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Editor’s note iNature is China’s largest academic official account.
It is jointly created by the doctoral team of Tsinghua University, Harvard University, Chinese Academy of Sciences and other units.
The iNature Talent Official Account is now launched, focusing on talent recruitment, academic progress, scientific research information, interested parties can Long press or scan the QR code below to follow us
.
iNature prostate cancer (PCa) is the second most common cancer in men
.
Currently, serum prostate-specific antigen (PSA) is the only widely used biomarker for prostate cancer
.
Unfortunately, the low specificity (25-40%) of PSA in the gray area of the so-called PSA level of 2.
0-10.
0 ng/mL has led to a significant increase in the detection of benign unnecessary biopsies and clinically indolent diseases
.
Therefore, there is an urgent need for more precise measures to identify PCa with clinical significance
.
On July 23, 2021, Li Liaoyuan’s team from Sun Yat-sen University published an online publication titled "A urine extracellular vesicle circRNA classifier for detection of high-grade prostate cancer in patients with prostate-specific antigen 2–10" in Molecular Cancer (IF=27.
40).
ng/mL at initial biopsy" research paper, the purpose of this research is to determine a urine extracellular vesicle circular RNA (circRNA) classifier that can detect grade group (GG) 2 or higher Prostate cancer (PCa)
.
To this end, the study used RNA sequencing to identify candidate circRNAs from the urine extracellular vesicles of 11 high-grade prostate cancer patients and 11 case-matched benign prostatic hyperplasia patients
.
Using ddPCR in the training cohort (n = 263), we constructed a urine extracellular vesicle circRNA classifier (Ccirc, including circPDLIM5, circSCAF8, circPLXDC2, circSCAMP1 and circCCNT2), and evaluated it in two independent cohorts (n = 497, n = 505)
.
Ccirc showed higher accuracy than the two standard care risk calculators (RC) (PCPT-RC 2.
0 and ERSPC-RC) in both the training cohort and the validation cohort
.
In all three cohorts, this new urine extracellular vesicle circRNA classifier plus RC was statistically more predictive than RC alone in predicting ≥ GG2 PCa
.
This test does not require pre-collection of digital rectal examination or special treatment, is reproducible, non-invasive, and can be easily implemented as part of the basic clinical workflow
.
Prostate cancer (PCa) is the second most common cancer in men
.
Currently, serum prostate-specific antigen (PSA) is the only widely used biomarker for prostate cancer
.
Unfortunately, the low specificity (25-40%) of PSA in the gray area of the so-called PSA level of 2.
0-10.
0 ng/mL has led to a significant increase in the detection of benign unnecessary biopsies and clinically indolent diseases
.
Therefore, there is an urgent need for more precise measures to identify PCa with clinical significance
.
According to reports, many non-coding RNAs (for example, microRNA, long-chain non-coding RNA, circular RNA [circRNA]) play a key role in cancer progression, showing great potential to influence cancer diagnosis
.
Especially in PCa, 76,311 circRNAs have been identified through RNA sequencing of tumor specimens
.
Interestingly, cancer-specific non-coding RNAs have been identified in extracellular vesicles
.
Compared with linear RNAs, circRNAs have covalently linked ends of a single RNA molecule and exhibit higher stability, which makes them more advantageous as potential molecular diagnostic markers
.
In this study, the aim was to analyze the circRNA expression profile of urine-derived extracellular vesicles in high-grade PCa to develop a multi-circRNA-based classifier to detect high-grade PCa during initial biopsy
.
The study evaluated the performance of this urine extracellular vesicle circRNA classifier in a training cohort and externally verified it in two large independent cohorts
.
The study also compared the performance of the test with two standard Care Risk Calculators (RC), the Prostate Cancer Prevention Trial (PCPT)-RC 2.
0 and the European Randomized Study of Prostate Cancer Screening (ERSPC)-RC
.
The study used RNA sequencing to identify candidate circRNAs from the urine extracellular vesicles of 11 high-grade prostate cancer patients and 11 case-matched benign prostatic hyperplasia patients
.
Using ddPCR in the training cohort (n = 263), we constructed a urine extracellular vesicle circRNA classifier (Ccirc, including circPDLIM5, circSCAF8, circPLXDC2, circSCAMP1 and circCCNT2), and evaluated it in two independent cohorts (n = 497, n = 505)
.
Ccirc showed higher accuracy than the two standard care risk calculators (RC) (PCPT-RC 2.
0 and ERSPC-RC) in both the training cohort and the validation cohort
.
In all three cohorts, this new urine extracellular vesicle circRNA classifier plus RC was statistically more predictive than RC alone in predicting ≥ GG2 PCa
.
This test does not require pre-collection of digital rectal examination or special treatment, is reproducible, non-invasive, and can be easily implemented as part of the basic clinical workflow
.
Reference message: https://molecular-cancer.
biomedcentral.
com/articles/10.
1186/s12943-021-01388-6
It is jointly created by the doctoral team of Tsinghua University, Harvard University, Chinese Academy of Sciences and other units.
The iNature Talent Official Account is now launched, focusing on talent recruitment, academic progress, scientific research information, interested parties can Long press or scan the QR code below to follow us
.
iNature prostate cancer (PCa) is the second most common cancer in men
.
Currently, serum prostate-specific antigen (PSA) is the only widely used biomarker for prostate cancer
.
Unfortunately, the low specificity (25-40%) of PSA in the gray area of the so-called PSA level of 2.
0-10.
0 ng/mL has led to a significant increase in the detection of benign unnecessary biopsies and clinically indolent diseases
.
Therefore, there is an urgent need for more precise measures to identify PCa with clinical significance
.
On July 23, 2021, Li Liaoyuan’s team from Sun Yat-sen University published an online publication titled "A urine extracellular vesicle circRNA classifier for detection of high-grade prostate cancer in patients with prostate-specific antigen 2–10" in Molecular Cancer (IF=27.
40).
ng/mL at initial biopsy" research paper, the purpose of this research is to determine a urine extracellular vesicle circular RNA (circRNA) classifier that can detect grade group (GG) 2 or higher Prostate cancer (PCa)
.
To this end, the study used RNA sequencing to identify candidate circRNAs from the urine extracellular vesicles of 11 high-grade prostate cancer patients and 11 case-matched benign prostatic hyperplasia patients
.
Using ddPCR in the training cohort (n = 263), we constructed a urine extracellular vesicle circRNA classifier (Ccirc, including circPDLIM5, circSCAF8, circPLXDC2, circSCAMP1 and circCCNT2), and evaluated it in two independent cohorts (n = 497, n = 505)
.
Ccirc showed higher accuracy than the two standard care risk calculators (RC) (PCPT-RC 2.
0 and ERSPC-RC) in both the training cohort and the validation cohort
.
In all three cohorts, this new urine extracellular vesicle circRNA classifier plus RC was statistically more predictive than RC alone in predicting ≥ GG2 PCa
.
This test does not require pre-collection of digital rectal examination or special treatment, is reproducible, non-invasive, and can be easily implemented as part of the basic clinical workflow
.
Prostate cancer (PCa) is the second most common cancer in men
.
Currently, serum prostate-specific antigen (PSA) is the only widely used biomarker for prostate cancer
.
Unfortunately, the low specificity (25-40%) of PSA in the gray area of the so-called PSA level of 2.
0-10.
0 ng/mL has led to a significant increase in the detection of benign unnecessary biopsies and clinically indolent diseases
.
Therefore, there is an urgent need for more precise measures to identify PCa with clinical significance
.
According to reports, many non-coding RNAs (for example, microRNA, long-chain non-coding RNA, circular RNA [circRNA]) play a key role in cancer progression, showing great potential to influence cancer diagnosis
.
Especially in PCa, 76,311 circRNAs have been identified through RNA sequencing of tumor specimens
.
Interestingly, cancer-specific non-coding RNAs have been identified in extracellular vesicles
.
Compared with linear RNAs, circRNAs have covalently linked ends of a single RNA molecule and exhibit higher stability, which makes them more advantageous as potential molecular diagnostic markers
.
In this study, the aim was to analyze the circRNA expression profile of urine-derived extracellular vesicles in high-grade PCa to develop a multi-circRNA-based classifier to detect high-grade PCa during initial biopsy
.
The study evaluated the performance of this urine extracellular vesicle circRNA classifier in a training cohort and externally verified it in two large independent cohorts
.
The study also compared the performance of the test with two standard Care Risk Calculators (RC), the Prostate Cancer Prevention Trial (PCPT)-RC 2.
0 and the European Randomized Study of Prostate Cancer Screening (ERSPC)-RC
.
The study used RNA sequencing to identify candidate circRNAs from the urine extracellular vesicles of 11 high-grade prostate cancer patients and 11 case-matched benign prostatic hyperplasia patients
.
Using ddPCR in the training cohort (n = 263), we constructed a urine extracellular vesicle circRNA classifier (Ccirc, including circPDLIM5, circSCAF8, circPLXDC2, circSCAMP1 and circCCNT2), and evaluated it in two independent cohorts (n = 497, n = 505)
.
Ccirc showed higher accuracy than the two standard care risk calculators (RC) (PCPT-RC 2.
0 and ERSPC-RC) in both the training cohort and the validation cohort
.
In all three cohorts, this new urine extracellular vesicle circRNA classifier plus RC was statistically more predictive than RC alone in predicting ≥ GG2 PCa
.
This test does not require pre-collection of digital rectal examination or special treatment, is reproducible, non-invasive, and can be easily implemented as part of the basic clinical workflow
.
Reference message: https://molecular-cancer.
biomedcentral.
com/articles/10.
1186/s12943-021-01388-6
