Identification Of Cancer Biomarkers Research Articles

A novel label free electrochemical aptasensor based on poly(anthranilic acid-Co-aniline) for detection of miRNA-155, a cancer biomarker

Traditional electrochemical biosensors often rely on expensive materials like gold nanoparticles within their electrode structures. In contrast to existing methods, this research introduces a novel, groundbreaking, label-free aptasensor. This sensor comprises simple and inexpensive materials, making it accessible and budget-conscious. It boasts exceptional sensitivity, allowing for the identification of leukemia cancer biomarker (miRNA-155) in its early stages. The proposed aptasensor consisted of a copolymer of poly anthranilic acid and polyaniline on the graphite sheet, in which the receptor of miRNA-155 was self-assembled on the copolymer. The substrate of receptors has benefits, including high electrical conductivity, excellent stability, good biocompatibility, and simple synthesis. The important variables affecting the adequacy of the designed aptasensor were examined in the presence of [Fe(CN)6]3-/4- as a redox probe. The proposed aptasensor was successfully used in human blood plasma. Due to the ease of preparation, cost, and time effectiveness, the proposed aptasensor is promising for mass production. Finally, at optimum conditions, the sensor exhibited a linear response within a concentration range of 0.1nM to 1 aM with a detection limit of 1 aM.

Multiomics biomarkers were not superior to clinical variables for pan-cancer screening

Abstract Background Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data. Methods We utilize multi-omics data from 500,000 participants in the UK Biobank. Machine learning is applied to search for proteins, metabolites, genetic variants, or clinical variables to diagnose cancers collectively and individually. Results Here we show that the overall performance of the potential blood biomarkers do not outperform clinical variables for collective diagnosis. However, we observe promising results for individual cancers in close proximity to peripheral blood, with an Area Under the Curve (AUC) greater than 0.8. Conclusions Our findings suggest that the identification of blood biomarkers for cancer might be complicated by variable overlap between molecular changes in tumor tissues and peripheral blood. This explanation is supported by local proteomics analyses of different tumors, which all show high AUCs, greater than 0.9. Thus, multi-omics biomarkers for the diagnosis of individual cancers may potentially be effective, but not for groups of cancers.

Identification of Non-Coding RNA Biomarkers in Non-Small Cell Lung Cancer to Address Racial Disparities.

Lung cancer significantly impacts mortality, with African Americans (AAs) showing higher rates than White Americans (WAs). Noncoding RNAs (ncRNAs) play a crucial role in lung tumorigenesis. To identify ncRNA biomarkers associated with racial disparities in lung cancer, we used droplet digital PCR to examine 93 lung cancer-associated ncRNAs in plasma and sputum samples from participants with AA and WA, including 118 patients and 92 cancer-free smokers. In the AA population, plasma showed differential expression of ten ncRNAs, while sputum revealed four ncRNAs when comparing lung cancer patients to the control group. In the WA population, the plasma displayed 11 ncRNAs, and the sputum had five ncRNAs showing differential expression between lung cancer patients and the control group. For AAs, we identified a three-ncRNA panel (plasma miRs-147b, 324-3p, 422a) for diagnosing lung cancer in AAs with 86% sensitivity and 89% specificity. For WAs, a four-ncRNA panel comprising sputum miR-34a-5p and plasma miRs-103-3p, 126-3p, and 205-5p was developed, achieving 88% sensitivity and 87% specificity. These panels remained effective across various stages and types of lung tumors, and were validated in an independent cohort of 56 patients and 72 controls. Ethnicity-related ncRNA signatures hold promise as biomarkers for tackling racial disparities in patients with lung cancer.

Some Glycoproteins Expressed on the Surface of Immune Cells and Cytokine Plasma Levels Can Be Used as Potential Biomarkers in Patients with Colorectal Cancer

Colorectal cancer (CRC) is a leading cause of mortality worldwide. Its incidence holds a major position among the most common life-threatening diseases. Hence, the early identification and precise characterization of disease activity based on proper biomarkers are of utmost importance for therapeutic strategy and patient survival. The identification of new biomarkers for colorectal cancer or disease-specific levels/combinations of biomarkers will significantly contribute to precise diagnosis and improved personalized treatment of patients. Therefore, the present study aims to identify colorectal cancer-specific immunological biomarkers. The plasma levels of several cytokines (interleukin-1β /IL-1β/, IL-2, IL-4, IL-10, IL-12, IL-15, TGFβ and IFNγ) of 20 patients with colorectal cancer and 21 healthy individuals were determined by ELISA. The expression of several types of glycoproteins on the surface of peripheral blood leukocytes isolated from CRC patients and healthy volunteers was evaluated by flow cytometry. Correlations between cytokine levels and cell surface glycoprotein expression were analyzed. The obtained results demonstrated significantly elevated levels of CD80, CD86, CD279 and CD274 expressing leukocyte populations in the cancer patient group, while the numbers of NK cells and CD8- and CD25-positive cells were decreased. Based on these data and the correlations with cytokine levels, it can be concluded that CD25, CD80, CD86, CD274 and CD279 glycoproteins combined with specific plasma levels of IL-1β, IL-2, IL-15 and TGFβ could represent potential biomarkers for colorectal cancer.

Paper-Based DNA Biosensor for Rapid and Selective Detection of miR-21.

Cancer is the second leading cause of death globally, with 9.7 million fatalities in 2022. While routine screenings are vital for early detection, healthcare disparities persist, highlighting the need for equitable solutions. Recent advancements in cancer biomarker identification, particularly microRNAs (miRs), have improved early detection. MiR-21 is notably overexpressed in various cancers and can be a valuable diagnostic tool. Traditional detection methods, though accurate, are costly and complex, limiting their use in resource-limited settings. Paper-based electrochemical biosensors offer a promising alternative, providing cost-effective, sensitive, and rapid diagnostics suitable for point-of-care use. This study introduces an innovative electrochemical paper-based biosensor that leverages gold inkjet printing for the quantitative detection of miR-21. The biosensor, aimed at developing cost-effective point-of-care devices for low-resource settings, uses thiolated self-assembled monolayers to immobilize single-stranded DNA-21 (ssDNA-21) on electrodeposited gold nanoparticles (AuNPs) on the printed gold surface, facilitating specific miR-21 capture. The hybridization of ssDNA-21 with miR-21 increases the anionic barrier density, impeding electron transfer from the redox probe and resulting in a current suppression that correlates with miR-21 concentration. The biosensor exhibited a linear detection range from 1 fM to 1 nM miR-21 with a sensitivity of 7.69 fM µA-1 cm2 and a rapid response time (15 min). With a low detection limit of 0.35 fM miR-21 in serum, the biosensor also demonstrates excellent selectivity against interferent species. This study introduces an electrochemical paper-based biosensor that uses gold inkjet printing to precisely detect miR-21, a key biomarker overexpressed in various cancers. This innovative device highlights the potential for cost-effective, accessible cancer diagnostics in underserved areas.

Performance of Predictive Models in Cervical Cancer Recurrence: A Systematic Review and Meta-Analysis of Biomarkers and Prognosis

This study investigates the use of Predictive Analytics Frameworks (PAF) for identifying biomarkers of recurrent cervical cancer and predicting prognosis. It addresses the limited comprehensive evaluations of the effectiveness of predictive models in this area, despite the growing application of machine learning in healthcare. The purpose of this systematic review and meta-analysis is to assess the performance of predictive analytics models in terms of sensitivity, accuracy, specificity, and the area under the curve (AUC-ROC) for identifying cervical cancer biomarkers and predicting prognosis. To address this research problem, a systematic review and meta-analysis was conducted, covering studies published between 2014 and 2024. A total of 1,515 studies were initially identified from the PubMed and Scopus databases, with 50 research studies meeting the inclusion criteria. Repeated measures ANOVA and meta-analysis were applied using data collected over an 8-year period to evaluate recurrence trends and the predictive power of various models. The findings suggest that predictive analytics models show significant potential for improving diagnostic accuracy in identifying cervical cancer biomarkers. However, the review also highlights several limitations, including the small number of included studies, heterogeneity across studies, and potential bias in retrospective analyses. In conclusion, while predictive analytics frameworks demonstrate promise in improving cervical cancer prognosis and biomarker identification, further research is required to validate these findings and assess their broader clinical utility. The study underscores the importance of continued exploration of predictive models to enhance decision-making in oncology.

A Controllability Reinforcement Learning Method for Pancreatic Cancer Biomarker Identification.

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

Application of meta-analysis for determining cancer biomarkers

The health care professionals are facing the challenge to combine and translate the findings from a plethora of, often conflicting, clinical trials or clinical studies in order to reach an evidence-based decision. The application of a meta-analytical approach in the medical field allows the systematic synthesis and assessment of the results across studies to draw conclusions about the main body of the research, such as a more accurate estimate of treatment effect or determining disease risk factors. Herein, we review the advantages and the basic steps of meta-analysis towards the identification of powerful cancer biomarkers.

Identification and validation of diagnostic and prognostic biomarkers in prostate cancer based on WGCNA

BackgroundProstate cancer (PCa) represents a significant health challenge for men, and the advancement of the disease often results in a grave prognosis for patients. Therefore, the identification of biomarkers associated with the diagnosis and prognosis of PCa holds paramount importance in patient health management.MethodsThe datasets pertaining to PCa were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to investigate the modules specifically associated with the diagnosis of PCa. The hub genes were identified using the LASSO regression analysis. The expression levels of these hub genes were further validated by qRT-PCR experiments. Receiver operating characteristic (ROC) curves and nomograms were employed as evaluative measures for assessing the diagnostic value.ResultsThe blue module identified by WGCNA exhibited a strong association with PCa. Six hub genes (SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3) were identified by LASSO regression analysis. Further verification confirmed that these six genes were significantly downregulated in tumor tissues and cells. The six hub genes and the nomogram demonstrated substantial diagnostic value, with area under the curve (AUC) values ranging from 0.754 to 0.961. Moreover, patients with low expression levels of these six genes exhibited elevated T/N pathological stage and Gleason score, implying a more advanced disease state. Meanwhile, their progression-free survival (PFS) was observed to be potentially poorer. Finally, a significant association could be observed between the expression of these genes and the dysregulation of immune cells, along with drug sensitivity.ConclusionsIn summary, our study identified six hub genes, namely SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3, which can be utilized to establish a diagnostic model for PCa. The discovery may offer potential molecular targets for clinical diagnosis and treatment of PCa.

Identification of Biomarkers for Endometrial Cancer Based on Natural Killer Cell-Related Genes

Endometrial cancer (EC) is a malignant tumor. Natural killer (NK) cells play a crucial role in various cancers, but their role in EC is unclear. To this purpose, in this paper, differential expression analysis was performed on transcriptome data from the TCGA database, and the obtained DEGs and the collected NRGs were intersected, and single-factor Cox regression analysis and Lasso-Cox regression analysis were performed on the intersected genes to obtain prognosis-related genes and risk model, respectively. These genes and models were validated by Kaplan-Meier (KM) survival curve analysis and ROC analysis on the internal and external test sets. In addition, nomogram models were constructed based on prognosis-associated genes, sample risk scores, and clinical factors. Finally, we explored the immune landscape of high- and low-risk groups of EC. The results showed that the risk models constructed in this paper exhibited excellent predictive effects, which will facilitate research on the precision treatment of EC. There were significant differences in prognosis, immune cell infiltration abundance, immune checkpoint-associated genes, and HLA gene expression between high- and low-risk groups of EC. The risk model in this paper can provide a reference for the personalized treatment of EC. In addition, we performed RT-qPCR to validate the levels of genes significantly associated with prognosis.

Identification of novel prognostic biomarkers for bladder cancer in Egyptian patients using mass spectrometry

BackgroundProteomic biomarkers are considered a central analytical method that improves the outcome of early progression and diagnosis with high accuracy in bladder cancer (BC). Currently, several proteins are detected in plasma and tissues using proteomic techniques, making it challenging to compare and identify common pathways and proteins. MethodUsing mass spectrometry, which can analyze multiple proteins with specific biological considerations and technical implications with high sensitivity and specificity, we identified clinically relevant protein biomarkers in tissue and plasma samples from healthy controls and patients with bladder cancer. The results were analyzed using bioinformatics to evaluate several proteins in the tissue and plasma. ResultsWe identified six candidate biomarkers (GSN, HP, SERPINA1, A2M, HBB, and HBD) that were correlated with increased tumor progression in the Egyptian population. ConclusionOur research focused on genes that are upregulated or downregulated. This indicated that A2M and SERPINA1 could serve as more specific upregulated biomarkers for plasma cancer samples, while GSN, HBB, HBD, and HP could be the downregulated ones for tissue cancer samples. Among patients with bladder cancer, different proteins have been reported to be under different regulations as compared to healthy controls. Thus, there is still a need for further studies in order to ascertain their functionality and treatment prospects, as well as to underscore their significance in pathogenesis as early predictor biomarkers.

Differential extracellular vesicle concentration and their biomarker expression of integrin αv/β5, EpCAM, and glypican-1 in pancreatic cancer models

Tumor-derived extracellular vesicles (EVs) show great potential as biomarkers for several diseases, including pancreatic cancer, due to their roles in cancer development and progression. However, the challenge of utilizing EVs as biomarkers lies in their inherent heterogeneity in terms of size and concentration, making accurate quantification difficult, which is highly dependent on the isolation and quantification methods used. In our study, we compared three EV isolation techniques and two EV quantification methods. We observed variations in EV concentration, with approximately 1.5-fold differences depending on the quantification method used. Interestingly, all EV isolation techniques consistently yielded similar EV quantities, overall size distribution, and modal sizes. In contrast, we found a notable increase in total EV amounts in samples from pancreatic cancer cell lines, mouse models, and patient plasma, compared to non-cancerous conditions. Moreover, individual tumor-derived EVs exhibited at least a 3-fold increase in several EV biomarkers. Our data, obtained from EVs isolated using various techniques and quantified through different methods, as well as originating from various pancreatic cancer models, suggests that EV profiling holds promise for the identification of unique and cancer-specific biomarkers in pancreatic cancer.

Membrane protein guided and ATP-gated dual module DNA nanodevice for amplified fluorescence labeling of cancer cells

The occurrence and progression of tumors are accompanied by changes in both the microenvironment and cancer cells. Therefore, the detection of extracellular alert molecule accumulation, as well as the identification of abnormal cancer biomarkers both contribute to the accurate localization of individual cancer cells. Here, we have designed a cancer cell fluorescence labeling nanodevice with a membrane protein targeting domain and extracellular ATP-fueled amplification switch. The AND logic-based nanodevice light-up the cancer cell membrane only in the coexistence of target membrane proteins and abnormal ATP concentration. We demonstrated the ability of the nanodevice to selectively label target cells in a cell mixture supplemented with ATP at concentrations within the range in the tumor microenvironment (1–5×10−4 M). Additionally, the modular design of DNA nanodevices enables broad application to different cancer cells by replacing them with corresponding membrane protein aptamers. Our fluorescence labeling nanodevice with signal amplification shows promise in serving as an imaging tool for visualizing the impact of ATP regulation on cancer evolution.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 p73 and p53 p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

PBMCs as Tool for Identification of Novel Immunotherapy Biomarkers in Lung Cancer.

Lung cancer (LC), including both non-small (NSCLC) and small (SCLC) subtypes, is currently treated with a combination of chemo- and immunotherapy. However, predictive biomarkers to identify high-risk patients are needed. Here, we explore the role of peripheral blood mononuclear cells (PBMCs) as a tool for novel biomarkers searching. We analyzed the expression of the cGAS-STING pathway, a key DNA sensor that activates during chemotherapy, in PBMCs from LC patients divided into best responders (BR), responders (R) and non-responders (NR). The PBMCs were whole exome sequenced (WES). PBMCs from BR and R patients of LC cohorts showed the highest levels of STING (p < 0.0001) and CXCL10 (p < 0.0001). From WES, each subject had at least 1 germline/somatic alteration in a DDR gene and the presence of more DDR gene mutations correlated with clinical responses, suggesting novel biomarker implications. Thus, we tested the effect of the pharmacological DDR inhibitor (DDRi) in PBMCs and in three-dimensional spheroid co-culture of PBMCs and LC cell lines; we found that DDRi strongly increased cGAS-STING expression and tumor infiltration ability of immune cells in NR and R patients. Furthermore, we performed FACS analysis of PBMCs derived from LC patients from the BR, R and NR cohorts and we found that cytotoxic T cell subpopulations displayed the highest STING expression. cGAS-STING signaling activation in PBMCs may be a novel potential predictive biomarker for the response to immunotherapy and high levels are correlated with a better response to treatment along with an overall increased antitumor immune injury.

Identification and Verification of Potential Ferroptosis-Related Biomarkers in Cervical Cancer

This study screened important genes contributing to morbidity from differential ferroptosis-related genes (FRGs) in cervical cancer and to establish a risk assessment model with ferroptosis-related LncRNAs. Total RNA sequencing data were extracted from The cancer genome atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx). By differential analysis, we identified ferroptosis-related hub genes close to prevalence of cervical cancer. According to receiver operator curves (ROC) curves, hub genes have good diagnostic performance. The diagnostic potential of hub genes for occurrence of the disease was further assessed and verified. Further, a risk-assessing model based on ferroptosis-related LncRNAs was established. Finally, the differential expressions of hub genes were verified through qRT-PCR. Five hub genes were identified, and we found through GO, KEGG and immune infiltration, that the hub genes are connection with cervical cancer. The Area Under Curve (AUC) values were all greater than 0.8 in ROC curve, and the hub genes presented differences between disease and control groups in validation set GSE29570. We created a risk assessment model with 16 ferroptosis-related LncRNAs. There was a difference in survival between high-risk and low-risk groups. The AUC result for risk assessment model reached 0.792, and there were significant expression differences of Hub genes in Huvec and Hela cells. The study screened 5 hub genes and constructed the risk-assessment model based on 16 LncRNAs associated with ferroptosis genes.

Identification of immune-related prognostic biomarkers in triple-negative breast cancer.

Triple-negative breast cancer (TNBC), a type of breast cancer, lacks immune-related markers that can be used for prognosis or prediction. Therefore, we created a predictive framework for TNBC using a risk assessment. Our previous study group consisted of 360 individuals who were diagnosed with TNBC through pathology using RNA sequencing and had clinical data from Fudan University Shanghai Cancer Center (FUSCC). A risk scoring model was constructed using the Cox regression method with the least absolute shrinkage and selection operator (LASSO). A multivariate Cox regression analysis was utilized to develop the prediction model, which was then assessed using the consistency index and calibration plots. The validation cohort of The Cancer Genome Atlas (TCGA) TNBC confirmed the strength of the signatures' predictive value. The prognostic risk score model included 12 genes: TDO2, CHIT1, CARML2, HLA-C, ADIRF, C19orf33, CA8, AHNAK2, RHOV, OPLAH, THEM6, and NEBL. The receiver operator characteristic (ROC) curves for survivability values at 1, 3, and 5 years in the FUSCC TNBC cohort demonstrated area under the curve (AUC) values of 0.78, 0.83, and 0.75, respectively. These results indicated a high level of accuracy in predicting outcomes, which was further confirmed through validation using TCGA database. The patients in the high-risk group showed worse prognoses and lower levels of immune cell infiltration, specifically CD8+ T cells, than those in the low-risk group. Furthermore, the low-risk group exhibited a significant upregulation of genes that encode immune checkpoints, including CD274 and CTLA4, suggesting that immunotherapy may yield enhanced efficacy within this particular group. In conclusion, the prognostic signature consisting of 12 genes can assist in the choice of immunotherapy for TNBC.

DNA Methylation Biomarkers in Cancer Diagnostics

Abstract Cancer development and progression are characterized by intricate genetic and epigenetic alternations, with DNA methylation playing a crucial role in this transformative process. Recent advancements in DNA methylation mapping technologies have significantly contributed to the identification of novel cancer biomarkers. We provide a short overview of Conformité Européenne-marked in vitro diagnostic tests designed to detect DNA methylation changes in oncology practice.

Identification of volatile biomarkers for lung cancer from different histological sources: A comprehensive study

The identification of noninvasive volatile biomarkers for lung cancer is a significant clinical challenge. Through in vitro studies, the recognition of altered metabolism in cell volatile organic compound (VOC) emitting profile, along with the occurrence of oncogenesis, provides insight into the biochemical pathways involved in the production and metabolism of lung cancer volatile biomarkers. In this research, for the first time, a comprehensive comparative analysis of the volatile metabolites in NSCLS cells (A549), SCLC cells (H446), lung normal cells (BEAS-2B), as well as metabolites in both the oxidative stress (OS) group and control group. Specifically, the combination of eleven VOCs, including n-dodecane, acetaldehyde, isopropylbenzene, p-ethyltoluene and cis-1,3-dichloropropene, exhibited potential as volatile biomarkers for lung cancer originating from two different histological sources. Furthermore, the screening process in A549 cell lines resulted in the identification of three exclusive biomarkers, isopropylbenzene, formaldehyde and bromoform. Similarly, the exclusive biomarkers 1,2,4-trimethylbenzene, p-ethyltoluene, and cis-1,3-dichloropropene were present in the H446 cell line. Additionally, significant changes in trans-2-pentene, acetaldehyde, 1,2,4-trimethylbenzene, and bromoform were observed, indicating a strong association with OS. These findings highlight the potential of volatile biomarkers profiling as a means of noninvasive identification for lung cancer diagnosis.

Abstract 4880: Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics

Abstract This work aims to decipher p73-regulated biomarkers for a prompt diagnosis of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling technologies. Transcriptome profile of HCT116 cell line p53−/− p73+/+ and p53−/− p73 knockdown was performed to identify differentially expressed genes (DEGs) followed by cross-checking with three CRC tissue expression datasets available in Gene Expression Omnibus. KEGG and Gene ontology were performed on differentially expressed transcripts obtained via the transcriptome profile and intersected genes. The PPI network was constructed via Cytoscape to extract hub genes. KM plots assisted in investigating the prognostic significance of the hub genes. The clinicopathological relevance was explored using the GEPIA and UALCAN databases. Finally, machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1,289 upregulated and 1,897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. The analysis of gene ontology and KEGG showed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan Meier, GEPIA, and UALCAN analyses uncovered the prognostic and diagnostic relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. This is a novel study utilizing transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-relevant genes. These genes are found relevant regarding the patients’ prognosis and diagnosis. The unveiled biomarkers are also found robust in TNM-stage prediction. This is the first study where transcriptomics, publicly available tissue datasets, and machine learning are altogether employed to reveal key genes in CRC pathogenesis. Our research highlights the robust analysis of four independent data sets to find the key hub genes, further predicting the performance of the key genes in stage-wise classification. Citation Format: Chanchal Bareja, Kountay Dwivedi, Apoorva Uboveja, Ankit Mathur, Naveen Kumar, Daman Saluja. Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4880.