| Biomarker | B2M | Full name | Beta-2 microglobulin | Molecular Type | Protein |
| Molecular Formula | / | Molecular Weight | / | Core Functional Dimensions | Renal function assessment、Early warning of complications |
| Disease(or Constitution) | Chronic Kidney Disease (CKD) | Disease Subtypes | / | ||
| Organ | Kidney | Test Sample / Technology Platform | Blood, urine (protein detection) | ||
| Pathobiological Mechanisms | Mainly through glomerular filtration, elevated levels reflect impaired glomerular filtration function. | ||||
| Clinical Availability Status | Research verification stage | ||||
| Notes and Dynamics | It is related to the risk of end-stage renal disease ( ESRD ) and cardiovascular disease in CKD patients, and can improve the accuracy of risk prediction in patients with moderate CKD. | ||||
| Biomarker | BTP | Full name | Beta-trace protein | Molecular Type | Protein |
| Molecular Formula | / | Molecular Weight | / | Core Functional Dimensions | Renal function assessment、Early warning of complications |
| Disease(or Constitution) | Chronic Kidney Disease (CKD) | Disease Subtypes | / | ||
| Organ | Kidney | Test Sample / Technology Platform | Blood, urine (protein detection) | ||
| Pathobiological Mechanisms | It is involved in renal metabolism, and abnormal levels are associated with reduced GFR. | ||||
| Clinical Availability Status | Research verification stage | ||||
| Notes and Dynamics | It can supplement the deficiency of serum creatinine in GFR estimation and provide additional renal function risk information for patients with moderate CKD. | ||||
| Biomarker | miR-181a | Full name | MicroRNA-181a | Molecular Type | Nucleic acid（non-coding RNA） |
| Molecular Formula | / | Molecular Weight | / | Core Functional Dimensions | Early prediction、Condition monitoring |
| Disease(or Constitution) | Chronic Kidney Disease (CKD) | Disease Subtypes | / | ||
| Organ | Kidney | Test Sample / Technology Platform | Urine, plasma (RNA sequencing, quantitative PCR) | ||
| Pathobiological Mechanisms | Regulating renal parenchymal injury-related genes, the level of urinary exosomes in CKD patients was significantly reduced. | ||||
| Clinical Availability Status | Research verification stage | ||||
| Notes and Dynamics | The level of miR-181a in urinary exosomes of CKD patients is about 200 times lower than that of healthy people, which is a highly specific marker of renal parenchymal injury. | ||||
| Biomarker | miR-451 | Full name | MicroRNA-451 | Molecular Type | Nucleic acid（non-coding RNA） |
| Molecular Formula | / | Molecular Weight | / | Core Functional Dimensions | Early prediction、Condition monitoring |
| Disease(or Constitution) | Chronic Kidney Disease (CKD) | Disease Subtypes | / | ||
| Organ | Kidney | Test Sample / Technology Platform | Urine, plasma (RNA sequencing, quantitative PCR) | ||
| Pathobiological Mechanisms | Post-transcriptional regulation of kidney injury-related genes, abnormal plasma / urine levels in CKD | ||||
| Clinical Availability Status | Research verification stage | ||||
| Notes and Dynamics | The sensitivity of plasma and urine miR-451 in the diagnosis of diabetic nephropathy was 90.9 % and 95.5 %, respectively. | ||||
| Biomarker | EASIX | Full name | Endothelial Activation and Stress Index (calculated as sVCAM-1×creatinine/platelet count) | Molecular Type | Composite index |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Prognosis, risk stratification | ||
| Disease(or Constitution) | Anemia | Disease Subtypes | Anemia | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Based on clinical detection parameters (such as creatinine, bilirubin, etc.) for calculation | ||
| Pathobiological Mechanisms | It is associated with endothelial dysfunction in patients with anemia and predicts all-cause mortality. | ||||
| Clinical Availability Status | National cohort validation: 2954 patients were followed up for 81 months. | ||||
| Notes and Dynamics | Critical value: 0.72. The hazard ratio for all-cause mortality in the high-score group was 1.42, and the 10-year predicted area under the curve was 0.78. | ||||
| Biomarker | Folic acid | Full name | Folic acid | Molecular Type | Vitamin |
| Molecular Formula | C19H19N7O6 | Molecular Weight | 441.397 | Core Functional Dimensions | Diagnosis, etiological differentiation |
| Disease(or Constitution) | Anemia | Disease Subtypes | MA | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Serum/Red blood cells (chemiluminescence method, microbial method) | ||
| Pathobiological Mechanisms | Involved in DNA synthesis, deficiency leads to erythropoiesis disorders, megaloblastic changes. | ||||
| Clinical Availability Status | Clinical routine application: Core indicators for classification of megaloblastic anemia. | ||||
| Notes and Dynamics | Red blood cell folate can better reflect long-term folate reserves and is superior to serum folate testing. | ||||
| Biomarker | Vitamin B12 | Full name | Cobalamin | Molecular Type | Vitamin |
| Molecular Formula | C63H88CoN14O14P | Molecular Weight | 1355.365 | Core Functional Dimensions | Diagnosis, etiological differentiation |
| Disease(or Constitution) | Anemia | Disease Subtypes | Megaloblastic anemia (MA) | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Serum (chemiluminescence method, radioimmunoassay) | ||
| Pathobiological Mechanisms | Involved in DNA synthesis, deficiency leads to megaloblastic change of bone marrow hematopoietic cells, red blood cell maturation disorders. | ||||
| Clinical Availability Status | Clinical routine application: Key indicators for diagnosing megaloblastic anemia. | ||||
| Notes and Dynamics | It can be combined with the levels of homocysteine and methylmalonic acid to improve the diagnostic accuracy. | ||||
| Biomarker | Ferritin | Full name | Ferritin | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Diagnosis, assessment of iron reserves | ||
| Disease(or Constitution) | Anemia | Disease Subtypes | IDA | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Serum (immunoturbidimetry, ELISA) | ||
| Pathobiological Mechanisms | It reflects the amount of iron storage in the body. When iron deficiency occurs, it decreases early, and when inflammation occurs, it increases nonspecifically. | ||||
| Clinical Availability Status | Clinical routine application: Core indicators for diagnosing IDA. | ||||
| Notes and Dynamics | It is necessary to make a comprehensive judgment based on inflammatory markers (such as CRP) to avoid false negatives caused by inflammation. | ||||
| Biomarker | CXCL-12 | Full name | C-X-C Motif Chemokine Ligand 12 | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Prognosis, monitoring | ||
| Disease(or Constitution) | Acute Myeloid Leukemia (AML) | Disease Subtypes | / | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Bone marrow plasma (ELISA) | ||
| Pathobiological Mechanisms | Down-regulated during diagnosis, up-regulated after complete remission and relapse, reflecting changes in the bone marrow microenvironment. | ||||
| Clinical Availability Status | Research validation: Inverse correlation with Activin-A expression. | ||||
| Notes and Dynamics | As an auxiliary biomarker for disease progression monitoring. | ||||
| Biomarker | Activin-A | Full name | Activin A (Homodimer of Inhibin Beta A Subunits) | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Prognosis, prediction of treatment response | ||
| Disease(or Constitution) | Acute Myeloid Leukemia (AML) | Disease Subtypes | / | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Bone marrow plasma (ELISA) | ||
| Pathobiological Mechanisms | Highly expressed during diagnosis, downregulated after complete remission, and elevated again upon recurrence. | ||||
| Clinical Availability Status | Research validation: 80 AML patient samples confirm its value. | ||||
| Notes and Dynamics | Predictable induction response and relapse risk, evaluation of patient outcomes. | ||||
| Biomarker | WT1 | Full name | Wilms Tumor 1 | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Diagnosis, prognosis, and treatment response monitoring | ||
| Disease(or Constitution) | Acute Myeloid Leukemia (AML) | Disease Subtypes | / | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Bone marrow/blood (RNA sequencing, qPCR) | ||
| Pathobiological Mechanisms | Overexpression is associated with elevated white blood cell count and increased proportion of primitive cells, with expression levels decreasing after induction therapy. | ||||
| Clinical Availability Status | Clinical validation: 345 diagnostic samples confirm its value. | ||||
| Notes and Dynamics | Can monitor disease status and guide risk stratification and treatment decisions. | ||||
| Biomarker | LRP12 | Full name | Low Density Lipoprotein Receptor-Related Protein 12 | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Diagnosis | ||
| Disease(or Constitution) | Acute Myeloid Leukemia (AML) | Disease Subtypes | / | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Bone marrow/AML cell line (RNA sequencing, in vitro validation). | ||
| Pathobiological Mechanisms | Co-overexpression with CD109, associated with immune checkpoint molecule dysregulation, promotes cancer cell immune evasion. | ||||
| Clinical Availability Status | Research Verification: Secretome analysis confirms its expression profile. | ||||
| Notes and Dynamics | First reported as a biomarker related to AML diagnosis. | ||||
| Biomarker | CD109 | Full name | CD109 Antigen | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Diagnosis | ||
| Disease(or Constitution) | Acute Myeloid Leukemia (AML) | Disease Subtypes | / | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Bone marrow/AML cell line (RNA sequencing, in vitro validation). | ||
| Pathobiological Mechanisms | Overexpression in AML cell lines, involved in T-cell receptor pathway dysregulation, and reduced immune response. | ||||
| Clinical Availability Status | Research Verification: Identification of potential biomarkers through transcriptome analysis. | ||||
| Notes and Dynamics | As a candidate molecule for AML diagnosis, it needs further clinical validation when used in conjunction with LRP12. | ||||
| Biomarker | STK25 | Full name | Serine/Threonine Kinase 25 | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Prognosis, risk stratification | ||
| Disease(or Constitution) | Acute Myeloid Leukemia (AML) | Disease Subtypes | / | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Bone marrow (RNA sequencing, siRNA validation) | ||
| Pathobiological Mechanisms | High expression promotes AML cell survival, while knockdown inhibits proliferation. | ||||
| Clinical Availability Status | Research Verification: Incorporating a novel risk stratification system. | ||||
| Notes and Dynamics | Provide new molecular basis for precise prognosis assessment of AML. | ||||
| Biomarker | SOD1 | Full name | Superoxide Dismutase 1 | Molecular Type | Protein |
| Molecular Formula | Molecular Weight | Core Functional Dimensions | Prognosis, risk stratification | ||
| Disease(or Constitution) | Acute Myeloid Leukemia (AML) | Disease Subtypes | / | ||
| Organ | Blood, bone marrow | Test Sample / Technology Platform | Bone marrow (RNA sequencing, siRNA validation) | ||
| Pathobiological Mechanisms | High expression is involved in the proliferation regulation of AML cells and associated with disease progression. | ||||
| Clinical Availability Status | Research validation: Enhanced risk stratification accuracy when combined with the other three factors. | ||||
| Notes and Dynamics | Targeted inhibition can serve as one of the combination therapy strategies. | ||||
| Biomarker | Gut microbiota | Full name | Gut microbiota | Molecular Type | Microorganism |
| Molecular Formula | / | Molecular Weight | / | Core Functional Dimensions | Early diagnosis、Risk assessment |
| Disease(or Constitution) | Chronic Kidney Disease (CKD) | Disease Subtypes | / | ||
| Organ | Kidney | Test Sample / Technology Platform | Feces, blood ( metagenomic sequencing, flora analysis ) | ||
| Pathobiological Mechanisms | The progress of CKD leads to the imbalance of intestinal flora, the decrease of probiotics and the increase of pathogenic bacteria, which leads to the damage of intestinal | ||||
| Clinical Availability Status | Research and exploration stage | ||||
| Notes and Dynamics | The metabolites of intestinal flora ( such as indole and phenols ) can be used as uremic toxins to aggravate kidney damage, and related markers are expected to become non-invasive screening tools. | ||||