Research Article

Circulating small extracellular vesicle RNA profiling for the detection of T1a stage colorectal cancer and precancerous advanced adenoma

Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, State Key Laboratory of Digestive Health, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, China
Key Laboratory of Bio-inspired Materials and Interfacial Science, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, China
Echo Biotech Co., Ltd, China
Department of Retroperitoneal Tumor Surgery, International Hospital, Peking University, China

Aug 9, 2024

https://doi.org/10.7554/eLife.88675.4

Open access
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Figures
Tables
Additional files

7 figures, 2 tables and 14 additional files

Figures

Figure 1

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Figure 2 with 2 supplements

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Transcriptome profiling of circulating sEVs.

(a) TEM images of circulating sEVs isolated from human plasma. (b) NTA results of circulating sEVs enriched from plasma. (c) WB results of sEV positive (Alix, TSG101, CD9) and negative (Calnexin) markers. (d) The numbers of detected RNA species in different groups. (e) The hierarchical clustering results of top 100 miRNAs (left panel), mRNAs (middle panel), and lncRNAs (right panel). (f) t-SNE clustering by those candidate RNAs. (g) A Venn diagram showed DEGs shared between different comparisons (CRC vs NC, AA vs NC, CRC vs AA). (h) KEGG enrichment of all those DEGs identified. (**i–k**) potential core regulatory networks between miRNAs and mRNAs in DEGs identified in three (i), two (j), and one (k) of all comparisons.

Figure 2—source data 1 Original file for the western blot analysis in Figure 2C (anti-Alix, anti-CD9, anti-TSG101, and anti-Calnexin).: https://cdn.elifesciences.org/articles/88675/elife-88675-fig2-data1-v1.zip
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Figure 2—source data 2 Figures containing Figure 2C and original scans of the relevant western blot analysis (anti-Alix, anti-CD9, anti-TSG101, and anti-Calnexin) with highlighted bands and sample labels.: https://cdn.elifesciences.org/articles/88675/elife-88675-fig2-data2-v1.zip
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Figure 2—figure supplement 1

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The hierarchical clustering results of Top 100 miRNAs/mRNAs/lncRNAs.

Figure 2—figure supplement 2

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Unsupervised t-SNE clustering by those 200 RNAs with nine repeats.

Figure 3 with 1 supplement

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Cell-specific features of the sEV-RNA profile.

(a) The hierarchical clustering heatmap of immune cell-specific features of each sample. (b) The hierarchical clustering heatmap of stromal-related features of each sample. (c) Boxplot of cell-specific features overexpressed in CC and RC patients (*p<0.05, **p<0.01, ***p<0.001). (d) Boxplot of cell-specific features overexpressed in NC participants (*p<0.05, **p<0.01, ***p<0.001). (e) The violinplot of the microenvironmental scores in different subgroups (*p<0.05, **p<0.01, ***p<0.001).(f) Correlation among cell-specific features differentially enriched among different groups.

Figure 3—figure supplement 1

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Cell-specific features of the sEV-RNA profile.

(A) The hierarchical clustering heatmap of different cell features in all sEV samples. (B) Correlation among all cell-specific features.

Figure 4 with 1 supplement

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WGCNA analysis of sEV-RNAs.

(a) Gene coexpression module construction of all DEGs identified in sEV-RNAs. (b) The heatmap exhibited Pearson correlations among different modules. (c) Bar plot of module composition of different modules (all DEGs). (d) Percentage bar plot of the RNA composition of different modules (all DEGs). (e) A heatmap exhibited the expression levels of the top 10 DEGs in each module. (f) t-SNE clustering by the top 10 DEGs in each module. (g) t-SNE clustering by the top 5 DEGs in each module. (h) t-SNE clustering by the top1 DEGs in each module.

Figure 4—figure supplement 1

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Proportions and numbers of RNA species in different modules.

(A) Percentage barplot of the RNA composition of different modules (only DEGs with kME >0.7). (B) Barplot of module composition of different modules (only DEGs with kME >0.7).

Figure 5

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The expression trends of sEV-RNA modules.

(**a–c**) GSEA analysis of DEGs in different modules (a: CRC vs. NC; b: AA vs. NC; c: CRC vs. AA). (**d-i**) The expression trends of the Top 10 DEGs of each module among NC, AA, and CRC (d: green module; e: red module; f: turquoise module; g: black module; h: blue module; i: brown module).

Figure 6

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Module-trait correlation analysis of sEV-RNA modules.

(a) The heatmap exhibited the correlation between modules and clinical traits. (b) The RT-qPCR validation of representive module-trait correlation (left panel: correlation between MT-ND2 and Paris classification; right panel: correlation between HIST2H2AA4 and LST morphology). (c) The heatmap exhibited the sEV-RNA expression levels of red, black, and green modules. (**d-f**) Circos plot showed the inner correlations among sEV-RNAs in the module green (d), red (e), and black (f).

Figure 7 with 3 supplements

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The plasma sEVs-RNA signature to detect early CRC and AA.

(**a–d**) The ROC analysis of different sEV-RNA signatures in the prediction of CRC patients by different algorithms (a: 5-gene panel; b: 6-gene panel; c: 7-gene panel; d: 8-gene panel). (**e–h**) The ROC analysis of different sEV-RNA signatures in the prediction of AA patients by different algorithms (e: 6-gene panel; f: 7-gene panel; g: 8-gene panel; h: 9-gene panel). (i) The QDA results of all 13 sEV-RNAs in classifying all samples. (j) Statistical summary of QDA performance in each sample group.

Figure 7—figure supplement 1

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Lasso regression to construct multivariate prediction models.

(A) Performance of Lasso regression in variable selection to identify CRC. (B) Performance of Lasso regression in variable selection to identify AA.

Figure 7—figure supplement 2

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The ROC analysis of different sEV-RNA signatures in the prediction of stage I CRC patients by different algorithms (a: 6-gene panel; b: 7-gene panel; c: 8-gene panel; d: 9-gene panel).

Figure 7—figure supplement 3

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The ROC analysis of different sEV-RNA signatures for predicting CRC patients using the Lasso regression algorithm in different clinical parameters (ab: age; cd: gender; ef: tumor size; gh: anatomical position).

Tables

Table 1

Selection of plasma sEVs-RNA candidates for AA and T1a stage CRC diagnosis.

Candidate	AA vs NC	CRC vs NC	CRC vs AA	Module attribution	RNA Type	Amount	Finally seclected
miR-3615	+*	+	-^†	brown	miRNA	High	Yes
miR-330–5 p	+	+	-	brown	miRNA	Low	No
miR-425–5 p	+	+	-	NA	miRNA	High	Yes
miR-106b-3p	+	+	-	NA	miRNA	High	Yes
miR-589–5 p	+	+	-	NA	miRNA	Low	No
miR-181a-2–3 p	+	+	-	brown	miRNA	Low	No
Let-7f-5p	-	-	+	NA	miRNA	High	Yes
Let-7e-5p	-	-	+	NA	miRNA	High	No
miR-320a/b-3p	-	-	+	brown	miRNA	High	Yes
miR-664a-5p	-	-	+	brown	miRNA	Low	No
YBX3	-	+	+	turquoise	mRNA	Low	No
C19orf43	-	+	+	turquoise	mRNA	Medium	Yes
TOP1	+	+	-	turquoise	mRNA	Medium	Yes
PPDPF	+	+	-	brown	mRNA	Medium	Yes
MT-ND2	+	+	-	blue	mRNA	High	Yes
HIST2H2AA4	+	+	-	green	mRNA	Medium	Yes
RPL10	+	+	-	green	mRNA	High	No
RPS29	+	+	-	blue	mRNA	High	No
IST1	-	+	+	black	mRNA	Low	No
CSE1L	-	+	-	red	mRNA	Low	No
lnc-MSI1-2:1	+	+	-	brown	lncRNA	High	Yes
lnc-FCGR1B-16:1	-	+	+	red	lncRNA	Low	No
lnc-NPY4R2-105:1	-	+	+	red	lncRNA	Medium	No
lnc-MKRN2-42:1	-	+	+	turquoise	lncRNA	High	Yes
LNC_EV_9572(Chr8: 34358093–34456247)	+	+	-	black	lncRNA	High	Yes
LNC_EV_21004(Chr21: 8212554–8440060)	+	+	-	brown	lncRNA	High	No
LNC_EV_15260(Chr14: 49555875–49923916)	+	+	-	turquoise	lncRNA	High	No

*

+: significant difference found in this comparision.
†

-: no significant difference found in this comparision.

Table 2

CRC and AA prediction models established by Lasso regression.

Additionally, we adopted quadratic discriminant analysis (QDA) to demonstrate the possibility of the plasma sEV-RNA signature for direct sample classification. An overall accuracy of 78% (ranging from 63% to 100%) was obtained for direct sample classification (Figure 7ij). Generally, the individuals classified into AA/CC/RC are considered as high-risk and should be advised to further endoscopic examination, and our QDA classifier provided a specificity of 79.25%, and a sensitivity of 99.0% (with only one CC sample missed) in identifying those high-risk individuals. Together, our RT-qPCR-based plasma sEVs-RNA signature could be a powerful and better alternative to FIT and FOBT tests in CRC and precancerous AA screening programs.

Model type	Signature	RNAs	Lambda	AUC
CRC prediction	5-RNA signature	Let-7f-5p, C19orf43, TOP1, PPDPF, lnc-MKRN2-42:1	0.05	0.73
	6-RNA signature	Let-7f-5p, C19orf43, TOP1, PPDPF, lnc-MKRN2-42:1, LNC-EV-9572	0.035	0.73
	7-RNA signature	Let-7f-5p, C19orf43, TOP1, PPDPF, lnc-MKRN2-42:1, LNC-EV-9572, HIST2H2AA4	0.03	0.74
	8-RNA signature	Let-7f-5p, C19orf43, TOP1, PPDPF, lnc-MKRN2-42:1, LNC-EV-9572, HIST2H2AA4, miR-320a-3p	0.02	0.76
AA prediction	6-RNA signature	miR-425–5 p, Let-7f-5p, C19orf43, TOP1, PPDPF, LNC-EV-9572	0.05	0.83
	7-RNA signature	miR-425–5 p, Let-7f-5p, C19orf43, TOP1, PPDPF, LNC-EV-9572, lnc-MKRN2-42:1	0.04	0.84
	8-RNA signature	miR-425–5 p, Let-7f-5p, C19orf43, TOP1, PPDPF, LNC-EV-9572, lnc-MKRN2-42:1, HIST2H2AA4	0.1	0.87
	9-RNA signature	miR-425–5 p, Let-7f-5p, C19orf43, TOP1, PPDPF, LNC-EV-9572, lnc-MKRN2-42:1, HIST2H2AA4, MT-ND2	0.05	0.88