Plasma exosomes proteome profiling discovers protein markers to identify sepsis
Abstract Sepsis, a life-threatening clinical issue caused by infection, can be diagnosed early through the analysis of exosomes in plasma. Plasma exosomes were extracted and identified by group (a sepsis group, a non-sepsis group, and a healthy control group). iTRAQ was used to identify the differentially expressed proteins among the three groups. The identified proteins were measured by ELISA in plasma samples. LCN2 and MGP were differentially expressed among the three groups. The median level of LCN2 were 131.93 (96.26,183.65), 52.79 (31.39,89.05) and 33.80 (26.80,47.06) ng/ml in the sepsis, non-sepsis and healthy control group, respectively. The plasma MGP levels in these three groups were 1.16 (1.06,1.30), 0.99 (0.91,1.07) and 0.89 (0.80,1.02) ng/ml, respectively. For both biomarkers, the sepsis group had higher levels than the non-sepsis group(P < 0.05). The area under the curve values of LCN2 and MGP were 0.903 and 0.813, respectively. When combined, these two biomarkers had a sensitivity of 0.900 and a specificity of 0.900 in distinguishing sepsis from non-sepsis. Plasma exosome proteome analysis has revealed that LCN2 and MGP may serve as effective biomarkers for diagnosing sepsis, further research is necessary to validate their diagnostic efficiency..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
ResearchSquare.com - (2024) vom: 19. März Zur Gesamtaufnahme - year:2024 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Gong, Siyin [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.21203/rs.3.rs-4051424/v1 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XRA042976731 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | XRA042976731 | ||
| 003 | DE-627 | ||
| 005 | 20240320132627.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240320s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.21203/rs.3.rs-4051424/v1 |2 doi | |
| 035 | |a (DE-627)XRA042976731 | ||
| 035 | |a (ResearchSquare)10.21203/rs.3.rs-4051424/v1 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Gong, Siyin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Plasma exosomes proteome profiling discovers protein markers to identify sepsis |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Sepsis, a life-threatening clinical issue caused by infection, can be diagnosed early through the analysis of exosomes in plasma. Plasma exosomes were extracted and identified by group (a sepsis group, a non-sepsis group, and a healthy control group). iTRAQ was used to identify the differentially expressed proteins among the three groups. The identified proteins were measured by ELISA in plasma samples. LCN2 and MGP were differentially expressed among the three groups. The median level of LCN2 were 131.93 (96.26,183.65), 52.79 (31.39,89.05) and 33.80 (26.80,47.06) ng/ml in the sepsis, non-sepsis and healthy control group, respectively. The plasma MGP levels in these three groups were 1.16 (1.06,1.30), 0.99 (0.91,1.07) and 0.89 (0.80,1.02) ng/ml, respectively. For both biomarkers, the sepsis group had higher levels than the non-sepsis group(P < 0.05). The area under the curve values of LCN2 and MGP were 0.903 and 0.813, respectively. When combined, these two biomarkers had a sensitivity of 0.900 and a specificity of 0.900 in distinguishing sepsis from non-sepsis. Plasma exosome proteome analysis has revealed that LCN2 and MGP may serve as effective biomarkers for diagnosing sepsis, further research is necessary to validate their diagnostic efficiency. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Liu, Tingting |4 aut | |
| 700 | 1 | |a Li, Ziwei |4 aut | |
| 700 | 1 | |a Yang, Yixuan |4 aut | |
| 700 | 1 | |a Yi, Hong |4 aut | |
| 700 | 1 | |a Wang, Xiaohao |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t ResearchSquare.com |g (2024) vom: 19. März |
| 773 | 1 | 8 | |g year:2024 |g day:19 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.21203/rs.3.rs-4051424/v1 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XRA | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 19 |c 03 | ||