[Application of serum tumor specific protein 70 for prognostic stratification in acute myeloid leukemia].

<b>Objective:</b> To assess the value of serum tumor specific protein 70 (SP70) for prognostic stratification in acute myeloid leukemia (AML). <b>Methods:</b> A cohort study design was adopted. 129 newly diagnosed AML patients from September 2022 to January 2024 at the Hematology Department of the First Affiliated Hospital of Nanjing Medical University were included, as well as a control group consisted of 120 healthy individuals and 7 cases with benign hematologic diseases during the same period (total 127 cases). Clinical data were collected from Electronic Medical Records. According to the 2023 edition of the Chinese Leukemia Diagnosis and Treatment Guidelines, AML patients with good or moderate prognosis were categorized as low-to-intermediate risk, while those with poor prognosis were high-risk group. Univariate and multivariate logistic regression analyses were used to identify variables significantly associated with AML prognostic risk. ROC analysis was used to evaluate diagnostic performance. A nomogram for predicting patient prognostic risk was constructed by R 4.0.2 software, and the internal validation was performed using bootstrapping. <b>Results:</b> Among 129 AML patients, there were 71 males (55.0%) and 58 females (45.0%), with 42 (32.6%) classified as high-risk and 87 (67.4%) as low-intermediate risk. The high-risk group had a significantly higher median age [62 (48, 67) years] compared to the low-intermediate risk group [50 (35, 63) years, <i>Z=</i>-2.381, <i>P</i>=0.017], and a significantly higher proportion of males (30 patients, 71.4%) compared to the low-intermediate risk group (41 patients, 47.1%, <i>χ<sup>2</sup></i>=6.760, <i>P</i>=0.009). Multivariate logistic regression analysis indicated that serum SP70 (<i>OR</i>=2.54, 95%<i>CI</i>: 1.68-3.84, <i>P</i><0.001), hemoglobin (HB) (<i>OR</i>=0.96, 95%<i>CI</i>: 0.93-0.99, <i>P</i><0.05), and bone marrow blast (BM blast) (<i>OR</i>=1.07, 95%<i>CI</i>: 1.02-1.13, <i>P</i><0.05) were independent risk factors for high-risk prognosis in AML patients. ROC analysis showed that the area under the curve (AUC) for SP70 predicting high-risk patients was 0.908 (cut-off value of 5.74 ng/ml, 95%<i>CI</i>: 0.845-0.952, sensitivity 90.5%, specificity 82.8%). The combined model of serum SP70, HB, and BM blasts had an AUC of 0.931 (95%<i>CI</i>: 0.890-0.973); C-index=0.925 (95%<i>CI</i>: 0.876-0.963),with no statistically significant difference compared to serum SP70 alone (<i>Z=</i>1.693,<i>P</i>>0.05). <b>Conclusion:</b> Serum SP70 may be a promising non-invasive molecular biomarker for prognostic stratification in AML.

Authors

Institutions

Publication Details

Journal
PubMed
Published
2024-10-06
DOI
https://doi.org/10.3760/cma.j.cn112150-20240506-00363
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

[Application of serum tumor specific protein 70 for prognostic stratification in acute myeloid leukemia].

Shiyang Pan, L X Zhang, Ying Mu, Shi Yang et al.
PubMed
Acute Myeloid Leukemia Research
article

[Application of serum tumor specific protein 70 for prognostic stratification in acute myeloid leukemia].

Shiyang Pan, L X Zhang, Ying Mu, Shi Yang, Qian Zhu, Jin Xu, Wenjie Xia, Feng Jin, Fengxiang Lu, Yu-Ting Huang
article en

Abstract

<b>Objective:</b> To assess the value of serum tumor specific protein 70 (SP70) for prognostic stratification in acute myeloid leukemia (AML). <b>Methods:</b> A cohort study design was adopted. 129 newly diagnosed AML patients from September 2022 to January 2024 at the Hematology Department of the First Affiliated Hospital of Nanjing Medical University were included, as well as a control group consisted of 120 healthy individuals and 7 cases with benign hematologic diseases during the same period (total 127 cases). Clinical data were collected from Electronic Medical Records. According to the 2023 edition of the Chinese Leukemia Diagnosis and Treatment Guidelines, AML patients with good or moderate prognosis were categorized as low-to-intermediate risk, while those with poor prognosis were high-risk group. Univariate and multivariate logistic regression analyses were used to identify variables significantly associated with AML prognostic risk. ROC analysis was used to evaluate diagnostic performance. A nomogram for predicting patient prognostic risk was constructed by R 4.0.2 software, and the internal validation was performed using bootstrapping. <b>Results:</b> Among 129 AML patients, there were 71 males (55.0%) and 58 females (45.0%), with 42 (32.6%) classified as high-risk and 87 (67.4%) as low-intermediate risk. The high-risk group had a significantly higher median age [62 (48, 67) years] compared to the low-intermediate risk group [50 (35, 63) years, <i>Z=</i>-2.381, <i>P</i>=0.017], and a significantly higher proportion of males (30 patients, 71.4%) compared to the low-intermediate risk group (41 patients, 47.1%, <i>χ<sup>2</sup></i>=6.760, <i>P</i>=0.009). Multivariate logistic regression analysis indicated that serum SP70 (<i>OR</i>=2.54, 95%<i>CI</i>: 1.68-3.84, <i>P</i><0.001), hemoglobin (HB) (<i>OR</i>=0.96, 95%<i>CI</i>: 0.93-0.99, <i>P</i><0.05), and bone marrow blast (BM blast) (<i>OR</i>=1.07, 95%<i>CI</i>: 1.02-1.13, <i>P</i><0.05) were independent risk factors for high-risk prognosis in AML patients. ROC analysis showed that the area under the curve (AUC) for SP70 predicting high-risk patients was 0.908 (cut-off value of 5.74 ng/ml, 95%<i>CI</i>: 0.845-0.952, sensitivity 90.5%, specificity 82.8%). The combined model of serum SP70, HB, and BM blasts had an AUC of 0.931 (95%<i>CI</i>: 0.890-0.973); C-index=0.925 (95%<i>CI</i>: 0.876-0.963),with no statistically significant difference compared to serum SP70 alone (<i>Z=</i>1.693,<i>P</i>>0.05). <b>Conclusion:</b> Serum SP70 may be a promising non-invasive molecular biomarker for prognostic stratification in AML.

PubMedVol. 58(10)
Nanjing Medical University (CN)
Acute Myeloid Leukemia Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.