Development and validation of a prediction model for lung adenocarcinoma based on RNA-binding protein

MicroRNA (let-7b-5p)-targeted DARS2 regulates lung adenocarcinoma growth by PI3K/AKT signaling pathway

Background

The aberrant intracellular expression of a mitochondrial aspartyl-tRNA synthetase 2 (DARS2) has been reported in human cancers. Nevertheless, its critical role and detailed mechanism in lung adenocarcinoma (LUAD) remain unexplored.

Methods

Initially, The Cancer Genome Atlas (TCGA)-based Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) was used to analyze the prognostic relevance of DARS2 expression in LUAD. Further, cell counting kit (CCK)-8, immunostaining, and transwell invasion assays in LUAD cell lines in vitro, as well as DARS2 silence on LUAD by tumorigenicity experiments in vivo in nude mice, were performed. Besides, we analyzed the expression levels of p-PI3K (phosphorylated-Phosphotylinosital3 kinase), PI3K, AKT (Protein Kinase B), p-AKT (phosphorylated-Protein Kinase B), PCNA (proliferating cell nuclear antigen), cleaved-caspase 3, E-cadherin, and N-cadherin proteins using the Western blot analysis.

Results

LUAD tissues showed higher DARS2 expression compared to normal tissues. Upregulation of DARS2 could be related to Tumor-Node-Metastasis (TNM) stage, high lymph node metastasis, and inferior prognosis. DARS2 silence decreased the proliferation, migration, and invasion abilities of LUAD cells. In addition, the DARS2 downregulation decreased the PCNA and N-cadherin expression and increased cleaved-caspase 3 and E-cadherin expressions in LUAD cells, coupled with the inactivation of the PI3K/AKT signaling pathway. Moreover, DARS2 silence impaired the tumorigenicity of LUAD in vivo. Interestingly, let-7b-5p could recognize DARS2 through a complementary sequence. Mechanistically, the increased let-7b-5p expression attenuated the promo-oncogenic action of DARS2 during LUAD progression, which were inversely correlated to each other in the LUAD tissues.

Conclusion

In summary, let-7b-5p downregulated DARS2 expression, regulating the progression of LUAD cells by the PI3K/AKT signaling pathway.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

Introduction

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.

Methods

This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.

Results

Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.

Conclusion

To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

DARS2 overexpression is associated with PET/CT metabolic parameters and affects glycolytic activity in lung adenocarcinoma

BACKGROUND

This study investigated the correlation between the expression of DARS2 and metabolic parameters of 18F-FDG PET/CT, and explored the potential mechanisms of DARS2 affecting the proliferation and glycolysis of lung adenocarcinoma (LUAD) cells.

METHODS

This study used genomics and proteomics to analyze the difference in DARS2 expression between LUAD samples and control samples. An analysis of 62 patients with LUAD who underwent 18F-FDG PET/CT examinations before surgery was conducted retrospectively. The correlation between DARS2 expression and PET/CT metabolic parameters, including SUVmax, SUVmean, MTV, and TLG, was examined by Spearman correlation analysis. In addition, the molecular mechanism of interfering with DARS2 expression in inhibiting LUAD cell proliferation and glycolysis was analyzed through in vitro cell experiments.

RESULTS

DARS2 expression was significantly higher in LUAD samples than in control samples (p < 0.001). DARS2 has high specificity (98.4%) and sensitivity (95.2%) in the diagnosis of LUAD. DARS2 expression was positively correlated with SUVmax, SUVmean, and TLG (p < 0.001). At the same time, the sensitivity and specificity of SUVmax in predicting DARS2 overexpression in LUAD were 88.9% and 65.9%, respectively. In vitro cell experiments have shown that interfering with DARS2 expression can inhibit the proliferation and migration of LUAD cells, promote cell apoptosis, and inhibit the glycolytic activity of tumor cells by inhibiting the expression of glycolytic related genes SLC2A1, GPI, ALDOA, and PGAM1.

CONCLUSIONS

Overexpression of DARS2 is associated with metabolic parameters on 18F-FDG PET/CT, which can improve LUAD diagnosis accuracy. DARS2 may be a useful biomarker to diagnose, prognosis, and target treatment of LUAD patients.

A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes

Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes.

LARP6 suppresses colorectal cancer progression through ZNF267/SGMS2-mediated imbalance of sphingomyelin synthesis

BACKGROUND

With increasing incidence and mortality, colorectal cancer (CRC) seriously endangers human health. LARP6, a member of La-related protein (LARP) family, is a RNA binding protein and probably associates with CRC progression, but its specific roles and mechanisms in CRC still remain unknown.

METHOD

Quantitative real-time PCR (qPCR), western blot, and immunohistochemistry were employed to examine LARP6 expression in CRC tissues. Using the stable LARP6 overexpression or interference CRC cell lines, the effect of LARP6 on CRC progression were evaluated. High-throughput RNA immunoprecipitation sequencing (RIP-seq) and a series of relevant experiments were conducted to explain how LARP6 functions. SPSS software was used for statistical analysis.

RESULT

In this study, we found that LARP6 expression is downregulated in CRC and correlates with patients' overall survival and relapse-free survival. Furthermore, altered LARP6 expression influences CRC cells invasion and metastasis. Mechanically, we discovered that LARP6 bind ZNF267 mRNA and regulated its stability and translation. LARP6 inhibited expression of SGMS2, a downstream target of ZNF267, resulting in ceramide and sphingomyelin imbalance in CRC cells. Interestingly, LARP6 also enhances autophagy activity of CRC cells, and the effect was at least partially determined by the inhibition of SGMS2-mediated sphingomyelin synthesis.

CONCLUSION

Our study showed how LARP6/ZNF267/SGMS2 axis influence CRC progression, which contributes to further understanding of the molecular mechanisms underlying CRC development.

Identification and validation of the mitochondrial function related hub genes by unsupervised machine learning and multi-omics analyses in lung adenocarcinoma

Background

The mitochondrion and its associated genes were heavily implicated in developing and therapy tumors as the primary cellular organelle in charge of metabolic reprogramming and ferroptosis. Our work focuses on discovering new potential targets while analyzing the multi-omics data of mitochondria-related genes in lung adenocarcinoma (LUAD).

Methods

The Cancer Genome Atlas (TCGA) database provided multi-omics data for LUAD patients. Based on the expression profile of the genes associated with mitochondria, the patients were grouped by the unsupervised clustering method. R was used to explore the differential expressed protein-code gene, miRNA, and lncRNA, as well as their enriched functions and ceRNA networks. Additionally, the discrepancy between immune infiltration and genetic variation was comprehensively characterized. Our clinical samples and in vitro experiments investigated the hub gene determined by LASSO and batch analysis.

Results

Two clusters are distinguished using unsupervised consensus clustering based on mitochondrial heterogeneity. The integrated analysis emphasized that patients in cluster B had a worse prognosis, higher mutation frequencies, and less immune cell infiltration. The hub genes DARS2 and COX5B are identified by further analysis using LASSO penalization. In vitro experiments indicated that DARS2 and COX5B knockdown inhibited tumor cell proliferation. The specimen of our hospital cohort conducted the immunohistochemistry analysis and validated that DARS2 and COX5B's expression was significantly higher in the tumor than in adjacent normal tissue and correlated to LUAD patients' prognosis.

Conclusion

Our observations implied that LUAD patients' tumors had distinct mitochondrial function heterogeneity with different clinical and molecular characteristics. DARS2 and COX5B might be critical genes involved in mitochondrial alterations and potential therapeutic targets.

Comprehensive analysis of the differences between left- and right-side colorectal cancer and respective prognostic prediction

Background

Previous studies have reported that the tumor heterogeneity and complex oncogenic mechanisms of proximal and distal colon cancer (CRC) are divergent. Therefore, we aim to analyze the differences between left-sided CRC (L_cancer) and right-sided CRC (R_cancer), as well as constructing respective nomograms.

Methods

We enrolled 335 colon cancer patients (146 L_cancer patients and 189 R_cancer patients) from The Cancer Genome Atlas (TCGA) data sets, and 102 pairs of color cancer tissue and adjacent normal tissue (51 L_cancer patients and 51 R_cancer patients) from our hospital. Firstly, we analyzed the differences between the L_cancer patients and R_cancer patients, and then established the L_cancer and R_cancer prognostic models using LASSO Cox.

Results

R_cancer patients had lower survival than L_cancer patients. R_cancer patients had higher ESTIMATE and immune scores and lower tumor purity. These patterns of expression of immune checkpoint-related genes and TMB level were higher in R_cancer than in L_cancer patients. Finally, we using Lasso Cox regression analyses established a prognostic model for L_cancer patients and a prognostic model for R_cancer patients. The AUC values of the risk score for OS in L_cancer were 0.862 in the training set and 0.914 in the testing set, while those in R_cancer were 0.835 in the training set and 0.857 in the testing set. The AUC values in fivefold cross-validation were between 0.727 and 0.978, proving that the two prognostic models have great stability. The nomogram of L_cancer included prognostic genes, age, pathological M, pathological stage, and gender, the AUC values of which were 0.800 in the training set and 0.905 in the testing set. Meanwhile, the nomogram of R_cancer comprised prognostic genes, pathological N, pathological T, and age, the AUC values of which were 0.836 in the training set and 0.850 in the testing set. In the R_cancer patients, high-risk patients had a lower proportion of ‘B cells memory’, ‘Dendritic cells resting’, immune score, ESTIMATE score, immune checkpoint-related genes, and HLA-family genes, and a higher proportion of ‘T cells follicular helper’, ‘Dendritic cells activated’, and ‘Mast cells activated’.

Conclusions

We found significant differences between L_cancer and R_cancer patients and established a clinical predictive nomogram for L_cancer patients and a nomogram for R_cancer patients. Additionally, R_cancer patients in low-risk groups may be more beneficial from immunotherapy.

Expression profile and prognostic values of LSM family in skin cutaneous melanoma

Background

The like-Smith (LSM) family is a group of RNA-binding proteins involved in RNA metabolism. However, their involvement in tumors, particularly skin cutaneous melanoma (SKCM), is not fully understood. In this study, we focused on the expression profiles and prognostic values of the LSM family in SKCM.

Methods

Raw data were downloaded from The Cancer Genome Atlas. The expression profile and prognostic value of LSM genes in SKCM were explored using the GEPIA, cBioPortal, and HPA databases. Protein–protein and gene–gene interaction analyses were performed using STRING and GeneMANIA. Enrichment and Cox regression analysis were conducted using R software. The TISIDB database was used to explore the relationship between LSMs and immunomodulators. Receiver operating characteristic curves and nomogram models were constructed to validate prognostic values.

Results

mRNA and protein expression levels of LSM2, LSM4, and LSM12 were significantly elevated in SKCM. The upregulated mRNA expression of LSM2 (p = 0.0013) and LSM4 (p = 0.0043) was significantly correlated with poor overall survival in patients with SKCM, whereas only LSM2 (p = 0.049) overexpression was markedly associated with worse disease-free survival. LSM2 overexpression was an independent risk factor (p = 0.013) and was confirmed to have a high prognostic value in SKCM using the receiver operating characteristic curve (AUC = 0.942) and nomogram models. All LSM genes were identified as genomic mutations, whereas alteration of LSM2 (p = 0.0153) significantly affected the overall survival in patients with SKCM. Significant correlations were observed between LSM family expression, immune cell infiltration, and immunomodulator. Furthermore, function and pathway enrichment analysis showed that the LSM family was mainly RNA binding proteins and involved in RNA splicing and degradation.

Conclusion

Expression profiles and prognostic values of LSM in SKCM were inconsistent. Among the LSM family, only LSM2 may serve as a potential poor prognosticator and immunotherapeutic target of SKCM.

Signature based on RNA-binding protein-related genes for predicting prognosis and guiding therapy in non-small cell lung cancer

Background: Studies have reported that RNA-binding proteins (RBPs) are dysregulated in multiple cancers and are correlated with the progression and prognosis of disease. However, the functions of RBPs in non-small cell lung cancer (NSCLC) remain unclear. The present study aimed to explore the function of RBPs in NSCLC and their prognostic and therapeutic value.

Methods: The mRNA expression profiles, DNA methylation data, gene mutation data, copy number variation data, and corresponding clinical information on NSCLC were downloaded from The Cancer Genome Atlas, Gene Expression Omnibus, and the University of California Santa Cruz Xena databases. The differentially expressed RBPs were identified between tumor and control tissues, and the expression and prognostic value of these RBPs were systemically investigated by bioinformatics analysis. A quantitative polymerase chain reaction (qPCR) was performed to validate the dysregulated genes in the prognostic signature.

Results: A prognostic RBP-related signature was successfully constructed based on eight RBPs represented as a risk score using least absolute shrinkage and selection operator (LASSO) regression analysis. The high-risk group had a worse overall survival (OS) probability than the low-risk group (p &lt; 0.001) with 1-, 3-, and 5-year area under the receiver operator characteristic curve values of 0.671, 0.638, and 0.637, respectively. The risk score was associated with the stage of disease (p &lt; 0.05) and was an independent prognostic factor for NSCLC when adjusted for age and UICC stage (p &lt; 0.001, hazard ratio (HR): 1.888). The constructed nomogram showed a good predictive value. The P53, focal adhesion, and NOD-like receptor signaling pathways were the primary pathways in the high-risk group (adjusted p value &lt;0.05). The high-risk group was correlated with increased immune infiltration (p &lt; 0.05), upregulated relative expression levels of programmed cell death 1 (PD1) (p = 0.015), cytotoxic T-lymphocyte-associated protein 4 (CTLA4) (p = 0.042), higher gene mutation frequency, higher tumor mutational burden (p = 0.034), and better chemotherapy response (p &lt; 0.001). The signature was successfully validated using the GSE26939, GSE31210, GSE30219, and GSE157009 datasets. Dysregulation of these genes in patients with NSCLC was confirmed using the qPCR in an independent cohort (p &lt; 0.05).

Conclusion: An RBP-related signature was successfully constructed to predict prognosis in NSCLC, functioning as a reference for individualized therapy, including immunotherapy and chemotherapy.

Pan-Cancer Analysis Predicts the Immunological and Prognostic Role of ZC3H12C in KIRC

ZC3H12C is an important member of the CCCH-zinc finger protein family and is mainly involved in host immune and inflammatory diseases. However, its abnormal expression and prognostic value in cancer have not yet been established. Through comparative analysis of the Cancer Genome Atlas (TCGA) database, we found that ZC3H12C is the most relevant to the prognosis, grade, and stage of renal clear cell carcinoma (ccRCC) across 33 cancers. With the help of patient transcription and clinical data from the TCGA and GEO (GSE53757, GSE36895) databases, we determined that in the immune environment of patients with ccRCC, ZC3H12C was clearly negatively correlated with Tregs and was significantly positively correlated with monocytes. In addition, protein phosphorylation and DNA methylation analysis also showed that ZC3H12C negatively regulates the role of cancer in ccRCC. Our research may provide new insights into ccRCC immunotherapy and bring forth novel biomarkers and therapeutic targets.

Bioinformatic Investigation of Micro RNA-802 Target Genes, Protein Networks, and Its Potential Prognostic Value in Breast Cancer

Background

An increasing number of studies have suggested that unveiling the molecular network of miRNAs may provide novel therapeutic targets or biomarkers. In this study, we investigated the probable molecular functions that are related to microRNA-802 (miR-802) and evaluated its prognostic value in breast cancer utilizing bioinformatics tools.

Methods

PPI network, pathway enrichment and transcription factor analysis were applied to obtain hub genes among overlapping genes of four miRNA target prediction databases. Prognosis value assessments and expression analysis of hub genes using bioinformatics tools, as well as their literature validation were performed.

Results

Our results showed a significant correlation of the miR-802 overexpression with poor patient survival rate (BC, p=2.7e-5). We determined 247 target genes significant for GO and KEGG terms. Analysis of TFs by TRUST showed that RUNX3, FOXO3, and E2F1 are possible TFs that regulate the miR-802 expression and target genes network. According to our analysis; 21 genes might have an important function in miR-802 molecular processes and regulatory networks. The result shows that among these 21 genes, 8 genes (CASC3, ITGA4, AGO3, TARDBP, MED13L, SF1, SNRPE and CRNKL1) are positively correlated with patient survival. Therefore these genes could be considered and experimentally evaluated as a prognostic biomarker for breast cancer.

Conclusion

The comprehensive bioinformatics study on miR-802 target genes provided insight into miR-802 mediated pathways and processes. Furthermore, representing candidate target genes by prognostic values indicates the potential clinical application of miR-802 in breast cancer.

Identification of a ZC3H12D-regulated competing endogenous RNA network for prognosis of lung adenocarcinoma at single-cell level

Background

To identify hub genes from the competing endogenous RNA (ceRNA) network of lung adenocarcinoma (LUAD) and to explore their potential functions on prognosis of patients from a single-cell perspective.

Methods

We performed RNA-sequencing of LUAD to construct ceRNA regulatory network, integrating with public databases to identify the vital pathways related to patients’ prognosis and to reveal the expression level of hub genes under different conditions, the functional enrichment of co-expressed genes and their potential immune-related mechanisms.

Results

ZC3H12D-hsa-miR-4443-ENST00000630242 axis was found to be related with LUAD. Lower ZC3H12D expression was significantly associated with shorter overall survival (OS) of patients (HR = 2.007, P < 0.05), and its expression was higher in early-stage patients, including T1 (P < 0.05) and N0 (P < 0.05). Additionally, ZC3H12D expression was higher in immune cells displayed by single-cell RNA-sequencing data, especially in Treg cells of lung cancer and CD8 T cells, B cells and CD4 T cells of LUAD. The functional enrichment analysis showed that the co-expressed genes mainly played a role in lymphocyte activation and cytokine-cytokine receptor interaction. In addition, ZC3H12D was associated with multiple immune cells and immune molecules, including immune checkpoints CTLA4, CD96 and TIGIT.

Conclusion

ZC3H12D-hsa-miR-4443-ENST00000630242 ceRNA network was identified in LUAD. ZC3H12D could affect prognosis of patients by regulating mRNA, miRNA, lncRNA, immune cells and immune molecules. Therefore, it may serve as a vital predictive marker and could be regarded as a potential therapeutic target for LUAD in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08992-1.