Integrated Bioinformatics Analysis the Function of RNA Binding Proteins (RBPs) and Their Prognostic Value in Breast Cancer

Refining the pool of RNA-binding domains advances the classification and prediction of RNA-binding proteins

From transcription to decay, RNA-binding proteins (RBPs) influence RNA metabolism. Using the RBP2GO database that combines proteome-wide RBP screens from 13 species, we investigated the RNA-binding features of 176 896 proteins. By compiling published lists of RNA-binding domains (RBDs) and RNA-related protein family (Rfam) IDs with lists from the InterPro database, we analyzed the distribution of the RBDs and Rfam IDs in RBPs and non-RBPs to select RBDs and Rfam IDs that were enriched in RBPs. We also explored proteins for their content in intrinsically disordered regions (IDRs) and low complexity regions (LCRs). We found a strong positive correlation between IDRs and RBDs and a co-occurrence of specific LCRs. Our bioinformatic analysis indicated that RBDs/Rfam IDs were strong indicators of the RNA-binding potential of proteins and helped predicting new RBP candidates, especially in less investigated species. By further analyzing RBPs without RBD, we predicted new RBDs that were validated by RNA-bound peptides. Finally, we created the RBP2GO composite score by combining the RBP2GO score with new quality factors linked to RBDs and Rfam IDs. Based on the RBP2GO composite score, we compiled a list of 2018 high-confidence human RBPs. The knowledge collected here was integrated into the RBP2GO database at https://RBP2GO-2-Beta.dkfz.de.

The multifaceted role of Fragile X-Related Protein 1 (FXR1) in cellular processes: an updated review on cancer and clinical applications

RNA-binding proteins (RBPs) modulate the expression level of several target RNAs (such as mRNAs) post-transcriptionally through interactions with unique binding sites in the 3'-untranslated region. There is mounting information that suggests RBP dysregulation plays a significant role in carcinogenesis. However, the function of FMR1 autosomal homolog 1(FXR1) in malignancies is just beginning to be unveiled. Due to the diversity of their RNA-binding domains and functional adaptability, FXR1 can regulate diverse transcript processing. Changes in FXR1 interaction with RNA networks have been linked to the emergence of cancer, although the theoretical framework defining these alterations in interaction is insufficient. Alteration in FXR1 expression or localization has been linked to the mRNAs of cancer suppressor genes, cancer-causing genes, and genes involved in genomic expression stability. In particular, FXR1-mediated gene regulation involves in several cellular phenomena related to cancer growth, metastasis, epithelial-mesenchymal transition, senescence, apoptosis, and angiogenesis. FXR1 dysregulation has been implicated in diverse cancer types, suggesting its diagnostic and therapeutic potential. However, the molecular mechanisms and biological effects of FXR1 regulation in cancer have yet to be understood. This review highlights the current knowledge of FXR1 expression and function in various cancer situations, emphasizing its functional variety and complexity. We further address the challenges and opportunities of targeting FXR1 for cancer diagnosis and treatment and propose future directions for FXR1 research in oncology. This work intends to provide an in-depth review of FXR1 as an emerging oncotarget with multiple roles and implications in cancer biology and therapy.

In silico analyses of pan-squamous cell carcinoma unveiled the immunological implications of MRPL13, which had previously been under-recognized

The involvement of the mitochondrial ribosomal protein 13 (MRPL13) gene in the development of adenocarcinoma has been previously reported. However, the clinicopathological significance of MRPL13 in squamous cell carcinoma (SCC) remains poorly understood. To gain insight into the clinicopathological and immunological implications of MRPL13 expression in SCC, we conducted a bioinformatic analysis utilizing various available databases, including TIMER 2.0, Xiantao academic tool and TISIDB, attempting to evaluate the abnormal expression, prognosis and immunological correlation of MRPL13 in the pan-SCC setting. Subsequently, we conducted experimental verification using an esophageal squamous cell carcinoma (ESCC) tissue array subjected to multiplexed immunofluorescent (mIF) staining. The ESCC tissue array we used consists of 93 dots of ESCC and 86 dots of matched adjacent normal tissues (ANT). Data from in silico analyses showed that MRPL13 mRNA is significantly up-regulated and correlated with infiltration of CD8+ T cells in pan-SCC. However, in silico analyses did not support the prognostic role of MRPL13 in SCC. Consistently, data from the ESCC tissue array showed that MRPL13 was remarkably elevated in ESCC tissues relative to ANT in stroma, which was controlled by pan-cytokeratin (pan-CK) staining. In the epithelia, no significant difference was identified between ESCC and ANT. Furthermore, MRPL13 expression markedly correlated with the infiltration of CD8+ T cells in the stromal region but not in the epithelial region. Prognostically, no significant association was observed between MRPL13 expression and overall survival, regardless of epithelial or stromal section. Through these pan-SCC analyses, we have expanded the understanding of MRPL13 previously reported, in particular, underscoring the immunological involvement of MRPL13 in the tumor microenvironment of SCC that has been under-recognized before, suggesting that MRPL13 may regulate the infiltration of CD8+ T cells into the SCC microenvironment.

Identification and validation of N7 -methylguanosine-associated gene NCBP1 as prognostic and immune-associated biomarkers in breast cancer patients

We intend to evaluate the importance of N7 -methylguanosine (m7G) for the prognosis of breast cancer (BC). We gained 29 m7G-related genes from the published literature and among them, 16 m7G-related genes were found to have differential expression. Five differentially expressed genes (CYFIP1, EIF4E, EIF4E3, NCBP1 and WDR4) were linked to overall survival. This suggests that m7G-related genes might be prognostic or therapeutic targets for BC patients. We put the five genes to LASSO regression analysis to create a four-gene signature, including EIF4E, EIF4E3, WDR4 and NCBP1, that divides samples into two risky groups. Survival was drastically worsened in a high-risk group (p < 0.001). The signature's predictive capacity was demonstrated using ROC (10-year AUC 0.689; 10-year AUC 0.615; 3-year AUC 0.602). We found that immune status was significantly different between the two risk groups. In particular, NCBP1 also has a poor prognosis, with higher diagnostic value in ROC. NCBP1 also has different immune states according to its high or low expression. Meanwhile, knockdown of NCBP1 suppresses BC malignancy in vitro. Therefore, m7G RNA regulators are crucial participants in BC and four-gene mRNA levels are important predictors of prognosis. NCBP1 plays a critical target of m7G mechanism in BC.

UTP23 Functions in Breast Cancer Progression and Predicts Poor Prognosis of Luminal a Breast Cancer

BACKGROUND

Luminal A breast cancer is the most common molecular subtype of breast cancer. Exploring biomarkers to identify luminal A breast cancer patients at high risk of recurrence and metastasis has important clinical significance. UTP23 is a component of ribosomal small-subunit processome, which is involved in ribosome synthesis and RNA maturation. The role of UTP23 in breast cancer has not been reported.

METHODS

TCGA-BRCA data, LinkedOmics, STRING, Metascape and ssGSEA were used to analyze UTP23 expression in breast cancer and evaluate prognosis. Quantitative real-time PCR, western blot and in vitro cell experiment were used to demonstrate the role of UTP23 in breast cancer.

RESULTS

UTP23 showed abnormally high expression in multiple cancers and was associated with poor prognosis. UTP23 was associated with T stage, lymph node metastasis, race, histological type, molecular subtypes and survival status in breast cancer. Importantly, UTP23 was significantly associated with poor OS in luminal A or early breast cancer, not in non-luminal A or advanced breast cancer. UTP23 expression was significantly correlated with immune cells infiltration. Enrichment analysis suggested that UTP23 might regulate cell cycle and cell division. Bioinformatics analysis showed DCAF13 might be downstream factor of UTP23. UTP23 expression promoted MCF-7 cells proliferation, migration and invasion possibly through regulating DCAF13 expression.

CONCLUSIONS

UTP23 may function in breast cancer progression. The elevated UTP23 may be a potential prognostic biomarker for luminal A or early breast cancer.

Function and prognosis analysis of nucleolus protein DCAF13 in breast cancer

Background

Breast cancer is one of the main causes of death among women. RNA binding proteins (RBPs) play a crucial role in the progression of breast cancer, with increasingly detailed understanding of RBP functional molecular mechanisms in breast cancer, the functional research of RBPs may help elucidate the potential mechanisms of tumor occurrence, development, invasion, metastasis and prognosis. DDB1- and CUL4-associated factor 13 (DCAF13) is an RBPs has been identified as a substrate receptor for the CUL4-DDB1 E3 ligase complex. Its expression is related to the prognosis of certain cancer. We tried to explore both co-expressed network and biological functions of DCAF13 in breast cancer.

Methods

The Cancer Genome Atlas (TCGA) database was used to analyze the different expression of DCAF13 messenger RNA (mRNA) between normal breast tissue and breast carcinoma tissue, and the clinical data about 960 samples were downloaded from the cBio Cancer Genomics Portal (cBioPortal). The expression level of DCAF13, co-expression network, and survival were analyzed. Those with a fold change ≥1 and FDR <0.05 were considered to have statistical significance. Unsupervised clustering of differentially expressed RBPs was performed based on log2-transformed FPKM values using the "pheatmap" package in R. Genes with a Spearman score >0.55 were regarded as moderately co-expressed genes. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to construct a co-expression network. Meanwhile, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify the biological process cluster and pathway cluster, respectively.

Results

Compared with normal breast tissue, DCAF13 mRNA expression was significantly increased in breast cancer tissue (P<0.01). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to identify the functions of the co-expressed network. These genes were mainly enriched in mitosis, nuclear division, metabolic process, recombination, replication and repair of DNA, double-strand break repair, posttranscriptional regulation of gene expression, regulation of cell cycle, division and proliferation, regulation of protein stability and also participation in in regulation of poly(A) RNA binding, mRNA binding, tRNA binding, adenosine triphosphate (ATP) binding. KEGG pathway analysis revealed that the genes were mainly enriched in cell cycle, oocyte meiosis and oxidative phosphorylation. According to survival analysis, upregulation of DCAF13 mRNA was significant for overall survival (OS) (P=0.0163).

Conclusions

DCAF13 is up-regulated in breast cancer, the OS of patients with DCAF13 up-regulation was obviously reduced. DCAF13 was used as a diagnostic marker and therapeutic target for breast cancer. By building a co-expression network of DCAF13 and conducting bioinformatics analysis, it is possible to find the biomarker to evaluate patient prognosis. This finding provides a new target in mechanism and cell research of breast cancer.

RNA binding proteins (RBPs) and their role in DNA damage and radiation response in cancer

Traditionally majority of eukaryotic gene expression is influenced by transcriptional and post-transcriptional events. Alterations in the expression of proteins that act post-transcriptionally can affect cellular signaling and homeostasis. RNA binding proteins (RBPs) are a family of proteins that specifically bind to RNAs and are involved in post-transcriptional regulation of gene expression and important cellular processes such as cell differentiation and metabolism. Deregulation of RNA-RBP interactions and any changes in RBP expression or function can lead to various diseases including cancer. In cancer cells, RBPs play an important role in regulating the expression of tumor suppressors and oncoproteins involved in various cell-signaling pathways. Several RBPs such as HuR, AUF1, RBM38, LIN28, RBM24, tristetrapolin family and Musashi play critical roles in various types of cancers and their aberrant expression in cancer cells makes them an attractive therapeutic target for cancer treatment. In this review we provide an overview of i). RBPs involved in cancer progression and their mechanism of action ii). the role of RBPs, including HuR, in breast cancer progression and DNA damage response and iii). explore RBPs with emphasis on HuR as therapeutic target for breast cancer therapy.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

DCAF-13 is required for C. elegans growth, development, and fertility

DCAF13 (DDB1 and CUL4 associated factor 13) is a potential oncogene but little is understood about the developmental roles of this highly conserved gene. We characterized the RNAi phenotypes of dcaf-13 , the C. elegans homolog of DCAF13, and show that compared to age-matched control worms, body length is decreased in dcaf-13 (RNAi) C. elegans larvae, suggesting a role of dcaf-13 in larval development. In addition, dcaf-13 (RNAi) worms display either a failure or delay in reaching the L4 and adult stages. Our data also indicates that dcaf-13 (RNAi) treatment beginning at L4 stage does not increase embryonic lethality in progeny; however, progeny production was significantly decreased in dcaf-13 (RNAi) worms, suggesting a general role in fertility and perhaps oocyte development.

The Notch Signaling Pathway Contributes to Angiogenesis and Tumor Immunity in Breast Cancer

Breast cancer in women is the first leading tumor in terms of incidence worldwide. Some subtypes of BC lack distinct molecular targets and exhibit therapeutic resistance; these patients have a poor prognosis. Thus, the search for new molecular targets is an ongoing challenge for BC therapy. The Notch signaling pathway is found in both vertebrates and invertebrates, and it is a highly conserved in the evolution of the species, controlling cellular fates such as death, proliferation, and differentiation. Numerous studies have shown that improper activation of Notch signaling may lead to excessive cell proliferation and cancer, with tumor-promoting and tumor-suppressive effects in various carcinomas. Thus, inhibitors of Notch signaling are actively being investigated for the treatment of various tumors. The role of Notch signaling in BC has been widely studied in recent years. There is a growing body of evidence suggesting that Notch signaling has a pro-oncogenic role in BC, and the tumor-promoting effect is largely a result of the diverse nature of tumor immunity. Immunological abnormality is also a factor involved in the pathogenesis of BC, suggesting that Notch signaling could be a target for BC immunotherapies. Furthermore, angiogenesis is essential for BC growth and metastasis, and the Notch signaling pathway has been implicated in angiogenesis, so studying the role of Notch signaling in BC angiogenesis will provide new prospects for the treatment of BC. We summarize the potential roles of the current Notch signaling pathway and its inhibitors in BC angiogenesis and the immune response in this review and describe the pharmacological targets of Notch signaling in BC, which may serve as a theoretical foundation for future research into exploring this pathway for novel BC therapies.

The function and regulatory mechanism of RNA-binding proteins in breast cancer and their future clinical treatment prospects

Breast cancer is the most common female malignancy, but the mechanisms regulating gene expression leading to its development are complex. In recent years, as epigenetic research has intensified, RNA-binding proteins (RBPs) have been identified as a class of posttranscriptional regulators that can participate in regulating gene expression through the regulation of RNA stabilization and degradation, intracellular localization, alternative splicing and alternative polyadenylation, and translational control. RBPs play an important role in the development of normal mammary glands and breast cancer. Functional inactivation or abnormal expression of RBPs may be closely associated with breast cancer development. In this review, we focus on the function and regulatory mechanisms of RBPs in breast cancer, as well as the advantages and challenges of RBPs as potential diagnostic and therapeutic targets in breast cancer, and discuss the potential of RBPs in clinical treatment.

Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer

Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.

Integrated In Silico Analyses Identify PUF60 and SF3A3 as New Spliceosome-Related Breast Cancer RNA-Binding Proteins

More women are diagnosed with breast cancer (BC) than any other type of cancer. Although large-scale efforts have completely redefined cancer, a cure remains unattainable. In that respect, new molecular functions of the cell should be investigated, such as post-transcriptional regulation. RNA-binding proteins (RBPs) are emerging as critical post-transcriptional modulators of tumorigenesis, but only a few have clear roles in BC. To recognize new putative breast cancer RNA-binding proteins, we performed integrated in silico analyses of all human RBPs (n = 1392) in three major cancer databases and identified five putative BC RBPs (PUF60, TFRC, KPNB1, NSF, and SF3A3), which showed robust oncogenic features related to their genomic alterations, immunohistochemical changes, high interconnectivity with cancer driver genes (CDGs), and tumor vulnerabilities. Interestingly, some of these RBPs have never been studied in BC, but their oncogenic functions have been described in other cancer types. Subsequent analyses revealed PUF60 and SF3A3 as central elements of a spliceosome-related cluster involving RBPs and CDGs. Further research should focus on the mechanisms by which these proteins could promote breast tumorigenesis, with the potential to reveal new therapeutic pathways along with novel drug-development strategies.

Integrated Analysis of RNA Binding Protein-Related lncRNA Prognostic Signature for Breast Cancer Patients

Long non-coding RNAs (lncRNAs) have been well known for their multiple functions in the tumorigenesis, development, and prognosis of breast cancer (BC). Mechanistically, their production, function, or stability can be regulated by RNA binding proteins (RBPs), which were also involved in the carcinogenesis and progression of BC. However, the roles and clinical implications of RBP-related lncRNAs in BC remain largely unknown. Therefore, we herein aim to construct a prognostic signature with RBP-relevant lncRNAs for the prognostic evaluation of BC patients. Firstly, based on the RNA sequencing data of female BC patients from The Cancer Genome Atlas (TCGA) database, we screened out 377 differentially expressed lncRNAs related to RBPs. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were then performed to establish a prognostic signature composed of 12-RBP-related lncRNAs. Furthermore, we divided the BC patients into high- and low-risk groups by the prognostic signature and found the overall survival (OS) of patients in the high-risk group was significantly shorter than that of the low-risk group. Moreover, the 12-lncRNA signature exhibited independence in evaluating the prognosis of BC patients. Additionally, a functional enrichment analysis revealed that the prognostic signature was associated with some cancer-relevant pathways, including cell cycle and immunity. In summary, our 12-lncRNA signature may provide a theoretical reference for the prognostic evaluation or clinical treatment of BC patients.

RNA-binding proteins in ovarian cancer: a novel avenue of their roles in diagnosis and treatment

Ovarian cancer (OC), an important cause of cancer-related death in women worldwide, is one of the most malignant cancers and is characterized by a poor prognosis. RNA-binding proteins (RBPs), a class of endogenous proteins that can bind to mRNAs and modify (or even determine) the amount of protein they can generate, have attracted great attention in the context of various diseases, especially cancers. Compelling studies have suggested that RBPs are aberrantly expressed in different cancer tissues and cell types, including OC tissues and cells. More specifically, RBPs can regulate proliferation, apoptosis, invasion, metastasis, tumorigenesis and chemosensitivity and serve as potential therapeutic targets in OC. Herein, we summarize what is currently known about the biogenesis, molecular functions and potential roles of human RBPs in OC and their prospects for application in the clinical treatment of OC.

Expression profile of RNA binding protein in cervical cancer using bioinformatics approach

BACKGROUND

It has been demonstrated by studies globally that RNA binding proteins (RBPs) took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between RBPs and overall survival of CC patients. We retrieved significant DEGs (differently expressed genes, RNA binding proteins) correlated to the process of cervical cancer development.

METHODS

Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Differently expressed RNA binding proteins (DEGs) were retrieved by Wilcoxon sum-rank test. ClusterProfiler package worked in R software was used to perform GO and KEGG enrichment analyses. Univariate proportional hazard cox regression and multivariate proportional hazard cox regressions were applied to identify DEGs equipped with prognostic value and other clinical independent risk factors. ROC curve was drawn for comparing the survival predict feasibility of risk score with other risk factors in CC patients. Nomogram was drawn to exhibit the prediction model and validated by C-index and calibration curve. Correlations between differentially expressed RNA binding proteins (DEGs) and other clinical features were investigated by t test or Cruskal Wallis analysis. Correlation between Immune and DEGs in cervical cancer was investigated by ssGSEA.

RESULTS

347 differentially expressed RBPs (DEGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these DEGs involved in RNA splicing, catabolic process and metabolism. Cox regression model showed that there were ten DEGs significantly associated with overall survival of cervical cancer patients. WDR43 (HR = 0.423, P = 0.008), RBM38 (HR = 0.533, P < 0.001), RNASEH2A (HR = 0.474, P = 0.002) and HENMT1 (HR = 0.720, P = 0.071) played protective roles in survival among these ten genes. Stage (Stage IV vs Stage I HR = 3.434, P < 0.001) and risk score (HR = 1.214, P < 0.001) were sorted as independent prognostic risk factors based on multivariate cox regression. ROC curve validated that risk score was preferable to predict survival of CC patients than other risk factors. Additionally, we found some of these ten predictor DEGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P < 0.05). Part of immune cells and immune functions showed a lower activity in high risk group than low risk group which is stratified by median risk score.

CONCLUSION

Our discovery showed that many RNA binding proteins involved in the progress of cervical cancer, which could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

Doxorubicin promotes breast cancer cell migration and invasion via DCAF13

DDB1 and CUL4 associated factor 13 (DCAF13) is a substrate receptor in the CUL4-DDB1 E3 ligase, and its expression is associated with the prognosis of certain cancers. In the present study, we report evidence that DCAF13 is aberrantly overexpressed in human breast cancer and its expression is positively associated with cancer progression. Further analysis showed that the DCAF13 expression level is significantly higher in triple-negative breast cancer compared to non-triple-negative breast cancer, indicating a positive correlation between its expression and the aggressiveness of breast cancer. Subsequent studies revealed that DCAF13 regulates cancer cell migration, invasion and epithelial-mesenchymal transition in human breast cancer, whereas it has no significant impact on breast cancer cell proliferation, cell cycle progressionor apoptosis. Taken together, our results demonstrate that DCAF13 promotes the epithelial-mesenchymal transition in human breast cancer cells, indicating an involvement in breast cancer metastasis. Furthermore, we report that doxorubicin, a widely used chemotherapy drug, increases DCAF13 expression in breast cancer cells, leading to enhanced cancer cell migration and invasion. These results suggest that doxorubicin chemotherapy may increase the risk of metastasis of drug-resistant breast cancer cells, and future therapeutics targeting DCAF13 may help reduce the risk, especially for patients undergoing chemotherapy.

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Background: The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms.

Methods: In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes.

Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways.

Conclusion: Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.

Identification of an Immune-Related Prognostic Gene CLEC5A Based on Immune Microenvironment and Risk Modeling of Ovarian Cancer

Objective: To understand the immune characteristics of the ovarian cancer (OC) microenvironment and explore the differences of immune-related molecules and cells to establish an effective risk model and identify the molecules that significantly affected the immune response of OC, to help guide the diagnosis. Methods: First, we calculate the TMEscore which reflects the immune microenvironment, and then analyze the molecular differences between patients with different immune characteristics, and determine the prognostic genes. Then, the risk model was established by least absolute shrinkage and selection operator (LASSO) analysis and combined with clinical data into a nomogram for diagnosis and prediction. Subsequently, the potential gene CLEC5A influencing the immune response of OC was identified from the prognostic genes by integrative immune-stromal analysis. The genomic alteration was explored based on copy number variant (CNV) and somatic mutation data. Results: TMEscore was a prognostic indicator of OC. The prognosis of patients with high TMEscore was better. The risk model based on immune characteristics was a reliable index to predict the prognosis of patients, and the nomogram could comprehensively evaluate the prognosis of patients. Besides, CLEC5A was closely related to the abundance of immune cells, immune response, and the expression of immune checkpoints in the OC microenvironment. OC cells with high expression of CLEC5A increased the polarization of M2 macrophages. CLEC5A expression was significantly associated with TTN and CDK12 mutations and affected the copy number of tumor progression and immune-related genes. Conclusion: The study of immune characteristics in the OC microenvironment and the risk model can reveal the factors affecting the prognosis and guide the clinical hierarchical treatment. CLEC5A can be used as a potential key gene affecting the immune microenvironment remodeling of OC, which provides a new perspective for improving the effect of OC immunotherapy.

Systematic Construction and Validation of an RNA-Binding Protein-Associated Prognostic Model for Acute Myeloid Leukemia

Background: The abnormal expression of RNA-binding proteins (RBPs) in various malignant tumors is closely related to the occurrence and development of tumors. However, the role of RBPs in acute myeloid leukemia (AML) is unclear. Methods: We downloaded harmonized RNA-seq count data and clinical data for AML from UCSC Xena, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) cohorts. R package edgeR was used for differential expression analysis of 337 whole-blood data and 173 AML data. The prognostic value of these RBPs was systematically investigated by using univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and multivariate Cox regression analysis. C-index and calibration diagram were used to judge the accuracy of the model, and decision curve analysis (DCA) was used to judge the net benefit. The biological pathways involved were revealed by gene set enrichment analysis (GSEA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and the protein-protein interaction (PPI) network performed lateral verification on the selected gene set and LASSO results. Results: A prognostic model of 12-RBP signature was established. In addition, the net benefit and prediction accuracy of the prognostic model and the mixed model based on it were significantly higher than that of cytogenetics. It is verified in the TARGET cohort and shows good prediction effect. Both the selection of our gene set and the LASSO results have high credibility. Most of these pathways are involved in the development of the disease, and they also accumulate in leukemia and RNA-related pathways. Conclusion: The prognosis model of the 12-RBP signature found in this study is an optimized biomarker that can effectively stratify the risk of AML patients. Nomogram based on this prognostic model is a reliable method to predict the median survival time of patients. This study expands our current understanding of the role of RBPs in the occurrence of AML and may lay the foundation for future treatment of the disease.

A Novel Prognostic Model for Oral Squamous Cell Carcinoma: The Functions and Prognostic Values of RNA-Binding Proteins

PURPOSE

The biological roles and clinical significance of RNA-binding proteins (RBPs) in oral squamous cell carcinoma (OSCC) are not fully understood. We investigated the prognostic value of RBPs in OSCC using several bioinformatic strategies.

MATERIALS AND METHODS

OSCC data were obtained from a public online database, the Limma R package was used to identify differentially expressed RBPs, and functional enrichment analysis was performed to elucidate the biological functions of the above RBPs in OSCC. We performed protein-protein interaction (PPI) network and Cox regression analyses to extract prognosis-related hub RBPs. Next, we established and validated a prognostic model based on the hub RBPs using Cox regression and risk score analyses.

RESULTS

We found that the differentially expressed RBPs were closely related to the defense response to viruses and multiple RNA processes. We identified 10 prognosis-related hub RBPs (ZC3H12D, OAS2, INTS10, ACO1, PCBP4, RNASE3, PTGES3L-AARSD1, RNASE13, DDX4, and PCF11) and effectively predicted the overall survival of OSCC patients. The area under the receiver operating characteristic (ROC) curve (AUC) of the risk score model was 0.781, suggesting that our model exhibited excellent prognostic performance. Finally, we built a nomogram integrating the 10 RBPs. The internal validation cohort results showed a reliable predictive capability of the nomogram for OSCC.

CONCLUSION

We established a novel 10-RBP-based model for OSCC that could enable precise individual treatment and follow-up management strategies in the future.

Roles of mitochondrial unfolded protein response in mammalian stem cells

The mitochondrial unfolded protein response (UPR^mt) is an evolutionarily conserved adaptive mechanism for improving cell survival under mitochondrial stress. Under physiological and pathological conditions, the UPR^mt is the key to maintaining intracellular homeostasis and proteostasis. Important roles of the UPR^mt have been demonstrated in a variety of cell types and in cell development, metabolism, and immune processes. UPR^mt dysfunction leads to a variety of pathologies, including cancer, inflammation, neurodegenerative disease, metabolic disease, and immune disease. Stem cells have a special ability to self-renew and differentiate into a variety of somatic cells and have been shown to exist in a variety of tissues. These cells are involved in development, tissue renewal, and some disease processes. Although the roles and regulatory mechanisms of the UPR^mt in somatic cells have been widely reported, the roles of the UPR^mt in stem cells are not fully understood. The roles and functions of the UPR^mt depend on stem cell type. Therefore, this paper summarizes the potential significance of the UPR^mt in embryonic stem cells, tissue stem cells, tumor stem cells, and induced pluripotent stem cells. The purpose of this review is to provide new insights into stem cell differentiation and tumor pathogenesis.

Identification of a Three-RNA Binding Proteins (RBPs) Signature Predicting Prognosis for Breast Cancer

BACKGROUND

To date, breast cancer remains the primary cause of tumor-related death among women, even though some leap-type developments of oncology have been done to slash the mortality. Considering the tumor heterogeneity and individual variation, the more reliable biomarkers are required to be identified for supporting the development of precision medicine in breast cancer.

METHODS

Based on the TCGA-BRCA and METABRIC databases, the differently expressed RNA binding proteins (RBPs) between tumor and normal tissues were investigated. In this study, we focused on the communal differently expressed RBPs in four subtypes of breast cancer. Lasso-penalized Cox analysis, Stepwise-multivariate Cox analysis and Kaplan-Meier survival curve were performed to identify the hub RBP-coding genes in predicting prognosis of breast cancer, and a prognostic model was established. The efficiency of this model was further validated in other independent GSE20685, GSE4922 and FUSCC-TNBC cohorts by calculating the risk score and performing survival analysis, ROC and nomogram. Moreover, pathologic functions of the candidate RBPs in breast cancer were explored using some routine experiments in vitro, and the potential compounds targeting these RBPs were predicted by reviewing the Comparative Toxicogenomics Database.

RESULTS

Here, we identified 62 RBPs which were differently expressed between the tumor and normal tissues. Thereinto, three RBPs (MRPL12, MRPL13 and POP1) acted as independent risk factors, and their expression pattern also correlated with poor prognosis of patients. A prognostic model, built with these 3-RBPs, possessed statistical significance to predict the survival probability of patients with breast cancer. Furthermore, experimental validations showed that down-regulating the expression of endogenous MRPL12, MRPL13 or POP1 could dramatically suppress the cellular viability and migration of breast cancer cells in vitro. Besides, some compounds (such as the Acetaminophen, Urethane and Tunicamycin) were predicted for curing breast cancer via targeting MRPL12, MRPL13 and POP1 simultaneously.

CONCLUSION

This study identified and established a 3-RBPs-based signature and nomogram for predicting the survival probability of patients with breast cancer. MRPL12, MRPL13 and POP1 might act as oncogenes in maintaining cellular viability and accelerating metastasis of breast cancer cells, implying the possibility of which to be designed as biomarkers and/or therapeutic targets for breast cancer.

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma

Methods

We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients' risk of illness and survival time. Also, the data in the HPA database were used for verification.

Results

In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient's life span is also made.

Conclusion

In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

Molecular characterization of colorectal cancer: A five-gene prognostic signature based on RNA-binding proteins

BACKGROUND

Colorectal cancer (CRC) is one of the most common cancers worldwide. RNA-binding proteins (RBPs) regulate essential biological processes and play essential roles in a variety of cancers. The present study screened differentially expressed RBPs, analyzed their function and constructed a prognostic model to predict the overall survival of patients with CRC.

METHODS

We downloaded CRC RNA-sequencing data from the Cancer Genome Atlas (TCGA) portal and screened differentially expressed RBPs. Then, functional analyses of these genes were performed, and a risk model was established by multivariate Cox regression.

RESULTS

We obtained 132 differentially expressed RBPs, including 66 upregulated and 66 downregulated RBPs. Functional analysis revealed that these genes were significantly enriched in RNA processing, modification and binding, ribosome biogenesis, post-transcriptional regulation, ribonuclease and nuclease activity. Additionally, some RBPs were significantly related to interferon (IFN)-alpha and IFN-beta biosynthetic processes and the Toll-like receptor signaling pathway. A prognostic model was constructed and included insulin like growth factor 2 messenger ribonucleic acid binding protein 3 (IGF2BP3), poly (A) binding protein cytoplasmic 1 like (PABPC1L), peroxisome proliferator activated receptor gamma coactivator 1 alpha (PPARGC1A), peptidyl- transfer ribonucleic acid hydrolase 1 homolog (PTRH1) and tudor domain containing 7 (TDRD7). The model is an independent risk factor for clinicopathological characteristics.

CONCLUSION

Our study provided novel insights into the pathogenesis of CRC and constructed a prognostic gene model, which may be helpful for determining the prognosis of CRC.

The Function and Prognostic Value of RNA-Binding Proteins in Colorectal Adenocarcinoma Were Analyzed Based on Bioinformatics of Smart Medical Big Data

Colon cancer is the third most frequent cancer in the world and is mainly adenocarcinoma in terms of pathological type. It has been confirmed that the dysregulation of RNA-binding proteins (RBPs) significantly participates in the occurrence and development of numerous malignant tumors. Therefore, we analyzed the RBPs associated with colon adenocarcinoma (COAD) to assess their possible biological effects and prognostic value. A total of 398 COAD tissue datasets and 39 normal tissue datasets were retrieved from the TCGA data resource and screened out the RBPs, which are differentially expressed between tumor tissues and nontumor tissues. Then, bioinformatics analyses based on smart medical big data were conducted on these RBPs. Overall, 181 differentially expressed RBPs were uncovered, consisting of 121 upregulated RBPs and 60 downregulated RBPs. Finally, we selected 7 prognostic-related RBPs with research prospects and constructed a prognostic model according to the median risk score. There were remarkable differences in OS between the high-risk and low-risk groups. In addition, the performance of the prognostic model was evaluated and verified with other COAD patient data in the TCGA database. The results showed that the area under the ROC curve (AUC) for the train group was 0.744 and the one for the test group was 0.661, confirming that the model assesses patients' prognosis to some extent. And based on 7 hub RBPs, we constructed a nomogram as a reference for evaluating the survival rate of COAD patients.

Mutations in cis that affect mRNA synthesis, processing and translation

Genetic mutations that cause hereditary diseases usually affect the composition of the transcribed mRNA and its encoded protein, leading to instability of the mRNA and/or the protein. Sometimes, however, such mutations affect the synthesis, the processing or the translation of the mRNA, with similar disastrous effects. We here present an overview of mRNA synthesis, its posttranscriptional modification and its translation into protein. We then indicate which elements in these processes are known to be affected by pathogenic mutations, but we restrict our review to mutations in cis, in the DNA of the gene that encodes the affected protein. These mutations can be in enhancer or promoter regions of the gene, which act as binding sites for transcription factors involved in pre-mRNA synthesis. We also describe mutations in polyadenylation sequences and in splice site regions, exonic and intronic, involved in intron removal. Finally, we include mutations in the Kozak sequence in mRNA, which is involved in protein synthesis. We provide examples of genetic diseases caused by mutations in these DNA regions and refer to databases to help identify these regions. The over-all knowledge of mRNA synthesis, processing and translation is essential for improvement of the diagnosis of patients with genetic diseases.

Integrated Analysis of Nine Prognostic RNA-Binding Proteins in Soft Tissue Sarcoma

RNA-binding proteins (RBPs) have been shown to be dysregulated in cancer transcription and translation, but few studies have investigated their mechanism of action in soft tissue sarcoma (STS). Here, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to identify differentially expressed RBPs in STS and normal tissues. Through a series of biological information analyses, 329 differentially expressed RBPs were identified. Functional enrichment analysis showed that differentially expressed RBPs were mainly involved in RNA transport, RNA splicing, mRNA monitoring pathways, ribosome biogenesis and translation regulation. Through Cox regression analyses, 9 RBPs (BYSL, IGF2BP3, DNMT3B, TERT, CD3EAP, SRSF12, TLR7, TRIM21 and MEX3A) were all up-regulated in STS as prognosis-related genes, and a prognostic model was established. The model calculated a risk score based on the expression of 9 hub RBPs. The risk score could be used for risk stratification of patients and had a high prognostic value based on the receiver operating characteristic (ROC) curve. We also established a nomogram containing risk scores and 9 key RBPs to predict the 1-year, 3-year, and 5-year survival rates of patients in STS. Afterwards, methylation analysis showed significant changes in the methylation degree of BYSL, CD3EAP and MEX2A. Furthermore, the expression of 9 hub RBPs was closely related to immune infiltration rather than tumor purity. Based on the above studies, these findings may provide new insights into the pathogenesis of STS and will provide candidate biomarkers for the prognosis of STS.

Development and Validation of a RNA Binding Protein-Associated Prognostic Model for Hepatocellular Carcinoma

BACKGROUND

Dysregulation of RNA binding proteins (RBPs) has been identified in multiple malignant tumors correlated with tumor progression and occurrence. However, the function of RBPs is not well understood in hepatocellular carcinoma (HCC).

METHODS

The RNA sequence data of HCC was extracted out of the Cancer Genome Atlas (TCGA) database and different RBPs were calculated between regular and cancerous tissue. The study explored the expression and predictive value of the RBPs systemically with a series of bioinformatic analyzes.

RESULTS

A total of 330 RBPs, including 208 up-regulated and 122 down-regulated RBPs, were classified differently. Four RBPs (MRPL54, EZH2, PPARGC1A, EIF2AK4) were defined as the forecast related hub gene and used to construct a model for prediction. Further study showed that the high-risk subgroup is poor survived (OS) compared to the model-based low-risk subgroup. The area of the prognostic model under the time-dependent receiver operator characteristic (ROC) curve is 0.814 in TCGA training group and 0.729 in validation group, indicating a strong prognostic model. We also created a predictive nomogram and a web-based calculator (https://dxyjiang.shinyapps.io/RBPpredict/) based on the 4 RBPs and internal validation in the TCGA cohort, which displayed a beneficial predictive ability for HCC.

CONCLUSIONS

Our results provide new insights into HCC pathogenesis. The 4-RBP gene signature showed a reliable HCC prediction ability with possible applications in therapeutic decision making and personalized therapy.

Differential regulation of autophagy by STAU1 in alveolar rhabdomyosarcoma and non-transformed skeletal muscle cells

PURPOSE

Recent work has highlighted the therapeutic potential of targeting autophagy to modulate cell survival in a variety of diseases including cancer. Recently, we found that the RNA-binding protein Staufen1 (STAU1) is highly expressed in alveolar rhabdomyosarcoma (ARMS) and that this abnormal expression promotes tumorigenesis. Here, we asked whether STAU1 is involved in the regulation of autophagy in ARMS cells.

METHODS

We assessed the impact of STAU1 expression modulation in ARMS cell lines (RH30 and RH41), non-transformed skeletal muscle cells (C2C12) and STAU1-transgenic mice using complementary techniques.

RESULTS

We found that STAU1 silencing reduces autophagy in the ARMS cell lines RH30 and RH41, while increasing their apoptosis. Mechanistically, this inhibitory effect was found to be caused by a direct negative impact of STAU1 depletion on the stability of Beclin-1 (BECN1) and ATG16L1 mRNAs, as well as by an indirect inhibition of JNK signaling via increased expression of Dual specificity phosphatase 8 (DUSP8). Pharmacological activation of JNK or expression silencing of DUSP8 was sufficient to restore autophagy in STAU1-depleted cells. By contrast, we found that STAU1 downregulation in non-transformed skeletal muscle cells activates autophagy in a mTOR-dependent manner, without promoting apoptosis. A similar effect was observed in skeletal muscles obtained from STAU1-overexpressing transgenic mice.

CONCLUSIONS

Together, our data indicate an effect of STAU1 on autophagy regulation in ARMS cells and its differential role in non-transformed skeletal muscle cells. Our findings suggest a cancer-specific potential of targeting STAU1 for the treatment of ARMS.

Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Venous Thromboembolism (VTE)

OBJECTIVES

To explore the key genes, and correlated pathways in venous thromboembolism (VTE) via bioinformatic analysis, and expected our findings could contribute to the development of new biomarkers and therapeutic target for VTE.

METHODS

Two VTE-related microarray expression profiles (GSE48000 and GSE19151) were downloaded from the Gene Expression Ominibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using limma package, and overlapping DEGs were identified form the above two expression profiles. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway enrichment analyses were performed by DAVID. Protein-protein interaction (PPI) network was constructed by using STRING and visualized with Cytoscape. Furthermore, module analysis plus centrality analysis of the PPI network were executed to identify the potential key genes. Finally, the pathway analysis was performed using GenCLiP 3.0.

RESULTS

A total of 173 DEGs (125 upregulated and 48 downregulated) were identified. GO analysis demonstrated that DEGs were mainly enriched in viral life cycle, ribosome and structural constituent of ribosome. Meanwhile, KEGG pathway analysis showed that these genes were enriched in ribosome, Parkinson's disease and cell cycle. Additionally, one most significant module and 12 hub genes were found. Finally, 6 key genes, namely ISG15, RPS15A, MRPL13, ICT1, MRPL15 and RPLP0, with high centrality features were identified. These key genes were mainly involved in translation, metabolism of proteins and ribosome pathway.

CONCLUSIONS

In summary, these 6 identified genes and correlated pathways should play an important role in VTE, which can provide new insight into the molecular mechanism, potential biomarkers and therapeutic targets associated with VTE.

Integrated analysis of RNA-binding proteins in thyroid cancer

Recently, the incidence of thyroid cancer (THCA) has been on the rise. RNA binding proteins (RBPs) and their abnormal expression are closely related to the emergence and pathogenesis of tumor diseases. In this study, we obtained gene expression data and corresponding clinical information from the TCGA database. A total of 162 aberrantly expressed RBPs were obtained, comprising 92 up-regulated and 70 down-regulated RBPs. Then, we performed a functional enrichment analysis and constructed a PPI network. Through univariate Cox regression analysis of key genes and found that NOLC1 (p = 0.036), RPS27L (p = 0.011), TDRD9 (p = 0.016), TDRD6 (p = 0.002), IFIT2 (p = 0.037), and IFIT3 (p = 0.02) were significantly related to the prognosis. Through the online website Kaplan-Meier plotter and multivariate Cox analysis, we identified 2 RBP-coding genes (RPS27L and IFIT3) to construct a predictive model in the entire TCGA dataset and then validate in two subsets. In-depth analysis revealed that the data gave by this model, the patient's high-risk score is very closely related to the overall survival rate difference (p = 0.038). Further, we investigated the correlation between the model and the clinic, and the results indicated that the high-risk was in the male group (p = 0.011) and the T3-4 group (p = 0.046) was associated with a poor prognosis. On the whole, the conclusions of our research this time can make it possible to find more insights into the research on the pathogenesis of THCA, this could be beneficial for individualized treatment and medical decision making.

Systematic analysis of the function and prognostic value of RNA binding proteins in Colon Adenocarcinoma

Background: Abnormal expression of RNA-binding proteins (RBPs) is closely related to tumorigenesis, progression, and prognosis. This study performed systematic bioinformatic analysis of RBPs abnormally expressed in colon adenocarcinoma (COAD) using the Cancer Genome Atlas (TCGA) database to screen prognostic markers and potential therapeutic targets.

Methods: First, the gene expression data from COAD samples were used to screen out differentially expressed RBPs for functional enrichment analysis and to visualize interaction relationships. Second, RBPs that were significantly related to prognosis were screened through univariate and multivariate Cox regression analysis to construct a prognostic model. The prediction performance of the prognostic model was evaluated by survival analysis and receiver operating characteristic (ROC) curve analysis. It addition, it was verified in the test cohort. The Human Protein Atlas (HPA) online database was used to verify the expression levels of RBPs in the prognostic model.

Results: The study identified 181 differentially expressed RBPs and analyzed their interaction and functional enrichment, which were mainly related to non-coding RNA processing, ribosome biogenesis, RNA metabolic processes, RNA phosphodiester bond hydrolysis, and alternative mRNA splicing. Five RBPs related to prognosis were used to construct a prognostic model, and its predictive ability was verified by the test cohort. ROC curve analysis showed that the prognostic model had good sensitivity and specificity. Independent prognostic analysis showed that risk scores could be used as independent prognostic factors for COAD.

Conclusion: This study constructed a reliable prognostic model by analyzing COAD differentially expressed RBPs, facilitating the screening of COAD prognostic markers and therapeutic targets.

Identification of vascular dementia and Alzheimer's disease hub genes expressed in the frontal lobe and temporal cortex by weighted co-expression network analysis and construction of a protein-protein interaction

Background: It is difficult to distinguish cognitive decline due to AD from that sustained by cerebrovascular disease in view of the great overlap. It is uncertain in the molecular biological pathway behind AD and VaD.Objective: Our study aimed to explore the hub molecules and their associations with each other to identify potential biomarkers and therapeutic targets for the AD and VaD.Methods: We screened the differentially expressed genes of AD and VaD, used weighted gene co-expression network analysis and then constructed a VaD-AD-specific protein-protein interaction network with functional annotation to their related metabolic pathways. Finally, we performed a ROC curve analysis of hub proteins to get an idea about their diagnostic value.Results: In the frontal lobe and temporal cortex, hub genes were identified. With regard to VaD, there were only three hub genes which encoded the neuropeptides, SST, NMU and TAC1. The AUC of these genes were 0.804, 0.768 and 0.779, respectively. One signature was established for these three hub genes with AUC of 0.990. For the identification of AD and VaD, all hub genes were receptors. These genes included SH3GL2, PROK2, TAC3, HTR2A, MET, TF, PTH2R CNR1, CHRM4, PTPN3 and CRH. The AUC of these genes were 0.853, 0.859, 0.796, 0.775, 0.706, 0.677, 0.696, 0.668 and 0.652, respectively. The other signature was built for eleven hub genes with AUC of 0.990.Conclusion: In the frontal lobe and temporal cortex regions, hub genes are used as diagnostic markers, which may provide insight into personalized potential biomarkers and therapeutic targets for patients with VaD and AD.

Integrated bioinformatic analysis of RNA binding proteins in hepatocellular carcinoma

RNA binding proteins (RBPs) are aberrantly expressed in a tissue-specific manner across many tumors. These proteins, which play a vital role in post-transcriptional gene regulation, are involved in RNA splicing, maturation, transport, stability, degradation, and translation. We set out to establish an accurate risk score model based on RBPs to estimate prognosis in hepatocellular carcinoma (HCC). RNA-sequencing data, proteomic data and corresponding clinical information were acquired from the Cancer Genome Atlas database and the Clinical Proteomic Tumor Analysis Consortium database respectively. We identified 406 differentially expressed RBPs between HCC tumor and normal tissues at the transcriptional and protein level. Overall, 11 RBPs (BRIX1, DYNC1H1, GTPBP4, PRKDC, RAN, RBM19, SF3B4, SMG5, SPATS2, TAF9, and THOC5) were selected to establish a risk score model. We divided HCC patients into low-risk and high-risk groups based on the median of risk score values. The survival analysis indicated that patients in the high-risk group had poorer overall survival compared to patients in the low-risk group. Our study demonstrated that 11 RBPs were associated with the overall survival of HCC patients. These RBPs may represent potential drug targets and can help optimize future clinical treatment.

MRPL13 is a Prognostic Cancer Biomarker and Correlates with Immune Infiltrates in Breast Cancer

Objective

To study the expression of MRPL13 in breast cancer tissues using TCGA database, analyze the correlation between the expression and clinicopathological characteristics of patients, and explore the role of MRPL13 in the development of breast cancer (BC).

Methods

The BC mRNA data and clinical information were downloaded from TCGA database. The correlation between MRPL13 expression and clinicopathological parameters was analyzed. Cox regression multivariate analysis was used to explore the factors affecting the prognosis of BC patients. The UALCAN database was used to analyze the expression level of MRPL13 in BC and its relationship with clinical pathological factors. The GSEA method was used to predict the possible regulatory pathways of MRPL13. Immune responses of MRPL13 expression were analyzed using TISIDB and CIBERSORT. Additionally, GEPIA, K-M survival analysis and data from the HPA were used to validate the outcomes.

Results

The expression of MRPL13 in BC tissues was significantly higher than normal counterparts, patients with low MRPL13 expression had a better survival prognosis, also indicated an independent prognostic factor. GSEA analysis showed that the regulation of cell migration, positive regulation of endothelial cell migration, and Notch signaling pathway were enriched in tissues with low expression of MRPL13. Additionally, depleting MRPL13 expression inhibited invasion in MCF-10A and MCF-7 cells. Furthermore, PCR showed that MRPL13 affected VEGFA and MMP gene expression. CIBERSORT analysis revealed that the amount of NK cells decreased when MRPL13 expression was high.

Conclusion

The expression of MRPL13 mRNA is upregulated in BC tissues, and the expression level of MRPL13 is significantly related to the clinicopathological factors of patients. High MRPL13 expression is a poor prognostic factor for BC, and it can be used as a molecular marker for prognosis judgment and as a potential therapeutic target.

Abnormal Expression of Mitochondrial Ribosomal Proteins and Their Encoding Genes with Cell Apoptosis and Diseases

Mammalian mitochondrial ribosomes translate 13 proteins encoded by mitochondrial genes, all of which play roles in the mitochondrial respiratory chain. After a long period of reconstruction, mitochondrial ribosomes are the most protein-rich ribosomes. Mitochondrial ribosomal proteins (MRPs) are encoded by nuclear genes, synthesized in the cytoplasm and then, transported to the mitochondria to be assembled into mitochondrial ribosomes. MRPs not only play a role in mitochondrial oxidative phosphorylation (OXPHOS). Moreover, they participate in the regulation of cell state as apoptosis inducing factors. Abnormal expressions of MRPs will lead to mitochondrial metabolism disorder, cell dysfunction, etc. Many researches have demonstrated the abnormal expression of MRPs in various tumors. This paper reviews the basic structure of mitochondrial ribosome, focuses on the structure and function of MRPs, and their relationships with cell apoptosis and diseases. It provides a reference for the study of the function of MRPs and the disease diagnosis and treatment.

Predicting the Clinical Outcome of Lung Adenocarcinoma Using a Novel Gene Pair Signature Related to RNA-Binding Protein

Adenocarcinoma is the most common type of lung cancer, and patients have varying prognoses. RNA-binding proteins (RBP) are deemed to be closely associated with tumorigenesis and development, but the exact mechanism is currently unknown. This study was aimed at constructing a new robust prognostic model based on RNA-binding protein-related gene pair scores for better clinical guidance. The model for this study was constructed based on data of lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Prognosis-related RBP gene pair models were created based on differentially expressed genes, and the accuracy of the models was verified in a different age, staging, and other subdatasets. A total of 379 RNA-binding protein-related genes were differentially expressed in tumor tissue. From these genes, we constructed a prognostic model consisting of 33 gene pairs, which were found to be significantly associated with survival in TCGA dataset (P < 0.0001, hazard ratio (HR) = 4.380 (3.139 to 6.111)) and different subdatasets. As expected, the results were verified in the GEO validation cohort (P = 7.8 × 10⁻³, HR = 1.597 (1.095 to 2.325)). We found that the signature exhibited an independent prognostic factor in both the univariate and multivariate Cox regression analyses (P < 0.001). CIBERSORT was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues. CD8 T cells, activated dendritic cells, regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group (P < 0.01). Patients in the high-risk group had significantly higher tumor mutational burden (TMB) (P = 4.953e − 04) and lower levels of immune cells (P = 3.473e − 05) and stromal cells (P = 0.005) in the tumor microenvironment than those in the low-risk group. Furthermore, the Protein-protein interaction (PPI) network and various enrichment analyses have genuinely uncovered the interrelationships and potential functions of the RBP genes within the model. The results of the present study validated the importance of RNA-binding proteins in tumorigenesis and progression and support the RBP gene-related signature as a promising marker for prognosis prediction in lung adenocarcinoma.

Integrated analysis of the functions of RNA binding proteins in clear cell renal cell carcinoma

RNA binding proteins (RBPs) dysregulation is involved in the processes of various tumors. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. In present study, we first performed consensus clustering and identified two clusters, of which cluster 2 was closely correlated with the malignancy of ccRCC. Differentially expressed RBPs between normal and tumor tissues were obtained, comprising 71 up-regulated and 44 down-regulated ones. Then, ten hub genes were selected and validated using The Human Protein Atlas database and receiver operating characteristic curves, showing good diagnostic value for cancers. Besides, we identified ten RBPs with the most useful prognostic values, and were used to construct a risk score model. The model could be used to stratify patients with different prognosis and phenotype distributions. The model showed good performance and can be used as a complementation for clinical factors to guide clinical practice in the future.

Molecular Characterization and Clinical Relevance of RNA Binding Proteins in Colorectal Cancer

Abnormal expression of RNA binding proteins (RBPs) has been reported across various cancers. However, the potential role of RBPs in colorectal cancer (CRC) remains unclear. In this study, we performed a systematic bioinformatics analysis of RBPs in CRC. We downloaded CRC data from The Cancer Genome Atlas (TCGA) database. Our analysis identified 242 differentially expressed RBPs between tumor and normal tissues, including 200 upregulated and 42 downregulated RBPs. Next, we found eight RBPs (RRS1, PABPC1L, TERT, SMAD6, UPF3B, RP9, NOL3, and PTRH1) related to the prognoses of CRC patients. Among these eight prognosis-related RBPs, four RBPs (NOL3, PTRH1, UPF3B, and SMAD6) were selected to construct a prognostic risk score model. Furthermore, our results indicated that the prognostic risk score model accurately predicted the prognosis of CRC patients [area under the receiver operating characteristic curve (AUC)for 3- and 5-year overall survival (OS) and was 0.645 and 0.672, respectively]. Furthermore, we developed a nomogram based on a prognostic risk score model. The nomogram was able to demonstrate the wonderful performance in predicting 3- and 5-year OS. Additionally, we validated the clinical value of four risk genes in the prognostic risk score model and identified that these risk genes were associated with tumorigenesis, lymph node metastasis, distant metastasis, clinical stage, and prognosis. Finally, we used the TIMER and Human Protein Atlas (HPA)database to validate the expression of four risk genes at the transcriptional and translational levels, respectively, and used a clinical cohort to validate the roles of NOL3 and UPF3B in predicting the prognosis of CRC patients. In summary, our study demonstrated that RBPs have an effect on CRC tumor progression and might be potential prognostic biomarkers for CRC patients.

Biological functions and prognostic value of RNA Binding Proteins in clear cell Renal Cell Carcinoma

Early detection and accurate evaluation were both critical to improving the prognosis of clear cell Renal Cell Carcinoma (ccRCC) patients. More importantly, RNA Binding Proteins (RBPs) play a vital role in the tumorigenesis and progression of numerous cancers. However, the relationship between RBPs and ccRCC is still unclear. Exploring the potential biological functions of RBPs in ccRCC and establishing a prognostic signature to predict the survival probability remains meaningful. In this study, transcriptome profiling and the corresponding clinical information were obtained from the TCGA database, GEO database, and ICGC database. By using the "edgeR" R package, 200 DERBPs were found, including 128 up-regulated and 72 down-regulated RBPs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DERBPs were mainly involved in regulating transcriptional processes and metabolism. Furthermore, there were 4 hub genes (RPS2, RPS14, RPS20, and RPLP0) were found in the PPI network, which may play vital biological roles among those DERBPs. Then we used LASSO regression to construct a prognostic signature and validated the signature in the GEO and ICGC cohort. The time-dependent receiver operating characteristic (ROC) curve showed that the signature could accurately predict the prognosis of ccRCC patients. Then we established a nomogram, and the calibration curve and ROC curve showed that the nomogram could accurately predict 1-year, 3-year, and 5-year overall survival (OS) of ccRCC patients (The AUC value: 0.871, 0.829, and 0.816). In conclusion, we constructed a 10-RBPs-based prognostic signature integrating clinical parameters to predict the prognosis of ccRCC patients. The prognostic signature based on the differentially expressed RBPs (DERBPs) might serve as promising diagnostic and prognostic biomarkers in ccRCC.

RNA-Binding Proteins in Cancer: Functional and Therapeutic Perspectives

Simple Summary

RNA-binding proteins (RBPs) play central roles in regulating posttranscriptional expression of genes. Many of them are known to be deregulated in a wide variety of cancers. Dysregulated RBPs influence the expression levels of target RNAs related to cancer phenotypes, such as proliferation, apoptosis, angiogenesis, senescence, and EMT/invasion/metastasis. Thus, understanding the molecular functions of RBPs and their roles in cancer-related phenotypes can lead to improved therapeutic strategies.

Abstract

RNA-binding proteins (RBPs) crucially regulate gene expression through post-transcriptional regulation, such as by modulating microRNA (miRNA) processing and the alternative splicing, alternative polyadenylation, subcellular localization, stability, and translation of RNAs. More than 1500 RBPs have been identified to date, and many of them are known to be deregulated in cancer. Alterations in the expression and localization of RBPs can influence the expression levels of oncogenes, tumor-suppressor genes, and genome stability-related genes. RBP-mediated gene regulation can lead to diverse cancer-related cellular phenotypes, such as proliferation, apoptosis, angiogenesis, senescence, and epithelial-mesenchymal transition (EMT)/invasion/metastasis. This regulation can also be associated with cancer prognosis. Thus, RBPs can be potential targets for the development of therapeutics for the cancer treatment. In this review, we describe the molecular functions of RBPs, their roles in cancer-related cellular phenotypes, and various approaches that may be used to target RBPs for cancer treatment.

Analysis of the autoimmune regulator (AIRE) gene variant rs2075876 (G/A) association with breast cancer susceptibility

BACKGROUND

Recently, unexpected autoimmune regulator (AIRE) implication in the scenario of several cancers, including breast cancer (BC), has emerged. This study aims to explore for the first time the possible association between AIRE gene rs2075876 G>A variant and BC risk in a sample of the Middle East population.

METHOD

In this case-control study, we genotyped AIRE rs2075876 G>A variant in 200 unrelated patients with BC and 340 cancer-free controls using a real-time allelic discrimination polymerase chain reaction. Odds ratios (ORs) and 95% confidence intervals (CIs) were applied to estimate the strength of association under several genetic models. In silico analysis of AIRE was also executed.

RESULTS

The minor allele (A) frequency of the specified variant accounted for 0.28 in the controls. G/G homozygote was significantly more frequent among patients (94%) compared to controls (66%) (P < .001). After adjusting confounding variables, individuals with A allele conferred protection against developing BC under allelic model (OR = 0.33, 95% CI = 0.20-0.55), recessive model (OR = 0.25, 95% CI = 0.10-0.60), dominant model (OR = 0.12, 95% CI = 0.05-0.29), and homozygote comparison (OR = 0.20, 95% CI = 0.08-0.50). In silico analysis revealed AIRE enrichment in several cancer-related pathways. Kaplan-Meier plotter for the cancer databases showed association of AIRE expression with prognosis in triple-negative BC (HR = 2.44, 95% CI = 1.44-4.15, log-rank P-value < .001).

CONCLUSION

The AIRE rs2075876 G>A variant showed association with BC risk in the study population. Further large-scale replication studies in different ethnicity are warranted to confirm the findings.

Identification of Candidate Genes Associated with Breast Cancer Prognosis

Breast cancer (BC) is the most malignant tumor in women. The molecular mechanisms underlying tumorigenesis still need to be further elucidated. It is necessary to investigate novel candidate genes involved in breast cancer progression and prognosis. In this study, we commit to explore candidate genes that associate with prognosis and therapy in BC by a comprehensive bioinformatic analysis. Four GEO datasets (GSE5764, GSE7904, GSE20711, and GSE29431) and the BC-related transcriptome data in TCGA database were downloaded and used to identify the differently expressed genes (DEGs). The function of DEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The protein-protein interaction (PPI) network of DEGs was constructed to identify hub genes. Prognostic candidate genes were identified through survival analysis. In addition, potential therapeutic targets were identified by constructed gene-drug interaction network through Comparative Toxicogenomics Database. A total of 547 DEGs (302 up and 245 down) were identified. Three core-subnetwork and 25 hub genes were identified in PPI network. Seven genes (namely COL12A1, QPRT, MRPL13, KRT14, KRT15, LAMB3, and MYBPC1) were identified as crucial prognostic candidate genes, which significantly associated with breast cancer overall survival. Furthermore, two representative candidate genes (COL12A1 and LAMB3) were optionally chosen for verification by reverse transcription and quantitative real-time polymerase chain reaction (RT-PCR). What's more, the gene-drugs interaction analysis indicates several antitumor drugs that could affect the expression of these prognostic markers, such as doxorubicin, cisplatin, and tamoxifen. These results identified seven crucial candidate genes that may serve as prognosis biomarkers and novel therapeutic targets of breast cancer, which may facilitate further understanding the molecular pathogenesis and providing potential therapeutic strategies for BC.

Prognostic Value of a Stemness Index-Associated Signature in Primary Lower-Grade Glioma

Objective

As a prevalent and infiltrative cancer type of the central nervous system, the prognosis of lower-grade glioma (LGG) in adults is highly heterogeneous. Recent evidence has demonstrated the prognostic value of the mRNA expression-based stemness index (mRNAsi) in LGG. Our aim was to develop a stemness index-based signature (SI-signature) for risk stratification and survival prediction.

Methods

Differentially expressed genes (DEGs) between LGG in the Cancer Genome Atlas (TCGA) and normal brain tissue samples from the Genotype-Tissue Expression (GTEx) project were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the mRNAsi-related gene sets. Meanwhile, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed for the functional annotation of the key genes. ESTIMATE was used to calculate tumor purity for acquiring the correct mRNAsi. Differences in overall survival (OS) between the high and low mRNAsi (corrected mRNAsi) groups were compared using the Kaplan Meier analysis. By combining the Lasso regression with univariate and multivariate Cox regression, the SI-signature was constructed and validated using the Chinese Glioma Genome Atlas (CGGA).

Results

There was a significant difference in OS between the high and low mRNAsi groups, which was also observed in the two corrected mRNAsi groups. Based on threshold limits, 86 DEGs were most significantly associated with mRNAsi via WGCNA. Seven genes (ADAP2, ALOX5AP, APOBEC3C, FCGRT, GNG5, LRRC25, and SP100) were selected to establish a risk signature for primary LGG. The ROC curves showed a fair performance in survival prediction in both the TCGA and the CGGA validation cohorts. Univariate and multivariate Cox regression revealed that the risk group was an independent prognostic factor in primary LGG. The nomogram was developed based on clinical parameters integrated with the risk signature, and its accuracy for predicting 3- and 5-years survival was assessed by the concordance index, the area under the curve of the time-dependent receiver operating characteristics curve, and calibration curves.

Conclusion

The SI-signature with seven genes could serve as an independent predictor, and suggests the importance of stemness features in risk stratification and survival prediction in primary LGG.

RBP EIF2S2 Promotes Tumorigenesis and Progression by Regulating MYC-Mediated Inhibition via FHIT-Related Enhancers

RNA-binding proteins (RBPs) play fundamental roles in cancer; however, we still lack knowledge about to what extent RBPs are dysregulated, as well as about perturbed signaling pathways in cancer. In this study, we integrated analysis of multidimensional data across >10,000 cancer patients and >1,000 cell lines. We identified a top candidate RBP: eukaryotic translation initiation factor 2 subunit beta (EIF2S2). EIF2S2 is highly expressed in tumors and is associated with malignant features as well as patient prognosis. Functional assays performed in cancer cells revealed that EIF2S2 promotes cancer cell proliferation, migration, and invasion in vitro as well as tumor growth and metastasis in vivo. Mechanistic investigations further demonstrated that EIF2S2 promotes tumorigenesis and progression by directly binding to a long non-coding RNA, LINC01600, which physically interacts with the MYC protein and increases its stability. Interestingly, we revealed that the EIF2S2-LINC01600-MYC axis can activate the Wnt/β-catenin pathway by inhibiting the activity of FHIT-related enhancers and FHIT expression. Finally, EIF2S2 knockdown combined with oxaliplatin treatment could be a potential combination therapy in cancer. Our integrated analysis provided detailed knowledge of the function of the EIF2S2-LINC01600-MYC axis, which will facilitate the development of rational combination therapies for cancer.

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma

Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. We then performed an integrative analysis to explore the expression and prognostic significance of these RBPs. A total of 164 differently expressed RBPs were identified, including 40 down-regulated and 124 up-regulated RBPs. Pathway and Gene ontology (GO) analysis indicated that the differently expressed RBPs were mainly related to RNA processing, RNA metabolic process, RNA degradation, RNA transport, splicing, localization, regulation of translation, RNA binding, TGF-beta signaling pathway, mRNA surveillance pathway, and aminoacyl-tRNA biosynthesis. Survival analysis revealed that the high expression of BOP1 or GNL3 or WDR12 or DCAF13 or IGF2BP3 or IGF2BP1 were associated with poor overall survival (OS). Conversely, overexpression of KHDRBS2/SMAD predicted high OS in these patients. ROC curve analysis showed that the eight hub genes with a better diagnostic accuracy to distinguish lung adenocarcinoma. The results provided novel insights into the pathogenesis of LUAD and the development of treatment targets and prognostic molecular markers.