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SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease

  • Authors:
    • Yanchen Liu
    • Yi Zhao
    • Yanting Yao
    • Dan Liu
    • Yilei Sun
    • Weishi Xu
    • Hongli Yu
    • Lijun Chi
  • View Affiliations / Copyright

    Affiliations: Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China, Department of Neurosurgery, Beidahuang Group General Hospital, Harbin, Heilongjiang 150088, P.R. China
    Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 172
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    Published online on: April 17, 2026
       https://doi.org/10.3892/mmr.2026.13882
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Abstract

Given the unclear pathogenesis and insidious progression of Alzheimer's disease (AD), the aim of the present study was to identify reliable diagnostic markers for AD detection using a combination of bioinformatics analysis, animal experiments and clinical patient validation. Gene expression profiles were retrieved from the GSE95587 dataset. Weighted gene co‑expression network analysis combined with four machine learning algorithms identified two signature genes: Serine/Arginine Rich Splicing Factor 1 (SRSF1) and NADH: Ubiquinone oxidoreductase subunit B5 (NDUFB5), and a diagnostic model with moderate efficiency in differentiating AD was established. The AD diagnostic signature genes (SRSF1 and NDUFB5) were associated with specific immune cell infiltration. SRSF1 was significantly enriched in the p38MAPK and AKT1/mTOR signalling pathways. Notably, in an Aβ1‑42‑induced mouse model, SRSF1 expression was upregulated in the hippocampus and cerebral cortex. Moreover, in patients with AD, SRSF1 mRNA levels in peripheral blood mononuclear cells showed a strong negative correlation with mini‑mental state examination and Montreal cognitive assessment scores and a positive correlation with clinical dementia rating scores, indicating a notable association between elevated SRSF1 expression and cognitive decline. Furthermore, SRSF1 levels were positively associated with plasma levels of p‑tau217, p‑tau181 and glial fibrillary acidic protein. These findings underscore the strong association between SRSF1 and AD pathology. The newly identified genes, particularly SRSF1, show potential as candidate biomarkers of AD progression and may provide insights into AD pathogenesis, but require further validation in a larger prospective cohort.
View Figures

Figure 1

Workflow of the present study. LASSO,
Least Absolute Shrinkage and Selection Operator; SVM-RFE, Support
Vector Machine-Recursive Feature Elimination; XGboost, Extreme
Gradient Boosting; ROC, receiver operating characteristic; GSVA,
gene set variation analysis; AD, Alzheimer's disease; ssGSEA,
single-sample gene set enrichment analysis; PBMCs, peripheral blood
mononuclear cells.

Figure 2

Weighted Gene Co-expression Network
Analysis was used to identify AD-associated core modules. (A)
Soft-thresholding analysis for scale-free network topology. (B)
Module-trait correlation heatmap in AD. (C) Gene clustering and
module assignment. (D) Visualization of gene expression and network
metrics. (E) Venn diagram of overlapping genes across network-based
metrics. AD, Alzheimer's disease; MNC, Maximal Clique Centrality;
MCC, Maximal Clique Centrality.

Figure 3

Screening for diagnostic biomarkers
in AD through machine learning algorithms. (A) LASSO regression
identified seven core feature genes associated with AD via 10-fold
cross-validation. (B) SVM-RFE model for feature selection in AD.
The x-axis represents the number of selected features, and the
y-axis shows prediction accuracy and error. (C) The XGBoost
algorithm determined seven feature genes and ranked them by
importance. (D) RF model for AD genes. RF analysis ranked
overlapping candidate genes by relative importance, highlighting
the top 10 significant genes. (E) A Venn diagram showing
intersections and unique genes across different machine-learning
models. AD, Alzheimer's disease. LASSO, Least Absolute Shrinkage
and Selection Operator; SVM-RFE, Support Vector Machine-Recursive
Feature Elimination; RF, Random Forest; XGBoost, Extreme Gradient
Boosting.

Figure 4

Expression patterns of SRSF1 and
NDUFB5 and their predictive performance for AD. Violin plots
showing (A) SRSF1 and (B) NDUFB5 expression levels in control and
AD groups from GSE95587. Violin width indicates data density. ROC
curves evaluating the diagnostic performance of (C) SRSF1 and (D)
NDUFB5 for AD. x-axis, specificity; y-axis, sensitivity.
***P<0.001. SRSF1, Serine/Arginine Rich Splicing Factor; NDUFB5,
NADH: ubiquinone oxidoreductase subunit B5; AD, Alzheimer's
disease; ROC, receiver operating characteristic.

Figure 5

Immune characteristics between the
control and AD groups. (A) Histogram of immune cell infiltration
levels in control and AD groups based on ssGSEA results. Blue
represents control samples and red represents AD samples. (B)
Heatmap of immune infiltration patterns in AD. Colores (light to
dark red) indicate immune cell infiltration levels or related
molecular markers across samples/regions. (C) Correlations between
SRSF1, NDUFB5 and differentially infiltrated immune cells. Solid
lines, positive correlations; dashed lines, negative correlations.
Colours indicate the strength of correlations between cells. Left
label, P-value; R, correlation coefficient. (D) Violin plots of
SRSF1 and NDUFB5 expression across Braak stages (III–VI) in AD. The
shape of the violin plot represents the distribution of gene
expression, with the central box indicating the interquartile range
and the median. (E) gene set variation analysis results exploring
the relationship between SRSF1 and two signalling pathways.
*P<0.05, **P<0.01, ***P<0.001. ns, not significant; AD,
Alzheimer's disease; ssGSEA, single-sample gene set enrichment
analysis; GSVA, gene set variation analysis.

Figure 6

Establishment of
Aβ1-42-induced AD mice model. Aβ1-42 induced
spatial memory impairment in the MWM task 2 weeks after
intracerebroventricular injection. (A) Plots showing the escape
latencies of mice in two groups when finding the hidden platform
over 5 consecutive training days. (B) The number of platform
location crossings during the probe trial, and (C) the percentage
of time spent in the target quadrant of MWM. (D) Representative
swimming path of mice on the test day. Results of (E) H&E, (F)
Nissl staining and (G) quantitative analysis of Nissl staining of
mice in each group. Data are presented as the mean ± SEM.
**P<0.001 vs. sham-treated control group. n=6. AD, Alzheimer's
disease; MWM, Morris water maze; H&E, haematoxylin and eosin;
DG, dentate gyrus; CA1, Cornu Ammonis 1.

Figure 7

Expression of SRSF1 in the AD mouse
model and the sham group. Mice in each group received PBS (sham) or
Aβ1-42 (model) for 14 days. Following the MWM test, mice
were euthanised, and hippocampal and cortical tissues were
collected. (A) Western blot bands and quantitative analysis of
SRSF1 and β-actin expression in (B) the hippocampus and (C) the
cortex; n=3/group. (D) Representative immunofluorescence images and
(E) quantitative analysis of SRSF1. Scale bar, 50 µm. n=3/group;
**P<0.01. AD, Alzheimer's disease; SRSF1, Serine/Arginine Rich
Splicing Factor; MWM, Morris water maze.

Figure 8

SRSF1 mRNA expression in PBMCs of
control and AD groups and correlations with cognitive scores and
plasma biomarkers. (A) SRSF1 expression was upregulated in PBMCs
from patients with AD compared with controls; **P<0.01.
Cognitive state parameters in AD, such as (B) MMSE and (C) MOCA
scores, showed inverse correlations with SRSF1 expression, whereas
(D) CDR scores were positively correlated with it. No significant
correlations were found between SRSF1 expression and (E) plasma
Aβ42/Aβ40 ratios or (F) NfL levels. SRSF1 mRNA levels positively
correlated with plasma (G) GFAP levels, (H) p-tau181 and (I)
p-tau217. AD, Alzheimer's disease; SRSF1, Serine/Arginine Rich
Splicing Factor; PBMC, peripheral blood mononuclear cells; MMSE,
mini-mental state examination; MOCA, Montreal cognitive assessment;
NfL, neurofilament light chain; GFAP, glial fibrillary acidic
protein; CDR, clinical dementia rating.
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Copy and paste a formatted citation
Spandidos Publications style
Liu Y, Zhao Y, Yao Y, Liu D, Sun Y, Xu W, Yu H and Chi L: SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease. Mol Med Rep 33: 172, 2026.
APA
Liu, Y., Zhao, Y., Yao, Y., Liu, D., Sun, Y., Xu, W. ... Chi, L. (2026). SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease. Molecular Medicine Reports, 33, 172. https://doi.org/10.3892/mmr.2026.13882
MLA
Liu, Y., Zhao, Y., Yao, Y., Liu, D., Sun, Y., Xu, W., Yu, H., Chi, L."SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease". Molecular Medicine Reports 33.6 (2026): 172.
Chicago
Liu, Y., Zhao, Y., Yao, Y., Liu, D., Sun, Y., Xu, W., Yu, H., Chi, L."SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease". Molecular Medicine Reports 33, no. 6 (2026): 172. https://doi.org/10.3892/mmr.2026.13882
Copy and paste a formatted citation
x
Spandidos Publications style
Liu Y, Zhao Y, Yao Y, Liu D, Sun Y, Xu W, Yu H and Chi L: SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease. Mol Med Rep 33: 172, 2026.
APA
Liu, Y., Zhao, Y., Yao, Y., Liu, D., Sun, Y., Xu, W. ... Chi, L. (2026). SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease. Molecular Medicine Reports, 33, 172. https://doi.org/10.3892/mmr.2026.13882
MLA
Liu, Y., Zhao, Y., Yao, Y., Liu, D., Sun, Y., Xu, W., Yu, H., Chi, L."SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease". Molecular Medicine Reports 33.6 (2026): 172.
Chicago
Liu, Y., Zhao, Y., Yao, Y., Liu, D., Sun, Y., Xu, W., Yu, H., Chi, L."SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease". Molecular Medicine Reports 33, no. 6 (2026): 172. https://doi.org/10.3892/mmr.2026.13882
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