Construction of exosome non-coding RNA feature for non-invasive, early detection of gastric cancer patients by machine learning: a multi-cohort study

Abstract

Background and objective: Gastric cancer (GC) remains a prevalent and preventable disease, yet accurate early diagnostic methods are lacking. Exosome non-coding RNAs (ncRNAs), a type of liquid biopsy, have emerged as promising diagnostic biomarkers for various tumours. This study aimed to identify a serum exosome ncRNA feature for enhancing GC diagnosis.

Designs: Serum exosomes from patients with GC (n=37) and healthy donors (n=20) were characterised using RNA sequencing, and potential biomarkers for GC were validated through quantitative reverse transcription PCR (qRT-PCR) in both serum exosomes and tissues. A combined diagnostic model was developed using LASSO-logistic regression based on a cohort of 518 GC patients and 460 healthy donors, and its diagnostic performance was evaluated via receiver operating characteristic curves.

Results: RNA sequencing identified 182 candidate biomarkers for GC, of which 31 were validated as potential biomarkers by qRT-PCR. The combined diagnostic score (cd-score), derived from the expression levels of four long ncRNAs (RP11.443C10.1, CTD-2339L15.3, LINC00567 and DiGeorge syndrome critical region gene (DGCR9)), was found to surpass commonly used biomarkers, such as carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9) and CA72-4, in distinguishing GC patients from healthy donors across training, testing and external validation cohorts, with AUC values of 0.959, 0.942 and 0.949, respectively. Additionally, the cd-score could effectively identify GC patients with negative gastrointestinal tumour biomarkers and those in early-stage. Furthermore, molecular biological assays revealed that knockdown of DGCR9 inhibited GC tumour growth.

Conclusions: Our proposed serum exosome ncRNA feature provides a promising liquid biopsy approach for enhancing the early diagnosis of GC.

Keywords: GASTRIC CANCER; RNA EXPRESSION; TUMOUR MARKERS.

Figure 1. Overview of the study design. The figure illustrates the overall workflow of the study, including the key stages of data collection and analysis. HD, healthy donors; GC, gastric cancer; PL, precancerous lesions.

Figure 2. Screening of exosome-derived ncRNAs. (A) Analysis of lncRNAs in gastric cancer patients compared with normal controls using transcriptome data from serum exosomes, tissues and the TCGA STAD cohort. Criteria for selection include p<0.05 and log₂FoldChange>1. (B) Venn diagram depicting the overlap of upregulated lncRNAs across serum exosomes, tissues and the TCGA STAD cohort. (C) Analysis of circRNAs in gastric cancer patients compared with normal controls using transcriptome data from serum exosomes and tissues. Criteria for selection include p<0.05 and log₂FoldChange>1. (D) Venn diagram showing the overlap of upregulated circRNAs in serum exosomes and tissues. Red and blue in A and C represent high and low levels of relative expression of non-coding RNAs. circRNA, circular RNA; GC, gastric cancer; lncRNA, long non-coding RNA; N, non-cancer control; STAD, stomach adenocarcinoma; SYSUCC, Sun Yat-sen University Cancer Center; T, tumour; TCGA, The Cancer Genome Atlas.

Figure 3. ROC curves for determining diagnostic performance. The ROC curves of the cd-score, CEA, CA19-9 and CA72-4 in the training, testing and external validation cohorts. CA19-9, carbohydrate antigen 19-9; CA72-4, carbohydrate antigen 72-4; cd-score, combined diagnostic model score; CEA, carcinoembryonic antigen; ROC, receiver operating characteristic.

Figure 4. Application of the cd-score. (A) Diagnostic performance of the cd-score for detecting GC patients with negative CEA, CA19-9 and CA72-4 in the training, testing and external validation cohorts. (B) Performance of the cd-score in distinguishing early-stage GC patients from healthy donors compared with CEA, CA19-9 and CA72-4. (C) The cd-score values of the different groups in the prediction cohort. (D) ROC curves for the cd-score, CEA, CA19-9 and CA72-4 in differentiating between any two groups in the prediction cohort. CA19-9, carbohydrate antigen 19-9; CA72-4, carbohydrate antigen 72-4; cd-score, combined diagnostic model score; CEA, carcinoembryonic antigen; GC, gastric cancer; NTB, negative for three biomarkers; ROC, receiver operating characteristic.

Figure 5. Impact of DGCR9 knockdown on GC cell proliferation. (A) Expression levels of DGCR9 in exosomes from GC patients (n=112), those with precancerous lesions (n=73) and healthy donors (n=100). (B) MTS assay assessing the effect of DGCR9 knockdown on HGC27 cell growth. (C) Clonogenic assay showing colony formation in DGCR9-knockdown HGC27 cells compared with control cells. (D) Statistical analysis of the clonogenic assays on HGC27 and MKN74 cells. (E) Glycolytic proton efflux rate (glycoPER) changes in DGCR9-knockdown HGC27 cells compared with control cells and statistical analysis. (F) Growth curves and tumour weights of DGCR9-knockdown HGC27 cells versus control cells in subcutaneous tissues of SCID mice (NCG mice). (G) Growth curve and tumour weights of PDX models in SCID mice (NCG mice) that were subcutaneously implanted with tumour tissues from two GC patients and intratumorally injected with scrambled or DGCR9 inhibitors (5 nmol per injection) every 3 days for five times. (H) Representative H&E staining and IHC images (Ki67 and TUNEL) of PDX tissue sections from each group. Scale bar: 100 µm. (I) Quantification of Ki67 and TUNEL expression in PDX tissues treated with scrambled or DGCR9 inhibitors. (J) Representative H&E and IHC images of Ki67 and TUNEL in GC tissues from patients with high (n=23) or low (n=23) serum exosome DGCR9 levels. Scale bar: 100 µm. (K) Percentage of specimens with high or low Ki67 and TUNEL index in high versus low DGCR9 groups. The numbers of biological replicates were three (B–D), four (E) and six (F–I), respectively. Data in A–B, D–G and I were presented as mean±SD.D. P values were determined by one-way ANOVA (A, D, E, tumour weight in F), two-way ANOVA (B, tumour volume in F–G), two-tailed unpaired Student’s t-test (tumour weight in G, I) and χ² test (K). ASO, antisense oligonucleotide; DGCR9, DiGeorge Critical Region 9; GC, gastric cancer; GlycoPER, glycolytic proton efflux rat; HD, healthy donor; IHC, immunohistochemistry; Ki67, a proliferation marker; MTS, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; PDX, patient-derived xenograft; PL, precancerous lesion; Rot/AA, rotenone and antimycin A; SCID, severe combined immunodeficiency; TUNEL, terminal deoxynucleotidyl transferase dUTP nick end labelling; 2-DG, 2-deoxy-D-glucose; *p<0.05; **p<0.01; ***p<0.001.