to be associated with a variety of tumor biological processes. However, DDX60 in pancreatic cancer has not been reported. Our study confirmed that DDX60 can serve as a novel biomarker for diagnosis and treatment of pancreatic cancer.Materials and methodsWe downloaded pancreatic cancer datasets from GEO and TCGA databases, respectively. To investigate the relationship between DDX60 expression and prognosis in pancreatic cancer. GSEA analysis was performed on DDX60. We performed RNA-seq to further explore the downstream biological targets of DDX60 and the signaling pathways that may be involved in pancreatic cancer. Finally, we tested it through molecular biology experiments. First, we constructed the plasmid and tested the plasmid effect by WB. Then MTT assay was performed to explore the effect of DDX60 knockout on the proliferation of pancreatic cancer cells. LDH assay was performed to explore the effect of DDX60 on the release of lactate dehydrogenase from tumor cells. The effect of DDX60 on cell proliferation was further explored by clonal formation experiment. Continue to explore clinical therapeutic drugs sensitive to DDX60 targets.ResultsBy analyzing the GSE71729, GSE183795, GSE16515, GSE28735 and GSE62452 data sets, we found that DDX60 was highly expressed in pancreatic cancer. And is associated with poorer outcomes for pancreatic patients. The mRNA expression level of DDX60 was correlated with lymph node metastasis and grade in clinical pancreatic patients. Through the results of RNA-seq analysis, GO and KEGG analysis showed that DDX60 may be associated with cell cycle in pancreatic cancer. Through molecular biology experiments (MTT, LDH, and clonal formation experiment), we found that When DDX60 is knocked down in pancreatic cancer cells, the proliferation ability of tumor cells is significantly decreased. Several drugs targeting about DDX60 have been found, such as JW-7–52-1, this could provide direction for drug therapy against the DDX60 target.ConclusionIn summary, DDX60 can be used as a novel biomarker related to the diagnosis and treatment of pancreatic cancer, participate in tumor proliferation, and is associated with poor prognosis in patients.

The Homeobox Transcription Factor NKX3.1 Displays an Oncogenic Role in Castration-Resistant Prostate Cancer Cells.

Prostate cancer (PCa) is the second leading cause of cancer-related death in men. The increase in incidence rates of more advanced and aggressive forms of the disease year-to-year fuels urgency to find new therapeutic interventions and bolster already established ones. PCa is a uniquely targetable disease in that it is fueled by male hormones (androgens) that drive tumorigenesis via the androgen receptor or AR. Current standard-of-care therapies directly target AR and its aberrant signaling axis but resistance to these therapies commonly arises, and the mechanisms behind the onset of therapy-resistance are still elusive. Research has shown that even with resistant disease, AR remains the main driver of growth and survival of PCa, and AR target genes and cofactors may help mediate resistance to therapy. Here, we focused on a homeobox transcription factor that exhibits a close relationship with AR-NKX3.1. Though NKX3.1 is traditionally thought of as a tumor suppressor, it has been previously reported to promote cancer cell survival by cooperating with AR. The role of NKX3.1 as a tumor suppressor perhaps in early-stage disease also contradicts its profile as a diagnostic biomarker for advanced prostate cancer. We investigated the physical interaction between NKX3.1 and AR, a modulated NKX3.1 expression in prostate cancer cells via overexpression and knockdown and assayed subsequent viability and downstream target gene expression. We find that the expression of NKX3.1 is maintained in advanced PCa, and it is often elevated because of aberrant AR activity. Transient knockdown experiments across various PCa cell line models reveal NKX3.1 expression is necessary for survival. Similarly, stable overexpression of NKX3.1 in PCa cell lines reveals an androgen insensitive phenotype, suggesting NKX3.1 is sufficient to promote growth in the absence of an AR ligand. Our work provides new insight into NKX3.1's oncogenic influence on PCa and the molecular interplay of these transcription factors in models of late-stage prostate cancer.

Chromatin licensing and DNA replication factor 1 as a potential prognostic and diagnostic biomarker for gastric cancer

Accurate and timely genetic material replication is essential for preserving genomic integrity. The replication process begins with chromatin licensing and DNA replication factor 1 (CDT1). It has been demonstrated that dysregulated CDT1 expression causes genomic instability, damages DNA, and may even cause cancer. Although this protein is overexpressed in several malignancies, its significance in gastric cancer is still unknown. This work aims to investigate the biological function and molecular mechanism of CDT1 in gastric cancer. Bioinformatics studies were carried out using TCGA database, Kaplan–Meier and GEPIA online data analysis software. The expression of CDT1 in stomach cancer tissues was determined by immunohistochemistry, and its relationship to clinicopathological features was examined. Using siRNA and cell function experiments, the impact of CDT1 expression on the capacity of gastric cancer cell lines to proliferate, migrate, and invade was investigated. In gastric cancer tissues, CDT1 is overexpressed and is linked to a dismal prognosis. Through the up-regulation of M-cyc, MCM5 (microchromosome maintenance protein 5), CyclinD1, N-cadherin, and the down-regulation of E-cadherin, CDT1 facilitated the proliferation and invasion of gastric cancer cells. The study’s findings shed insight on the possible molecular processes behind CDT1’s function in promoting the start and progression of gastric cancer and demonstrate the potential predictive and diagnostic use of CDT1 expression in gastric cancer.

Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models

This study investigates the use of machine learning (ML) models combined with explainable artificial intelligence (XAI) techniques to identify the most influential genes in the classification of five recurrent cancer types in women: breast cancer (BRCA), lung adenocarcinoma (LUAD), thyroid cancer (THCA), ovarian cancer (OV), and colon adenocarcinoma (COAD). Gene expression data from RNA-seq, extracted from The Cancer Genome Atlas (TCGA), were used to train ML models, including decision trees (DTs), random forest (RF), and XGBoost (XGB), which achieved accuracies of 98.69%, 99.82%, and 99.37%, respectively. However, the challenges in this analysis included the high dimensionality of the dataset and the lack of transparency in the ML models. To mitigate these challenges, the SHAP (Shapley Additive Explanations) method was applied to generate a list of features, aiming to understand which characteristics influenced the models’ decision-making processes and, consequently, the prediction results for the five tumor types. The SHAP analysis identified 119, 80, and 10 genes for the RF, XGB, and DT models, respectively, totaling 209 genes, resulting in 172 unique genes. The new list, representing 0.8% of the original input features, is coherent and fully explainable, increasing confidence in the applied models. Additionally, the results suggest that the SHAP method can be effectively used as a feature selector in gene expression data. This approach not only enhances model transparency but also maintains high classification performance, highlighting its potential in identifying biologically relevant features that may serve as biomarkers for cancer diagnostics and treatment planning.

Diagnostic and therapeutic role of non-coding RNAs regulating programmed cell death in melanoma.

lncRNAs (long non-coding RNAs) are heterogeneous RNA molecules that modulate various cellular processes, such as proliferation, differentiation, migration, invasion, and apoptosis, via different mechanisms. An increasing amount of research indicates that abnormal expression of lncRNA influences the development of drug resistance as well as the genesis and advancement of cancer, including melanoma. Furthermore, they are attractive biomarkers for non-invasive cancer diagnostics due to their strongly modulated expression and improved tissue and disease specificity. This review offers a succinct overview of the present understanding concerning the potential diagnostic biomarker potential of lncRNAs in melanoma. Cell death occurs frequently during growth and throughout life and is an active, organized, and genetically determined process. It is essential for the regulation of homeostasis. Controlled cell death and non-programmed cell death are both forms of cell death. The most prevalent forms of regulatory cell death are pyroptosis, ferroptosis, autophagy, necroptosis, necrosis, and apoptosis. Ferroptosis, pyroptosis, and autophagy are less common forms of cell death compared to necrosis, apoptosis, and necroptosis. ncRNAs are regulatory RNA molecules that are not involved in encoding proteins. They primarily consist of circular RNAs (circ RNAs), lncRNAs, and microRNAs (miRNAs). Moreover, non-coding RNAs have the ability to modulate tumor cell autophagy, pyroptosis, and ferroptosis at the transcriptional or post-transcriptional stage, as well as function as oncogenes and tumor suppressor genes, which can have considerable effects on the incidence and growth of tumors. This review concentrated on the recent advancements in the research of the diagnostic and therapeutic functions of ncRNAs in the regulation of programmed cell death in melanoma.

Comparative analysis of extracellular vesicles miRNAs (EV-miRNAs) and cell-free microRNAs (cf-miRNAs) reveals that EV-miRNAs are more promising as diagnostic and prognostic biomarkers for prostate cancer.

MicroRNAs can be found intracellularly incorporated into extracellular vesicles (EV-miRNAs) or extracellularly as cell-free miRNAs (cf-miRNAs). This study aimed to compare the diagnostic and prognostic potential of four miRNAs with recognized roles in prostate cancer as cf-miRNAs and EV-miRNAs, obtained from liquid biopsies (LB). Total RNA was isolated from whole plasma and plasma EVs from 15 controls (CTR) and 30 patients (20 with localized prostate cancer (PCa), 10 with metastatic prostate cancer (mPCa)). The miRNAs were quantified by RT-qPCR and the relative expression of these miRNAs was compared between the three groups, and their associations with clinicopathological parameters were assessed. Receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic potential of the miRNAs in discriminating different groups. Overall, EV-miRNAs showed higher expression compared to cf-miRNAs. All EV-miRNAs analyzed showed diagnostic potential with an area under the curve (AUC) above 0.744. EV-miR-21-5p, EV-miR-375-3p, and EV-miR-1290-3p were overexpressed in PCa and mPCa compared to CTR, while EV-miR-200c-3p was overexpressed only in mPCa in comparison to CTR. Remarkably, EV-miR-375-3p and EV-miR-1290-3p could differentiate mPCa with ISUP ≥ 3, demonstrating their prognostic potential. In addition, EV-miR-1290-3p and EV-4-miR-panel detected patients with PSA > 10 ng/mL. Cf-miRNAs performed lower than EV-miRNAs, which can be explained by the greater stability and specificity of EV-miRNAs, making them superior to cf-miRNA. The results show that LB, a non-invasive strategy, is clinically feasible to identify EV-miRNAs as biomarkers for PCa and may provide additional information for assessing PCa risk stratification.

Circulating TERT serves as the novel diagnostic and prognostic biomarker for the resectable NSCLC

BackgroundTelomerase reverse transcriptase (TERT) is a catalytic subunit of telomerase and required for cancer development. This study aims to reveal its clinical utility for diagnosis and prognosis of resectable NSCLC.MethodsTERT was quantitatively evaluated by the enzyme-linked immunosorbent assay (ELISA) from 69 patients before and after the surgery. The prognostic value was evaluated by disease-free survival (DFS) and overall survival (OS).ResultsCirculating TERT in NSCLC patients were significantly higher than that in the healthy group, possessing the AUC of 0.90. Importantly, TERT change between pre- and post- operation was significantly correlated with OS and DFS (p = 0.022, p = 0.046 respectively), acted as the independent prognostic factors for DFS and OS, indicating it can serve as the promising diagnostic and prognostic biomarker for resectable non-small cell lung cancer (NSCLC).ConclusionsTERT change between pre- and post- resection can serve as the promising biomarker for prognosis of resectable NSCLC.

Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) represents one of the most prevalent forms of lung cancer, with a five-year survival rate of 21.7%. There is an urgent need to identify pertinent biomarkers to inform the diagnosis and prognosis of tumors, particularly those that can be applied to different age groups. Herein, we would apply machine learning methods to specifically analyze the issue of biomarker applicability across different age groups in NSCLC.MethodsStudies have shown a higher incidence of NSCLC in people over 40 years of age, and due to the limitations of data set, studies of individuals under 40 years of age were not included in this study. To simulate the human aging model as closely as possible, we gathered corresponding non-small cell lung cancer (NSCLC) samples from the UCSC Xena database based on patient age information. These samples were then categorized into three groups: 40–60, 60–80, and over 80 years old. Subsequently, we employed four machine learning methods—Random Forest, LASSO regression analysis, XGBoost, and GBM—to identify gene sets with significant diagnostic value for each age group. By taking the intersection of these sets, we identified the optimal gene and assessed its prognostic significance in NSCLC. Then, the diagnostic value of CAT gene was validated using global public databases, including the GSE32863, GSE43458, GSE68571, GSE10072, and GSE63459 datasets from the Americas, the GSE30219 and GSE102511 datasets from Europe, and the GSE31210 and GSE19804 datasets from Asia. Furthermore, immunohistochemical staining was performed in an independent cohort from a tissue microarray. Additionally, cell culture and RT-qPCR were employed for external validation.ResultsThrough the implementation of machine learning methods, we successfully identified the catalase (CAT) gene. Our analysis revealed that individuals with high expression of the CAT gene experienced improved survival rates. Additionally, these individuals exhibited elevated immune scores. We further discovered that the CAT gene synergizes with multiple components of neutrophils, including TLRs, FcRn, and the selective GEF of Rho-family GTPases. In addition, we identified a potential immune checkpoint, TNFSF15, which is applicable to the human aging model. Finally, we validated the CAT gene's diagnostic value using databases encompassing the Americas, Europe, and Asia regions. Through external RT-qPCR validation, we verified that CAT expression in BEAS-2B was higher than that of A549. In an independent human cohort, we also verified that CAT is lowly expressed in lung cancer tissues. In addition, higher CAT levels were associated with improved survival in the 40–60 and 60–80 age groups.ConclusionsIn our analysis of the NSCLC database, we pinpointed the CAT gene, which holds promise for potential diagnostic and prognostic applications in the context of human aging. Furthermore, it may offer insights into addressing age-related heterogeneity of NSCLC.