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. 2024 Jan 12;15(5):1299-1313.
doi: 10.7150/jca.92102. eCollection 2024.

New plasma diagnostic markers for colorectal cancer: transporter fragments of glutamate tRNA origin

Changda Ye  1   2 , Fu Cheng  1   2 , Luji Huang  1   2 , Kang Wang  1   2 , Lin Zhong  1   2 , Yan Lu  3 , Manzhao Ouyang  1   2
Affiliations

New plasma diagnostic markers for colorectal cancer: transporter fragments of glutamate tRNA origin

Changda Ye et al. J Cancer. .

Abstract

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Early diagnosis of the disease can greatly improve the clinical prognosis for patients with CRC. Unfortunately, there are no current simple and effective early diagnostic markers available. The transfer RNA (tRNA)-derived RNA fragments (tRFs) are a class of small non-coding RNAs (sncRNAs), which have been shown to play an important role in the development and prognosis of CRC. However, only a few studies on tRFs as early diagnostic markers in CRC have been conducted. In this study, previously ignored tRFs expression data were extracted from six paired small RNA sequencing data in the Sequence Read Archive (SRA) database using MINTmap. Three i-tRFs, derived from the tRNA that transports glutamate (i-tRF-Glu), were identified and used to construct a random forest diagnostic model. The model performance was evaluated using the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. The area under the curves (AUC) for the ROC and PR was 0.941 and 0.944, respectively. We further verified the differences in expression of the these i-tRF-Glu in the tissue and plasma of both CRC patients and healthy subjects using quantitative real-time PCR (qRT-PCR). We found that the ROC-AUC of the three was greater than traditional plasma tumor markers such as CEA and CA199. Our bioinformatics analysis suggested that the these i-tRF-Glu are associated with cancer development and glutamate (Glu)-glutamine (Gln) metabolism. Overall, our study uncovered these i-tRF-Glu that have early diagnostic significance and therapeutic potential for CRC, this warrants further investigation into the diagnostic and therapeutic potential of these i-tRF-Glu in CRC.

Keywords: colorectal cancer; glutamate metabolism; i-tRF; liquid biopsy; tRNA-derived fragments.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Workflow of data process. Expression levels of tRFs were extracted using small RNA sequencing data from the SRA database of paired colon cancer and paracancerous tissues (Upper left panel). Random forest analysis was used to screen tRFs, and 3 tRFs were finally identified (upper right panel). Bioinformatics analysis: target gene prediction, function and pathway enrichment analysis (lower left panel). Expression of the 3 tRFs was verified at tissue and plasma levels using qRT-PCR (lower right panel).
Figure 2
Figure 2
Differential analysis and the composition analysis of the intersection tRFs. (A) Volcano plot of Limma package differential analysis. (B) Volcano plot of Deseq2 package differential analysis. (C) Composition analysis of the intersection tRFs: (a) tRF-Glu accounted for 21.77% of the intersection tRFs. (b) tRF-Glu accounted for 46.43% of the i-tRFs. (c) i-tRF accounted for 35.33% of the intersection tRFs. (d) i-tRF was 75.36% in tRF-Glu.
Figure 3
Figure 3
Heat map of the expression of the top 100 difference tRFs. Differential analysis of the tRFs using Limma package and DEseq2 package, the expression heat map displays the top 100 intersecting tRFs of LogFC absolute values.
Figure 4
Figure 4
Exploring the diagnostic efficacy of tRF-22/27/32 for CRC. (A) Random forest RF mean decrease Gini score rank. (B) ROC curves of the random forest diagnostic model in the training set and independent validation set. (C) PR curves of the random forest diagnostic model. (D~F) Separate ROC curves for tRF-22/27/32 in the training set (logistic regression analysis).
Figure 5
Figure 5
Enrichment analysis of database predicted target genes (intersection). (A~C) Network analysis of tRF-22/27/32 with predicted target genes. (D~F) Venn diagram of four database predicted target genes of tRF-22/27/32. (G) KEGG enrichment analysis of tRF-22/27/32 target genes. (H) GO enrichment analysis of tRF-22/27/32 target genes.
Figure 6
Figure 6
Correlation analysis of tRF22/27/32 with glutamate metabolism-related genes. The bottom left corner is the connection is the expression analysis of tRF-22/27/32 with glutamate-related genes, and the top right corner is the expression correlation analysis between glutamate-related genes. Glutamate-related gene expression information was obtained from the GEO database (GSE121842).
Figure 7
Figure 7
qRT-PCR to validate the differential expression of tRF-22/27/32. (A~C) qRT-PCR validation of tRF-22/27/32 expression differences in tissues. (D~F) qRT-PCR validation of tRF-22/27/32 expression in plasma of CRC patients and normal volunteers. (G) ROC curves of tRF-22/27/32, CEA, and CA199 in CRC patients and normal individuals.
Figure 8
Figure 8
Correlation analysis of clinical information. (A) Heat map of correlation between patient clinical information and tRF-22/27/32. (B~C) Correlation analysis of CA199 and tRF-22/27. (D~E) Correlation analysis of CEA and tRF-27/32. (G) Correlation between the number of lymph node metastases and tRF-32 was demonstrated.
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