The identification of biomarkers for Alzheimer's disease using a systems biology approach based on lncRNA-circRNA-miRNA-mRNA ceRNA networks.

In addition to being the most prevalent form of neurodegeneration among the elderly, AD is a devastating multifactorial disease. Currently, treatments address only its symptoms. Several clinical studies have shown that the disease begins to manifest decades before the first symptoms appear, indicating that studying early changes is crucial to improving early diagnosis and discovering novel treatments. Our study used bioinformatics and systems biology to identify biomarkers in AD that could be used for diagnosis and prognosis. The procedure was performed on data from the GEO database, and GO and KEGG enrichment analysis were performed. Then, we set up a network of interactions between proteins. Several miRNA prediction tools including miRDB, miRWalk, and TargetScan were used. The ceRNA network led to the identification of eight mRNAs, four circRNAs, seven miRNAs, and seven lncRNAs. Multiple mechanisms, including the cell cycle and DNA replication, have been linked to the promotion of AD development by the ceRNA network. By using the ceRNA network, it should be possible to extract prospective biomarkers and therapeutic targets for the treatment of AD. It is possible that the processes involved in DNA cell cycle and the replication of DNA contribute to the development of Alzheimer's disease.

Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes and screening chemical libraries.

Introduction: Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem. Methods: In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis. Results: The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 - 40 folds. Conclusion: The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.

The role of MAPK, notch and Wnt signaling pathways in papillary thyroid cancer: Evidence from a systematic review and meta-analyzing microarray datasets employing bioinformatics knowledge and literature.

Papillary thyroid cancer (PTC) is a prevalent kind of thyroid cancer (TC), with the risk of metastasis increasing faster than any other malignancy. So, understanding the role of PTC in pathogenesis requires studying the various gene expressions to find out which particular molecular biomarkers will be helpful. The authors conducted a comprehensive search on the PubMed microarray database and a meta-analysis approach on the remaining ones to determine the differentially expressed genes between PTC and normal tissues, along with the analyses of overall survival (OS) and recurrence-free survival (RFS) rates in patients with PTC. We considered the associated genes with MAPK, Wnt, and Notch signaling pathways. Two GEO datasets have been included in this research, considering inclusion and exclusion criteria. Nineteen genes were found to have higher differences through the meta-analysis procedure. Among them, ten genes were upregulated, and nine genes were downregulated. The expression of 19 genes was examined using the GEPIA2 database, and the Kaplan-Meier plot statistics were used to analyze RFS and the OS rates. We discovered seven significant genes with the validation: PRICKLE1, KIT, RPS6KA5, GADD45B, FGFR2, FGF7, and DTX4. To further explain these findings, it was discovered that the mRNA expression levels of these seven genes and the remaining 12 genes were shown to be substantially linked with the results of the experimental literature investigations on the PTC. Our research found nineteen panels of genes that could be involved in the PTC progression and metastasis and the immune system infiltration of these cancers.

A review of the literature on the use of CRISPR/Cas9 gene therapy to treat hepatocellular carcinoma.

Noncoding RNAs instruct the Cas9 nuclease to site-specifically cleave DNA in the CRISPR/Cas9 system. Despite the high incidence of hepatocellular carcinoma (HCC), the patient's outcome is poor. As a result of the emergence of therapeutic resistance in HCC patients, clinicians have faced difficulties in treating such tumor. In addition, CRISPR/Cas9 screens were used to identify genes that improve the clinical response of HCC patients. It is the objective of this article to summarize the current understanding of the use of the CRISPR/Cas9 system for the treatment of cancer, with a particular emphasis on HCC as part of the current state of knowledge. Thus, in order to locate recent developments in oncology research, we examined both the Scopus database and the PubMed database. The ability to selectively interfere with gene expression in combinatorial CRISPR/Cas9 screening can lead to the discovery of new effective HCC treatment regimens by combining clinically approved drugs. Drug resistance can be overcome with the help of the CRISPR/Cas9 system. HCC signature genes and resistance to treatment have been uncovered by genome-scale CRISPR activation screening, although this method is not without limitations. It has been extensively examined whether CRISPR can be used as a tool for disease research and gene therapy. CRISPR and its applications to tumor research, particularly in HCC, are examined in this study through a review of the literature.

A review on in silico virtual screening methods in COVID-19 using anticancer drugs and other natural/chemical inhibitors.

The present coronavirus disease 2019 (COVID-19) pandemic scenario has posed a difficulty for cancer treatment. Even under ideal conditions, malignancies like small cell lung cancer (SCLC) are challenging to treat because of their fast development and early metastases. The treatment of these patients must not be jeopardized, and they must be protected as much as possible from the continuous spread of the COVID-19 infection. Initially identified in December 2019 in Wuhan, China, the contagious coronavirus illness 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Finding inhibitors against the druggable targets of SARS-CoV-2 has been a significant focus of research efforts across the globe. The primary motivation for using molecular modeling tools against SARS-CoV-2 was to identify candidates for use as therapeutic targets from a pharmacological database. In the published study, scientists used a combination of medication repurposing and virtual drug screening methodologies to target many structures of SARS-CoV-2. This virus plays an essential part in the maturation and replication of other viruses. In addition, the total binding free energy and molecular dynamics (MD) modeling findings showed that the dynamics of various medications and substances were stable; some of them have been tested experimentally against SARS-CoV-2. Different virtual screening (VS) methods have been discussed as potential means by which the evaluated medications that show strong binding to the active site might be repurposed for use against SARS-CoV-2.

A meta-analysis of systematic reviews and meta-analyses to evaluate the psychological consequences of COVID-19.

BACKGROUND: Several meta-analysis studies have been reported in the literature on the incidence of psychopathological conditions resulting from the COVID-19 pandemic. This investigation aims to compile and analyze the findings of previously published meta-analysis research, as shown by the present meta-analysis of previous meta-analysis studies. METHODS: The PubMed and Scopus databases were searched from 1 January 2019 to 30 May 2022. The procedure was carried out according to the PRISMA flow chart and the qualities of the identified studies were analyzed using AMSTAR 2. Heterogeneities and risk of bias were assessed using the Meta-MUMS tool. The corresponding results, forest and funnel plots of the psychological consequences of COVID-19 were synthesized. RESULTS: Eleven meta-analysis studies were included. Random-effects meta-analysis of anxiety and depression showed (ER = 0.318 p-value < 0.001, ER = 0.295 p-value < 0.001) high heterogeneities (I2 = 99.70%, I2 = 99.75) between studies. Random-effects meta-analyses of sleep difficulties and insomnia were shown (ER = 0.347 p-value < 0.001, ER = 0.265, p-value < 0.001) along with heterogeneities (I2 = 99.89, I2 = 99.64). According to the random meta-analysis of post-traumatic stress syndrome (PTSS) and post-traumatic stress disorder (PTSD) (ER = 0.246, p-value = 0.001, ER = 0.223 p-value < 0.001) with heterogeneities (I2 = 99.75, I2 = 99.17). Random-effects meta-analyses of somatic and fear symptoms have been shown (ER = 0.16 p-value < 0.001, ER = 0.41, p-value = 0.089) with high heterogeneities (I2 = 99.62, I2 = 98.63). Random-effects meta-analysis of obsessive-compulsive symptoms and distress (ER = 0.297 p-value = 0.103; ER = 0.428, p-value = 0.013) with high heterogeneity, as I2 = 99.38%. Subgroup analysis of all symptoms and Egger's tests for detecting publication bias were also assessed. CONCLUSION: The data from the current meta-analysis showed different psychological disorders of COVID-19 during the pandemic. Clinicians should be aware of the prevalence with which COVID-19-infected patients experience emotional distress, anxiety, fatigue, and PTSD. About half of the included systematic reviews (SRs)/meta-analyses (MAs) suffered from poorer methodological quality and increased risk of bias, reducing confidence in the findings. There must be more SRs/MAs and high-quality clinical trials conducted to confirm these findings.

Synergistic effects of flavonoids and paclitaxel in cancer treatment: a systematic review.

Paclitaxel is a natural anticancer compound with minimal toxicity, the capacity to stabilize microtubules, and high efficiency that has remained the standard of treatment alongside platinum-based therapy as a remedy for a variety of different malignancies. In contrast, polyphenols such as flavonoids are also efficient antioxidant and anti-inflammatory and have now been shown to possess potent anticancer properties. Therefore, the synergistic effects of paclitaxel and flavonoids against cancer will be of interest. In this review, we use a Boolean query to comprehensively search the well-known Scopus database for literature research taking the advantage of paclitaxel and flavonoids simultaneously while treating various types of cancer. After retrieving and reviewing the intended investigations based on the input keywords, the anticancer mechanisms of flavonoids and paclitaxel and their synergistic effects on different targets raging from cell lines to animal models are discussed in terms of the corresponding involved signaling transduction. Most studies demonstrated that these signaling pathways will induce apoptotic / pro-apoptotic proteins, which in turn may activate several caspases leading to apoptosis. Finally, it can be concluded that the results of this review may be beneficial in serving as a theoretical foundation and reference for future studies of paclitaxel synthesis, anticancer processes, and clinical applications involving different clinical trials.

Significant roles of potential genes and their mutations in nonalcoholic fatty liver disease.

In addition to having inflammation in the liver, overweight people also have changes in the composition of their immune systems and subsets of their immune systems. There are several genes involved in liver metabolism that have been implicated in nonalcoholic fatty liver disease (NAFLD), a liver disease associated with obesity, which is caused by high triglycerides and liver transaminases. NAFLD, a global liver disease, may differ in gene expression depending on where a person lives. In some alleles, the risk factors were independent. Finally, the researchers identified many genetic variations connected to fatty liver disease in those who did not drink alcohol regularly. These variants were located in genes involved in RNA metabolism, protein catabolism, and energy metabolism.

A systematic review and meta-analysis on the outcomes of extracorporeal shock wave compared to ureteroscopic lithotripsy for the treatment of ureteral stones.

Objectives: In this systematic review and meta-analysis, we sought to identify whether extracorporeal shockwave lithotripsy (ESWL) or ureteroscopic lithotripsy (URSL) is the most appropriate method for treating ureteral stones. Methods: We identified relevant literature by searching the Google Scholar and PubMed databases in accordance with PRISMA guidelines. We focused on the outcomes of extracorporeal shockwave lithotripsy and ureteroscopic lithotripsy. For each method, we compared complications, hematuria, perforation, failure, stone clearance, initial stone-free, operating time, stone size, auxiliary procedures, and overall stone-free outcomes. Our analysis involved meta-analysis, heterogeneity testing, subgroup analysis, meta-regression sensitivity analyses, Egger's tests, Smoothed Variance Egger's (SVE) testing, and Smoothed Variance Thomson (SVT) testing. In addition, we detected publication bias for all outcomes related to the two procedures. Results: Based on ten eligible studies, we conducted a meta-analysis on a total of 1509 patients. Extracorporeal shockwave lithotripsy was used to treat 677 patients; the remaining 832 patients were treated by the ureteroscopic lithotripsy procedure. Considering the meta-analysis statistical parameters including odds ratio (OR), standardized mean difference (SMD), Q, I2 and their p-values, the overall stone-free, operating time, stone size outcomes were identified with significant OR, SMD, and Q values. The hematuria, failure, and stone clearance outcomes were determined to have significant Q values. The perforation and initial stone free outcomes had significant OR values. And, complications and auxiliary urinary procedures were not significant in terms of OR and Q values. Conclusions: Analysis indicated that ESWL and URSL procedures are essential for the treatment of ureteral stones, even though the perforation rate is higher for URSL than for ESWL. Overall stone-free rates were better for the URSL procedure.

6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling.

The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the present work, we studied inhibitors of human aldose reductase (AR) to generate multi-dimensional QSAR models using 3D- and 6D-QSAR methods. For this purpose, Pentacle and Quasar's programs were used to produce the QSAR models using corresponding dissociation constant (Kd) values. By inspecting the performance metrics of the generated models, we achieved similar results with comparable internal validation statistics. However, considering the externally validated values, 6D-QSAR models provide significantly better prediction of endpoint values. The obtained results suggest that the higher the dimension of the QSAR model, the higher the performance of the generated model. However, more studies are required to verify these outcomes.

The impact of simulation time in predicting binding free energies using end-point approaches.

The profound impact of in silico studies for a fast-paced drug discovery pipeline is undeniable for pharmaceutical community. The rational design of novel drug candidates necessitates considering optimization of their different aspects prior to synthesis and biological evaluations. The affinity prediction of small ligands to target of interest for rank-ordering the potential ligands is one of the most routinely used steps in the context of virtual screening. So, the end-point methods were employed for binding free energy estimation focusing on evaluating simulation time effect. Then, a set of human aldose reductase inhibitors were selected for molecular dynamics (MD)-based binding free energy calculations. A total of 100[Formula: see text]ns MD simulation time was conducted for the ligand-receptor complexes followed by prediction of binding free energies using MM/PB(GB)SA and LIE approaches under different simulation time. The results revealed that a maximum of 30[Formula: see text]ns simulation time is sufficient for determination of binding affinities inferred from steady trend of squared correlation values (R2) between experimental and predicted [Formula: see text]G as a function of MD simulation time. In conclusion, the MM/PB(GB)SA algorithms performed well in terms of binding affinity prediction compared to LIE approach. The results provide new insights for large-scale applications of such predictions in an affordable computational cost.

A systematic review of anti-cancer roles and mechanisms of kaempferol as a natural compound.

It has been shown in multiple experimental and biological investigations that kaempferol, an edible flavonoid generated from plants, may be used as an anti-cancer drug and has been shown to have anti-cancer properties. Many signaling pathways are altered in cancer cells, resulting in cell growth inhibition and death in various tumor types. Cancer is a multifaceted illness coordinated by multiple external and internal mechanisms. Natural extracts with the fewest side effects have piqued the attention of researchers in recent years, attempting to create cancer medicines based on them. An extensive array of natural product-derived anti-cancer agents have been examined to find a successful method. Numerous fruits and vegetables have high levels of naturally occurring flavonoid kaempferol, and its pharmacological and biological effects have been studied extensively. Certain forms of cancer are sensitive to kaempferol-mediated anti-cancer activity, although complete research is needed. We have endeavored to concentrate our review on controlling carcinogenic pathways by kaempferol in different malignancies. Aside from its extraordinary ability to modify cell processes, we have also discussed how kaempferol has the potential to be an effective therapy for numerous tumors.

Development of New Inhibitors of HDAC1-3 Enzymes Aided by In Silico Design Strategies.

Histone deacetylases (HDACs) are overexpressed in cancer, and their inhibition shows promising results in cancer therapy. In particular, selective class I HDAC inhibitors such as entinostat are proposed to be more beneficial in breast cancer treatment. Computational drug design is an inevitable part of today's drug discovery projects because of its unequivocal role in saving time and cost. Using three HDAC inhibitors trichostatin, vorinostat, and entinostat as template structures and a diverse fragment library, all synthetically accessible compounds thereof (∼3200) were generated virtually and filtered based on similarity against the templates and PAINS removal. The 298 selected structures were docked into the active site of HDAC I and ranked using a calculated binding affinity. Top-ranking structures were inspected manually, and, considering the ease of synthesis and drug-likeness, two new structures (3a and 3b) were proposed for synthesis and biological evaluation. The synthesized compounds were purified to a degree of more than 95% and structurally verified using various methods. The designed compounds 3a and 3b showed 65-80 and 5% inhibition on HDAC 1, 2, and 3 isoforms at a concentration of 10 µM, respectively. The novel compound 3a may be used as a lead structure for designing new HDAC inhibitors.

A systems biology approach for investigating significantly expressed genes among COVID-19, hepatocellular carcinoma, and chronic hepatitis B.

Background: Worldwide, COVID-19's death rate is about 2%, considering the incidence and mortality. However, the information on its complications in other organs, specifically the liver and its disorders, is limited in mild or severe cases. In this study, we aimed to computationally investigate the typical relationships between liver-related diseases [i.e., hepatocellular carcinoma (HCC), and chronic hepatitis B (CHB)] and COVID-19, considering the involved significant genes and their molecular mechanisms. Methods: We investigated two GEO microarray datasets (GSE164805 and GSE58208) to identify differentially expressed genes (DEGs) among the generated four datasets for mild/severe COVID-19, HCC, and CHB. Then, the overlapping genes among them were identified for GO and KEGG enrichment analyses, protein-protein interaction network construction, hub genes determination, and their associations with immune cell infiltration. Results: A total of 22 significant genes (i.e., ACTB, ATM, CDC42, DHX15, EPRS, GAPDH, HIF1A, HNRNPA1, HRAS, HSP90AB1, HSPA8, IL1B, JUN, POLR2B, PTPRC, RPS27A, SFRS1, SMARCA4, SRC, TNF, UBE2I, and VEGFA) were found to play essential roles among mild/severe COVID-19 associated with HCC and CHB. Moreover, the analysis of immune cell infiltration revealed that these genes are mostly positively correlated with tumor immune and inflammatory responses. Conclusions: In summary, the current study demonstrated that 22 identified DEGs might play an essential role in understanding the associations between the mild/severe COVID-19 patients with HCC and CHB. So, the HCC and CHB patients involved in different types of COVID-19 can benefit from immune-based targets for therapeutic interventions. Supplementary Information: The online version contains supplementary material available at 10.1186/s43042-022-00360-3.

A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach.

AIM OF THE STUDY: Liver regeneration is one of the essential fields of regenerative medicine as a branch of tissue engineering and molecular biology that draws global researchers' attention. This study aims to conduct a systematic review and meta-analysis on the high-throughput gene expression microarray dataset of liver regeneration on the NCBI-GEO database to identify the significant genes and signaling pathways and confirm the genes from literature studies on associated diseases. MATERIAL AND METHODS: We thoroughly searched the NCBI-GEO database to retrieve and screen the GEO microarray datasets' contents. Due to the inclusion of different species in eligible GEO datasets in the meta-analysis, the list of significant genes for the random-effects model were identified. Moreover, we carried out detailed gene analyses for three main gene ontology components and the KEGG signaling pathway. Furthermore, we investigated the possibility of genes' association with liver cancer through the Kaplan-Meier plot. RESULTS: The random-effects model from six eligible GEO datasets identified 71 genes with eight down-regulated and 63 up-regulated genes. The target genes are involved in various cellular functions such as cell proliferation, cell death, and cell cycle control. Finally, we noted that 58 out of 71 genes are associated with different types of diseases related explicitly to other liver and inflammation diseases. CONCLUSIONS: The current study assessed various GEO datasets at the early stages of liver regeneration with promising results. The present systematic review and meta-analysis results are beneficial for future novel drug design and discovery specifically for patients in the liver transplantation process.

The role of PI3K signaling pathway and its associated genes in papillary thyroid cancer.

BACKGROUND: One of the well-differentiated types of thyroid cancer is papillary thyroid cancer (PTC), often developed by genetic mutations and radiation. METHODS: In this study, the public NCBI-GEO database was systematically searched. The eligible datasets were the targets for biomarker discovery associated with PI3K signaling pathway. RESULTS: Only two datasets were suitable and passed the inclusion criteria. The meta-analysis outcomes revealed eleven upregulation and thirteen downregulation genes differentially expressed between PTC and healthy tissues. Moreover, the outcomes for survival and disease-free rates for each gene were illustrated. CONCLUSIONS: The present research suggests a panel signature of 24 gene biomarkers in diagnosing the PTC.

A hybrid systems biology and systems pharmacology investigation of Zingerone's effects on reconstructed human epidermal tissues.

Background: As individuals live longer, elderly populations can be expected to face issues. This pattern urges researchers to investigate the aging concept further to produce successful anti-aging agents. In the current study, the effects of Zingerone (a natural compound) on epidermal tissues were analyzed using a bioinformatics approach. Methods: For this purpose, we chose the GEO dataset GSE133338 to carry out the systems biology and systems pharmacology approaches, ranging from identifying the differentially expressed genes to analyzing the gene ontology, determining similar structures of Zingerone and their features (i.e., anti-oxidant, anti-inflammatory, and skin disorders), constructing the gene-chemicals network, analyzing gene-disease relationships, and validating significant genes through the evidence presented in the literature. Results: The post-processing of the microarray dataset identified thirteen essential genes among control and Zingerone-treated samples. The procedure revealed various structurally similar chemical and herbal compounds with possible skin-related effects. Additionally, we studied the relationships of differentially expressed genes with skin-related diseases and validated their direct connections with skin disorders the evidence available in the literature. Also, the analysis of the microarray profiling dataset revealed the critical role of interleukins as a part of the cytokines family on skin aging progress. Conclusions: Zingerone, and potentially any constituents of Zingerone (e.g., their similar compound scan functionality), can be used as therapeutic agents in managing skin disorders such as skin aging. However, the beneficial effects of Zingerone should be assessed in other models (i.e., human or animal) in future studies.

Impact of Gene Biomarker Discovery Tools Based on Protein-Protein Interaction and Machine Learning on Performance of Artificial Intelligence Models in Predicting Clinical Stages of Breast Cancer.

Breast cancer, as one of the most common diseases threatening the women's life, has attracted serious attention of the clinical and biomedical researchers worldwide. The genome-based studies along with their registered GEO datasets are frequent in the literature. Since several methodologies have been developed for analyzing and identifying gene biomarkers, it is necessary to evaluate their robustness. In this study, three well-known biomarker identification methods (i.e., ClusterOne, MCODE, and BioDiscML) were employed in order to identify the potential biomarkers. Then, the methods were ranked and evaluated using nonlinear classification models developed based on the identified sets of biomarkers. A combined BC microarray dataset consisting of GSE124647, GSE124646, and GSE15852 was used as training set, and two test datasets, GSE15852 and GSE25066, were used for the performance measurement of the trained models. The validation of the proposed models was carried out internally (leave-one-out, fivefold and tenfold cross-validation, random sampling, test on training set) and externally (test on test set). The results showed that ClusterOne, MCODE, and BioDiscML tools ranked first, second, and third, respectively, based on the area under the curve (AUC), accuracy, F1 score, precision, and recall metrics. Overall, it can be concluded that the descriptive values of gene biomarkers in terms of their biological aspects that have been determined by a given methodology and the predictive power of the models developed based on the identified gene biomarkers should be considered simultaneously while validating the biomarker identification approaches.

Systems biology comprehensive analysis on breast cancer for identification of key gene modules and genes associated with TNM-based clinical stages.

Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein-protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan-Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan-Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.