Mutational alterations of TDRD 1, 4 and 9 genes in colorectal cancers

Investigating the role of prognostic mitophagy-related genes in non-small cell cancer pathogenesis via multiomics and network-based approach

As one of the most prevalent malignancies, lung cancer displays considerable biological variability in both molecular and clinical characteristics. Lung cancer is broadly categorized into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) with the latter being most prevalent. The primary histological subtypes of NSCLC are lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). In the present work, we primarily extracted mRNA count data from a publicly accessible database followed by differentially expressed genes (DEGs) and differentially expressed mitophagy-related genes (DEMRGs) identification in case of both LUAD and LUSC cohorts. Next, we identified important DEMRGs via clustering approach followed by enrichment, survival, and mutational analyses. Lastly, the finalized prognostic biomarker was validated using wet-lab experimentations. Primarily, we obtained 986 and 1714 DEGs across LUAD and LUSC cohorts. Only 7 DEMRGs from both cohorts had significant membership values as indicated by the clustering analysis. Most significant pathway, Gene Ontology (GO)-biological process (BP), GO-molecular function (MF), GO-cellular compartment (CC) terms were macroautophagy, GTP metabolic process, magnesium ion binding, mitochondrial outer membrane. Among all, only TDRKH reported significant overall survival (OS) and 14% amplification across LUAD patients. Lastly, we validated TDRKH via immunohistochemistry (IHC) and semi-quantitative polymerase chain reaction (PCR). In conclusion, our findings advocate for the exploration of TDRKH and their genetic alterations in precision oncology therapeutic approaches for LUAD, emphasizing the potential for target-driven therapy and early diagnostics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04127-y.

Development and validation of an RBP gene signature for prognosis prediction in colorectal cancer based on WGCNA

BACKGROUND

RNA binding proteins (RBPs) have been implicated in oncogenesis and progression in various cancers. However, the potential value of RBPs as prognostic indicators and therapeutic targets in colorectal cancer (CRC) requires further investigation.

METHODS

Four thousand eighty two RBPs were collected from literature. The weighted gene co-expression network analysis (WGCNA) was performed to identify prognosis-related RBP gene modules based on the data attained from the TCGA cohorts. LASSO algorithm was conducted to establish a prognostic risk model, and the validity of the proposed model was confirmed by an independent GEO dataset. Functional enrichment analysis was performed to reveal the potential biological functions and pathways of the signature and to estimate tumor immune infiltration. Potential therapeutic compounds were inferred utilizing CMap database. Expressions of hub genes were further verified through the Human Protein Atlas (HPA) database and RT-qPCR.

RESULTS

One thousand seven hundred thirty four RBPs were differently expressed in CRC samples and 4 gene modules remarkably linked to the prognosis were identified, based on which a 12-gene signature was established for prognosis prediction. Multivariate Cox analysis suggested this signature was an independent predicting factor of overall survival (P < 0.001; HR:3.682; CI:2.377-5.705) and ROC curves indicated it has an effective predictive performance (1-year AUC: 0.653; 3-year AUC:0.673; 5-year AUC: 0.777). GSEA indicated that high risk score was correlated with several cancer-related pathways, including cytokine-cytokine receptor cross talk, ECM receptor cross talk, HEDGEHOG signaling cascade and JAK/STAT signaling cascade. ssGSEA analysis exhibited a significant correlation between immune status and the risk signature. Noscapine and clofazimine were screened as potential drugs for CRC patients with high-risk scores. TDRD5 and GPC1 were identified as hub genes and their expression were validated in 15 pairs of surgically resected CRC tissues.

CONCLUSION

Our research provides a depth insight of RBPs' role in CRC and the proposed signature are helpful to the personalized treatment and prognostic judgement.

PIWI-Interacting RNA Pathway Genes: Potential Biomarkers for Clear Cell Renal Cell Carcinoma

Background. Clear cell renal cell carcinoma (ccRCC) is one of the most lethal malignancies in the urinary system, yet effective diagnostic and prognostic markers are lacking. Recently, several of piRNA pathway genes have been reported to be associated with cancer diagnosis and prognosis, but their role in ccRCC is still unclear. Methods. We analysed the expression of 27 piRNA pathway genes in 539 kidney renal clear cell carcinoma (KIRC) and 72 nontumor tissue samples (data from TCGA), and 12 mRNAs were significantly different. The aim was to sift the piRNA pathway genes that are correlated with ccRCC patient survival and to construct a piRNA pathway gene risk prognostic model using Kaplan-Meier survival curve and ROC curve, respectively. Results. 5 piRNA pathway genes (TDRD7, GPAT2, PLD6, SUV39H1, and DOM3Z) were picked out and used to construct the piRNA pathway gene risk model. Kaplan-Meier survival curve analysis showed that compared with that of the low-risk group of ccRCC patients, the OS of the high-risk group of ccRCC patients was significantly reduced. The predictive performance of the prognostic risk model was measured using a ROC curve, which individually showed AUC values for 1 year of 0.707, for 3 years of 0.713, and for 5 years of 0.701. Moreover, the mRNA and protein expression levels of TDRD7 were overexpressed in the ccRCC datasets (data from our cohort, TCGA, GEO, and CPTAC) and ccRCC cell lines, and the expression levels correlated with the clinicopathological characteristics in ccRCC. The Tumor Immune Estimation Resource (TIMER) showed that the mRNA expression level of TDRD7 was positively related to tumor immune infiltrating cells (TICs) in ccRCC. Mechanistically, gene set enrichment analysis (GSEA) was performed to uncover the mechanism of TDRD7 in ccRCC. In summary, the piRNA pathway genes,especially TDRD7, may be potential cancer diagnostic and prognostic biomarkers of ccRCC.

Integrated analysis of RNA-binding proteins in human colorectal cancer

Background

Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients.

Methods

Analysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. Finally, the risk curves and nomograms of the two groups were generated to predict the survival period.

Results

Among RBP DEGs, 314 genes were upregulated while 155 were downregulated, of which twelve RBPs (NOP14, MRPS23, MAK16, TDRD6, POP1, TDRD5, TDRD7, PPARGC1A, LIN28B, CELF4, LRRFIP2, MSI2) with prognostic value were obtained.

Conclusions

The twelve identified genes may be promising predictors of CRC and play an essential role in the pathogenesis of CRC. However, further investigation of the underlying mechanism is needed.