Invest Clin 66(4): 390- 407, 2025 https://doi.org/10.54817/IC.v66n4a04

Corresponding author: Xinwei Su. Department of Obstetrics and Gynecology, The Fifth Affiliated Hospital of Sun

Yat-sen University, 52 Meihua East Road, Zhuhai, Guangdong 519000, China. Tel: +86 0756 2528453.

Email: XXinnwei_ss@outlook.com

Comprehensive analysis of autophagy-

and glycolysis-related differentially

expressed genes involved in chronic

inflammation in obese patients.

Simin Yang1, Rexidanmu Hudabai1 and Xinwei Su2

1Department of Anesthesiology, The Fifth Affiliated Hospital of Sun Yat-sen University,

Zhuhai, Guangdong, China.

2Department of Obstetrics and Gynecology, The Fifth Affiliated Hospital of Sun Yat-sen

University, Zhuhai, Guangdong, China.

Keywords: Obesity; Autophagy; Glycolysis; Inflammation.

Abstract. The interaction between glycolysis and autophagy contributes

to reprogramming chronic inflammation in obesity, but the knowledge about

this interaction remains limited. Publicly available data were used to analyze

autophagy- and glycolysis-related differentially expressed genes (A&GRDEGs)

in two datasets comparing patients with obesity and normal-weight patients.

A total of 5 A&GRDEGs were obtained through screening, namely recombi-

nant eukaryotic translation initiation factor 4E binding protein 1 (EIF4EBP1),

transforming growth factor beta 1 (TGFB1), fatty acid synthase (FASN), alpha-

synuclein (SNCA), and C-X-C chemokine receptor 4 (CXCR4). Levels of autoph-

agy and glycolysis exhibit substantial predictive value for obesity development

and mechanistically contribute to disease pathogenesis through immunometa-

bolic dysregulation.

Multi-Omics map of obesity inflammation 391

Vol. 66(4): 390 - 407, 2025

Análisis exhaustivo de los genes expresados diferencialmente,

relacionados con la autofagia y la glucólisis, que intervienen

en la inflamación crónica en pacientes obesos.

Invest Clin 2025; 66 (4): 390 – 407

Palabras clave: Obesidad; Autofagia; Glucólisis; Inflamación.

Resumen. La interacción entre la glucólisis y la autofagia contribuye a la

reprogramación de la inflamación crónica en la obesidad, pero el conocimiento

sobre esta interacción es limitado. Se utilizaron datos públicamente disponi-

bles para analizar los genes expresados diferencialmente relacionados con la

autofagia y la glucólisis (A&GRDEG) entre pacientes con obesidad y con peso

normal en dos conjuntos de datos. Se obtuvo un total de 5 A&GRDEG mediante

cribado, a saber: la proteína recombinante de unión al factor de iniciación de la

traducción eucariótica 4E 1 (EIF4EBP1), el factor de crecimiento transforman-

te beta 1 (TGFB1), la sintasa de ácidos grasos (FASN), la alfa-sinucleína (SNCA)

y el receptor de quimiocina C-X-C 4 (CXCR4). Los niveles de autofagia y de glu-

cólisis mostraron un valor predictivo elevado para el desarrollo de la obesidad

y contribuyen mecánicamente a la patogénesis de la enfermedad mediante la

desregulación inmunometabólica.

Received: 03-08-2025 Accepted: 21-09-2025

INTRODUCTION

According to projections, the global

population of people living with obesity, a

metabolic syndrome, will reach one billion

by 2030, affecting one in five women and one

in seven men 1 Obesity is characterized by

excess weight (body mass index, BMI ≥30

kg/m2), accompanied by chronic low-grade

inflammation, oxidative stress, insulin resis-

tance, hypertension, dyslipidemia, and other

abnormalities. It could also increase the sus-

ceptibility of individuals to chronic diseases,

such as type 2 diabetes mellitus (T2DM),

non-alcoholic fatty liver disease (NAFLD),

and specific malignancies 2. Furthermore,

adipocytes serve as central metabolic and

inflammatory regulators through their en-

docrine capacity, secreting both pro- and an-

ti-inflammatory mediators, including leptin

(pro-inflammatory) and adiponectin (anti-

inflammatory), which systemically influence

energy homeostasis and immune responses1.

Leptin and adiponectin exert pro-inflam-

matory and anti-inflammatory functions,

respectively, and mutually regulate carcino-

genesis 2.

Adequate lipid storage prevents ectopic

lipid accumulation in non-specific organs

(e.g. muscle, liver, and heart) and toxic lipid

accumulation (e.g., lipotoxicity) and is also

associated with stable metabolic function 3.

Lipid droplet accumulation in the liver and

other organs has been linked with obesity-

associated autophagic dysfunction 4. The

seminal recognition of autophagy through

the 2016 Nobel Prize in Physiology or Medi-

cine underscores its pivotal role as a funda-

mental cellular clearance mechanism for

superfluous or deleterious constituents5.

Chronic inflammation in individuals with

obesity may suppress autophagy. Moreover,

392 Yang et al.

Investigación Clínica 66(4): 2025

obesity-associated autophagic dysfunction

may cause protein and organelle degrada-

tion, cellular dysfunction, and cell death 6.

Autophagy is presumed to be inactive during

obesity owing to the chronic upregulation of

mammalian target of rapamycin complex 1

(mTORC1) 7. Obesity could contribute to an

elevated risk of cancer through autophagic

impairment; thus, treatment methods in-

volving the enhancement of autophagy may

serve as a practical approach against obesi-

ty-associated cancers 8.

Metabolism is widely known as a core

process underpinning all biological phenom-

ena, supplying energy and building blocks

for macromolecules 9. Restricting glycolysis

has been shown to impede cytokine produc-

tion but not cellular proliferation, suggest-

ing that glycolysis plays a critical role in

regulating inflammation 10. However, infor-

mation on the exact mechanism by which

the interaction between glycolysis and au-

tophagy contributes to reprogramming the

progression of chronic inflammation in obe-

sity remains limited.

Individuals with obesity exhibit adipo-

cyte hypoxia, which could lead to the elevat-

ed expression of hypoxia-inducible factors,

activation of adipocytes, production and re-

lease of free fatty acids and pro-inflammato-

ry mediators, induction of circulating mono-

cyte recruitment, and aggregation of adipose

tissue macrophages 11. Studies have revealed

that individuals with obesity have a higher

macrophage count in white adipose tissues

than individuals with normal BMI, showing

increases ranging from 10% to >50% of the

total cell count 12. Metabolically activated

macrophages show a higher rate of glycoly-

sis and produce more lactic acid in the fatty

tissues of individuals with obesity than in the

tissues of normal-weight individuals 13. Alter-

ations in immune cell infiltration dynamics

and their secretion of pro-inflammatory cy-

tokines critically contribute to the sustained

low-grade inflammation observed in obesity.

Consequently, targeting immune cell re-

cruitment and activation has emerged as a

key therapeutic strategy for obesity-associat-

ed chronic inflammation 14.

In this study, screening was first per-

formed to identify genes frequently involved

in autophagy and glycolysis. Next, we lever-

aged publicly available datasets to compare

the expression profiles of autophagy- and gly-

colysis-related genes between obese and nor-

mal-weight individuals. Additionally, we as-

sessed immune cell infiltration patterns and

examined their correlations with the differen-

tially expressed genes. These findings could

help elucidate the key mechanisms underly-

ing the pathogenesis of chronic inflammation

in patients with obesity and provide potential

targets for prevention and treatment.

MATERIALS AND METHODS

Dataset download and processing

We obtained two obesity-related gene

expression datasets (GSE134913 15 and

GSE59034 16) from the Gene Expression

Omnibus (GEO) database (https://www.

ncbi.nlm.nih.gov/geo/). The GSE134913

dataset included samples. We included 14

pre-surgery samples from obese patients

(Obese group) who underwent metabolic

surgery and six controls (Control group),

forming a subset of 20 samples.

The GSE134913 dataset is a subset of

participants from the GSE135066 cohort.

It comes from the clinical study “Dynamic

Changes in Muscle Insulin Sensitivity after

Metabolic Surgery” by Gancheva et al. 15.

The original cohort included six control sub-

jects and 16 obese individuals who had met-

abolic surgery, with longitudinal gene ex-

pression profiling done at three timepoints:

before surgery, two weeks after surgery, and

52 weeks after surgery. For this analysis, we

included all six control subjects and 14 of

the 16 obese participants who had surgery,

based on data completeness criteria.

Although aggregate baseline character-

istics of the entire cohort are provided in the

original publication, detailed demographic

and clinical metadata for the specific genet-

Multi-Omics map of obesity inflammation 393

Vol. 66(4): 390 - 407, 2025

ic sequencing subset were unavailable. As a

result, participant-level clinical information

could not be included in our analyses.

The GSE59034 dataset contained 48

samples. We selected 16 pre-surgery obese

samples and 16 controls, yielding a total of

32 samples. This dataset served as the pri-

mary cohort for all subsequent analyses. De-

tailed information about the datasets is pro-

vided in Table S1.

Autophagy-related genes (ARGs) and

glycolysis-related genes (GRGs) were sys-

tematically identified through comprehen-

sive searches of the GeneCards database

(https://www.genecards.org), followed by

literature validation using PubMed (https://

pubmed.ncbi.nlm.nih.gov/) with “Autopha-

gy” 17-19 and “Glycolysis” 20-22 as the primary

search terms, respectively. The intersection

of ARGs and GRGs defined the autophagy-

and glycolysis-related genes (A&GRGs) used

in this study (Fig. 1).

Differentially expressed genes analysis

Based on the original study designs,

samples were stratified into control and

obese groups. Differentially expressed genes

(DEGs) were identified using the limma

package (v3.58.1) in R (v4.2.2), with signifi-

cance defined as |logFC| > 0.5 and p<0.05.

Results were visualized through volcano

plots (ggplot2, v3.4.4).

To obtain the autophagy- and glycol-

ysis-related differentially expressed genes

(A&GRDEGs), we first intersected all signifi-

cant DEGs with our curated list of A&GRGs,

and a Venn diagram was plotted to provide

the A&GRDEGs. Expression patterns of

these A&GRDEGs were subsequently dis-

played as clustered heatmaps (pheatmap

package, v1.0.12) in R, using z-score nor-

malized expression values.

Fig. 1. Technology roadmap.

ARGs, Autophagy Related Ge-

nes. GRGs, Glycolysis Related

Genes. A&GRGs, Autophagy

& Glycolysis Related Genes.

A&GRDEGs, Autophagy &

Glycolysis Related Differen-

tially Expressed Genes. DEGs,

Differentially expressed genes.

GSEA, Gene Set Enrichment

Analysis. GO, Gene Ontology.

KEGG, Kyoto Encyclopedia of

Genes and Genomes. ssGSEA,

single-sample gene-set enrich-

ment Analysis. ROC, receiver

operating characteristic cur-

ve. PPI, Protein-protein inte-

raction network. TF, Transcrip-

tion factors.

394 Yang et al.

Investigación Clínica 66(4): 2025

Gene ontology and Kyoto Encyclopedia of

Genes and Genomes pathway enrichment

analyses

Functional enrichment analysis was

performed using the clusterProfiler pack-

age (v4.10.0) in R. Gene Ontology (GO) and

Kyoto Encyclopedia of Genes and Genomes

(KEGG) pathway analyses were conducted

on the differentially expressed genes. Terms

with p<0.05 and false discovery rate (FDR;

q) <0.25 were considered statistically sig-

nificant.

Gene Set Enrichment Analysis

Gene Set Enrichment Analysis (GSEA)

was conducted on the GSE59034 dataset us-

ing the clusterProfiler package. Genes were

ranked by log₂ fold-change and analyzed

against the ‘c2.all.v2022.1.Hs.symbols.gmt’

gene set (All Canonical Pathways, n=3,050)

from MSigDB, with Homo sapiens as the ref-

erence species. Significance thresholds were

set at p<0.05 and q <0.25.

Differential expression and receiver

operating characteristic analyses

of A&GRDEGs

The expression patterns of A&GRDEGs

were compared between the obese and con-

trol groups across both datasets. The diag-

nostic potential was evaluated using receiver

operating characteristic (ROC) analysis per-

formed with the pROC package (v1.18.5).

Area under the curve (AUC) values were de-

termined to measure the predictive ability.

Analysis of immune infiltration

Immune cell infiltration levels were

quantified using single-sample Gene Set En-

richment Analysis (ssGSEA) implemented in

the GSVA package (v1.50.0). Correlations

among differentially abundant immune cell

populations (obese vs. control), and between

immune cells and A&GRDEG expression,

were analyzed in the GSE59034 dataset. Re-

sults were visualized as correlation dot plots

(ggplot2).

Statistical analysis

All statistical analyses were conducted

using R software (R Foundation for Statis-

tical Computing, Vienna, Austria). Con-

tinuous variables were compared with Stu-

dent’s t-test (for normally distributed data)

or Wilcoxon rank-sum test (for non-normal

data). Multi-group comparisons employed

the Kruskal-Wallis test. Categorical variables

were analyzed with χ² tests or Fisher’s exact

tests, depending on the situation. Correla-

tions were assessed with Spearman’s rank

correlation. A two-tailed α level of 0.05 was

deemed statistically significant unless other-

wise noted.

RESULTS

Identification of A&GRDEGs

Differential expression analysis re-

vealed 628 significant DEGs (|logFC| >0.5

and p<0.05) in the GSE134913 dataset,

comprising 316 upregulated and 312 down-

regulated genes (Fig. 2A). The GSE59034

dataset showed more pronounced differen-

tial expression with 904 DEGs under the

same thresholds (653 upregulated, 251

downregulated; Fig. 2B). Volcano plots vi-

sually represent these expression patterns,

highlighting the asymmetric distribution

of upregulated versus downregulated genes

between datasets.

To identify the A&GRDEGs, we inter-

sected the significant DEGs (|logFC| > 0.5

and p<0.05) from both datasets with our

curated A&GRG list, revealing five overlap-

ping genes (Fig. 2C): eukaryotic transla-

tion initiation factor 4E binding protein 1

(EIF4EBP1), transforming growth factor

beta 1 (TGFB1), fatty acid synthase (FASN),

alpha-synuclein (SNCA), and C-X-C chemo-

kine receptor type 4 (CXCR4) (Table 1).

We subsequently analyzed and visualized

the differential expression patterns of these

A&GRDEGs between sample groups in the

GSE134913 dataset (Fig. 2D) and in the

GSE59034 dataset (Fig. 2E).

Multi-Omics map of obesity inflammation 395

Vol. 66(4): 390 - 407, 2025

Construction and analysis

of the prediction model

We systematically investigated the func-

tional associations of the five A&GRDEGs

with obesity through comprehensive GO and

KEGG enrichment analyses (Table 2). GO

analysis identified significant enrichment in

biological processes (BP) such as positive reg-

ulation of glial cell differentiation, receptor

metabolic process, and gliogenesis; cellular

Fig. 2. Differentially expressed genes (DEGs) analysis.

(A) Volcano plot of differential analysis results between the obese and control groups in the GSE134913

dataset. (B) Volcano plot of differential analysis results between the obese and control groups in the

GSE59034 dataset. (C) Venn diagram of DEGs in the GSE134913 and GSE59034 datasets and A&GRGs.

(D) Differential expression heatmap of DEGs in the GSE134913 dataset. (E) Differential expression

heatmap of DEGs in the GSE59034 dataset. Green indicates the control group, and orange indicates

the obese group. In the heatmaps, blue represents low expression, and red represents high expression.

Table 1. A&GRDEGs in GSE59034 and GSE134913.

Gene logFC AveExpr t p p adjust B group

GSE59034

EIF4EBP -0.75697 6.168652 -7.58047 8.52E-09 3.51E-06 10.16121 down

TGFB1 0.571166 6.797319 5.933876 1.06E-06 4.33E-05 5.525466 up

FASN -0.64127 10.46345 -4.02795 0.0003 0.002172 0.123576 down

SNCA 0.596172 7.741707 2.504538 0.017237 0.049432 -3.63114 up

CXCR4 0.512738 6.194027 2.197896 0.034888 0.085782 -4.25343 up

GSE134913

EIF4EBP -0.79216 9.560483 -4.21636 0.00039 0.901994 -3.5489 down

SNCA 1.325622 9.526431 2.616228 0.016162 0.999958 -4.10701 up

CXCR4 0.785232 7.648631 2.278605 0.033299 0.999958 -4.22823 up

TGFB1 1.137448 6.567356 2.192964 0.039755 0.999958 -4.25833 up

FASN -0.57511 7.727896 -2.11711 0.046404 0.999958 -4.28468 down

396 Yang et al.

Investigación Clínica 66(4): 2025

Table 2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)

enrichment analysis results.

Ontology ID Description Gene

Ratio

p p adjust q geneID Count

BP GO:0032103 positive regulation

of response to

external stimulus

3/5 442/

18800

0.00012461 0.02430796 0.00669754 TGFB1/

SNCA/

CXCR4

BP GO:0045687 positive regulation

of glial cell

differentiation

2/5 42/

18800

4.8517E-05 0.02430796 0.00669754 TGFB1/

CXCR4

BP GO:0043112 receptor metabolic

process

2/5 61/

18800

0.00010291 0.02430796 0.00669754 TGFB1/

SNCA

BP GO:0014015 positive regulation

of gliogenesis

2/5 64/

18800

0.00011333 0.02430796 0.00669754 TGFB1/

CXCR4

BP GO:2000379 positive regulation

of reactive oxygen

species metabolic

process

2/5 71/

18800

0.0001396 0.02430796 0.00669754 TGFB1/

SNCA

CC GO:0031091 platelet alpha

granule

2/5 91/

19594

0.0002114 0.00718767 0.00400552 TGFB1/

SNCA

CC GO:0031092 platelet alpha

granule membrane

1/5 17/

19594

0.00433098 0.07362671 0.04103037 SNCA 1

CC GO:0031093 platelet alpha

granule lumen

1/5 67/

19594

0.01698227 0.08811426 0.04910392 TGFB1 1

CC GO:0016234 inclusion body 1/5 74/

19594

0.01874314 0.08811426 0.04910392 SNCA 1

CC GO:0005902 microvillus 1/5 90/

19594

0.0227585 0.08811426 0.04910392 TGFB1 1

MF GO:0003779 actin binding 2/5 439/

18410

0.00540893 0.04141684 0.01278836 SNCA/

CXCR4

MF GO:0008190 eukaryotic

initiation factor 4E

binding

1/5 10/

18410

0.00271326 0.04141684 0.01278836 EIF4EBP1 1

MF GO:0034713 Type I transforming

growth factor beta

receptor binding

1/5 10/

18410

0.00271326 0.04141684 0.01278836 TGFB1 1

MF GO:0004312 fatty acid synthase

activity

1/5 13/

18410

0.00352609 0.04141684 0.01278836 FASN 1

MF GO:0043027 cysteine-type

endopeptidase

inhibitor activity

involved in the

apoptotic process

1/5 22/

18410

0.0059614 0.04141684 0.01278836 SNCA 1

KEGG hsa04672 Intestinal immune

network for IgA

production

2/5 49/

8164

0.00034888 0.02163052 0.01652586 TGFB1/

CXCR4

KEGG hsa04152 AMPK signaling

pathway

2/5 121/

8164

0.00211594 0.05414617 0.04136804 FASN/

EIF4EBP1

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Vol. 66(4): 390 - 407, 2025

components (CC) such as platelet alpha gran-

ule and its subcompartments (membrane, lu-

men); molecular functions (MF) such as fatty

acid synthase activity, TGF-β receptor type I

binding, and eukaryotic initiation factor 4E

binding. KEGG pathway analysis revealed in-

volvement in the intestinal immune network

for IgA production, the insulin signaling path-

way, and the AMP-activated protein kinase

(AMPK) signaling pathway. Results were vi-

sualized as bar charts (Fig. 3A) and bubble

plots (Fig. 3B), and functional networks were

constructed for BP (Fig. 3C), MF (Fig. 3D),

CC (Fig. 3E), and KEGG pathways (Fig. 3F).

Results of Gene Set Enrichment Analysis

(GSEA)

GSEA of the GSE59034 dataset re-

vealed significant associations between

global gene expression patterns and key bio-

logical processes (Fig. 4A), including inter-

leukin-10 signaling (Fig. 4B), neutrophil de-

granulation (Fig. 4C), Leishmania infection

responses (Fig. 4D), and proinflammatory/

profibrotic mediator networks (Fig. 4E).

These findings, detailed in Table S2, demon-

strate broad enrichment in immune-meta-

bolic pathways, highlighting their potential

role in obesity-related pathophysiology.

Differential expression and ROC analyses

of A&GRDEGs

Wilcoxon rank-sum tests revealed sig-

nificant differential expression (p<0.05)

of three A&GRDEGs (EIF4EBP1, TGFB1,

SNCA) between obese and control groups

in the GSE134913 dataset (Fig. 5A). ROC

analysis demonstrated strong diagnostic

potential for EIF4EBP1 (AUC = 0.921, Fig.

5D) and moderate predictive accuracy for

FASN (AUC = 0.750), SNCA (AUC = 0.833),

TGFB1 (AUC = 0.798), and CXCR4 (AUC =

0.762) (Fig. 5C-E), with AUC values indicat-

ing their utility as potential biomarkers for

obesity classification.

Consistent analysis of the GSE59034

dataset revealed significant differential ex-

pression (p<0.05) for all five A&GRDEGs

between obese and control groups (Fig. 5B).

ROC analysis demonstrated excellent diag-

nostic performance for EIF4EBP1 (AUC =

0.980) and TGFB1 (AUC = 0.910), moderate

accuracy for FASN (AUC = 0.828), and lim-

ited predictive value for SNCA (AUC = 0.699)

and CXCR4 (AUC = 0.691) (Fig. 5F-H).

Analysis of immune cell infiltration

Comparative analysis of immune cell

infiltration using Wilcoxon rank-sum tests

revealed significant abundance differences

(p<0.05) for 26 of 28 immune cell types

between obese and control groups in the

GSE59034 dataset (Fig. 6A). Correlation

analysis of these differentially abundant im-

mune populations identified a particularly

strong positive association (r = 0.98) be-

tween activated dendritic cells and myeloid-

derived suppressor cells (MDSCs) (Fig. 6B).

Ontology ID Description Gene

Ratio

p p adjust q geneID Count

KEGG hsa04910 Insulin signaling

pathway

2/5 137/

8164

0.00270445 0.05414617 0.04136804 FASN/

EIF4EBP1

KEGG hsa04218 Cellular senescence 2/5 156/

8164

0.0034933 0.05414617 0.04136804 TGFB1/

EIF4EBP1

KEGG hsa05163 Human

cytomegalovirus

infection

2/5 225/

8164

0.00715783 0.08875712 0.06781104 CXCR4/

EIF4EBP1

BP: biological process; CC: cellular component; MF: molecular function.

Table 2. CONTINUATION

398 Yang et al.

Investigación Clínica 66(4): 2025

Fig. 3. GO and KEGG analyses.

(A) Bar charts of GO and KEGG analysis results for A&GRDEGs. The vertical axis shows GO and KEGG

terms. (B) Bubble charts of GO and KEGG analysis results for A&GRDEGs. The vertical axis shows GO

and KEGG terms. (C–E) Network diagrams of GO enrichment analysis for A&GRDEGs (C: BP, D: CC,

E: MF). (F) Network diagrams of KEGG enrichment analysis for A&GRDEGs. In the network diagrams

(C–F), pink dots represent specific pathways and blue dots represent specific genes. In the bubble

charts, the bubble size represents the number of genes, and the bubble color represents the p-value,

with redder hues indicating smaller values and bluer hues indicating larger values. The screening cri-

teria for GO and KEGG analyses were p<0.05 and q <0.25.

Multi-Omics map of obesity inflammation 399

Vol. 66(4): 390 - 407, 2025

Correlation analysis between the 26 dif-

ferentially abundant immune cell types and

A&GRDEG expression levels in GSE59034

revealed a strong positive association be-

tween TGFB1 expression and MDSC infiltra-

tion (r = 0.94), and a significant negative

correlation between EIF4EBP1 levels and

Th1 cell abundance (r = -0.87) (Fig. 6C).

DISCUSSION

The continuous rise of the obesity rate

requires multifaceted and effective preven-

tion and treatment. Inflammatory programs

are activated during the early stages of adi-

pose tissue expansion and during chronic

obesity, thus perpetuating a proinflamma-

tory phenotype in the immune system. This

metabolic dysregulation may elevate the risk

of developing severe comorbidities, includ-

ing insulin resistance, T2DM, cardiovascular

disease, NAFLD, certain malignancies, and

neurodegenerative disorders 23. Inhibition of

glycolysis has been shown to suppress mac-

roautophagy and chaperone-mediated au-

tophagy, leading to increased lipid accumu-

lation 24. The mechanisms of both autophagy

and glycolysis in obesity-associated diseases

are currently hot topics in research. Further

research on the regulatory mechanisms of

autophagy and the effects of glycolysis lev-

els on obesity and chronic inflammation will

help formulate more effective treatment

strategies for obesity control.

By phosphorylating the EIF4EBP1,

mTOR could dissociate from eIF4E and pro-

mote the initiation of translation 25. There-

fore, EIF4EBP1 is believed to be closely re-

lated to a wide range of diseases through its

regulation of autophagy and glycolysis26,27.

Fig. 4. GSEA of the GSE59034 dataset.

(A) Ridgeline plots of the four main biological functions in GSEA for the GSE59034 dataset. (B–E)

Genes in the GSE59034 dataset were significantly enriched in interleukin 10 signaling (B), neutrophil

degranulation (C), leishmania infection (D) and overview of proinflammatory and profibrotic mediators

(E). The screening criteria for GSEA were p<0.05 and q <0.25. NES, normalized enrichment score.

400 Yang et al.

Investigación Clínica 66(4): 2025

Experimental studies demonstrate that

mTOR target proteins EIF4EBP1 and EIF-

4EBP2 play critical roles in metabolic regula-

tion. Genetic ablation of both EIF4EBP1 and

EIF4EBP2 exacerbates diet-induced obesity

in murine models 28. Conversely, enhanced

EIF4EBP1 activity in skeletal muscle confers

metabolic protection, mitigating age- and

diet-induced insulin resistance while main-

taining energy expenditure. This protec-

tive effect is associated with reduced white

adipose tissue accumulation and preserved

brown adipose tissue mass 29. The above re-

sults are consistent with the findings of this

Fig. 5. Differential expression and ROC analyses of A&GRDEGs.

(A) Group comparison plot of A&GRDEGs between the obese and control groups in the GSE134913

dataset. (B) Group comparison plot of A&GRDEGs between the obese and control groups in the

GSE59034 dataset. (C–E) ROC curves of A&GRDEGs: FASN and SNCA (C), EIF4EBP1 and TGFB1

(D), and CXCR4 (E) between different groups (obese or control) in the GSE134913 dataset. (F-H)

ROC curves of A&GRDEGs: FASN and SNCA (F), EIF4EBP1 and TGFB1 (G), and CXCR4 (H) between

different groups (obese or control) in the GSE59034 dataset. *p<0.05; ***p<0.001.

Sensitivity (TPR)

1-Specicity (FPR)

Multi-Omics map of obesity inflammation 401

Vol. 66(4): 390 - 407, 2025

study, EIF4EBP1 is closely related to the reg-

ulatory mechanisms of obesity and chronic

inflammation. However, the mechanisms

by which EIF4EBP1 regulates obesity and

chronic inflammation through autophagy

and glycolysis require further study.

Patients with hyperlipidemia exhibit

elevated levels of TGF-β1, which could sup-

press the function of natural killer (NK)

cells, whereas restoring NK cell function

could improve the prognosis of patients with

metabolic syndrome 30. Studies have shown

that TGF-β1 could promote autophagy via

the Smad and non-Smad pathways, driving

hepatic fibrosis in NAFLD, and the progres-

sion of cardiac and renal fibrosis after ioniz-

ing radiation 31, 32. TGF-β1 induces metabolic

reprogramming in target cells, shifting ener-

gy production from mitochondrial oxidative

phosphorylation to glycolytic metabolism

– a phenomenon consistent with the War-

burg effect observed in many pathological

states33. Systemic blocking of TGF-β1 signal-

ing regulates glucose tolerance and energy

homeostasis, protecting mice from obesity,

diabetes, and fatty liver disease 34. TGF-β1

directly modulates PBX-regulating protein-

1expression in both adipose-derived stem

cells and mature adipocytes, thereby regu-

lating adipogenic differentiation and insulin

Fig. 6. ssGSEA of the GSE59034 dataset.

(A) Group comparison box plots of immune cells under the obese and control groups in the GSE59034

dataset. (B) Heatmap of correlations among the 26 immune cell types with significant differences

in the GSE59034 dataset. (C) Correlation dot plot between the expression levels of A&GRDEGs and

the abundance of 26 infiltrating immune cell types. Red indicates positive correlation and blue

indicates negative correlation, with the shade of color indicating the strength of correlation. ns

p≥0.05; * p<0.05; ** p<0.01; *** p<0.001.

402 Yang et al.

Investigación Clínica 66(4): 2025

sensitivity 35. Many studies have shown a re-

lationship between TGF-β1 and obesity and

chronic inflammation, consistent with our

study. However, there is no research on the

mechanism of TGF-β1 inhibiting obesity and

chronic inflammation by regulating autoph-

agy and glycolysis levels, which is worthy of

further exploration.

FASN is a key enzyme in hepatic de

novo lipogenesis, while its upregulation is

associated with insulin resistance 36. Com-

pared with normal-weight patients, FASN

expression is significantly downregulated in

patients with obesity 37. Studies have dem-

onstrated that the effect of hepatic FASN

deficiency on NAFLD and diabetes is depen-

dent on the etiology of obesity 36. In mice

with diet-induced obesity, adipose-specific

FASN knockout aggravated high-fat diet-

induced metabolic disturbances, exacerbat-

ing both hyperglycemia and hepatic dysfunc-

tion. These effects may be mediated through

impaired hepatic glucose uptake secondary

to glycogen accumulation and suppressed

glycolytic flux 36. The elevated FASN expres-

sion observed in obesity may confer a prolif-

erative advantage to malignant cells through

enhanced lipogenesis, potentially promoting

tumorigenesis in obese microenvironments

38. However, research has shown that renal

cancer patients with obesity or overweight

have a longer median overall survival owing

to low FASN expression 37. Therefore, consid-

ering the presence of obesity or overweight

is important in future interventional treat-

ments targeting the FASN pathway.

The high expression levels of the SNCA

gene are closely associated with earlier on-

set, more rapid progression, and more severe

manifestation of Parkinson’s disease 39. The

SNCA gene encodes α-synuclein. Mitochon-

drial accumulation of α-synuclein aggre-

gates triggers apoptotic pathways through

multiple mechanisms, including mitochon-

drial permeability transition pore open-

ing, calcium efflux, cytochrome C release,

and subsequent mitochondrial swelling 40.

Importantly, high-fat diet-induced obesity

was shown to accelerate the onset of mo-

tor deficits in human α-synuclein-expressing

transgenic mice, correlating with premature

α-synucleinopathy development and astrogli-

osis 41. These findings suggest that diet-in-

duced metabolic dysfunction may represent

a significant environmental risk factor for

α-synuclein pathology progression. Hence,

molecular and cellular mechanisms under-

lying the interactions between obesity and

SNCA in cognitive disorders await further

elucidation.

CXCR4, the exclusive receptor for che-

mokine (C-X-C motif) ligand 12, orchestrates

neutrophil metabolic reprogramming toward

glycolysis and lactate production, while driv-

ing neutrophil accumulation in both circu-

lation and psoriatic skin lesions 42. Beyond

its inflammatory functions, metabolic stress

induces fat mass and obesity-related protein

(FTO)-dependent N6-methyadenosine (m6A)

mRNA demethylation, which upregulates

CXCR4 expression via autophagic pathways.

This mechanism critically contributes to mel-

anoma pathogenesis and confers resistance

to PD-1 checkpoint inhibition 43. Moreover,

inhibition of CXCR4 led to the sensitization

of osteosarcoma to doxorubicin via the induc-

tion of autophagic cell death 44. In summary,

CXCR4 is closely related to inflammation, au-

tophagy, and glycolysis, and hence its regu-

latory role in patients with obesity warrants

further exploration.

Functional enrichment analysis revealed

that the five A&GRDEGs were significantly

associated with five key biological processes:

insulin signaling, AMPK-mediated metabolic

regulation, intestinal immune network for

IgA production, cellular senescence path-

ways, and human cytomegalovirus (CMV)

infection response. Among these, biological

processes such as insulin resistance, AMPK

signaling pathway, intestinal IgA immunity,

and cellular senescence are thought to be

closely related to chronic low-grade inflam-

mation in patients with obesity 45-48. GSEA

of the GSE59034 dataset demonstrated sig-

nificant enrichment of pro-inflammatory

Multi-Omics map of obesity inflammation 403

Vol. 66(4): 390 - 407, 2025

and pro-fibrotic pathways in obesity. Nota-

bly, three metabolic regulators (EIF4EBP1,

FASN, and TGFB1) exhibited consistent

diagnostic accuracy across both datasets

(Fig. 5). These findings implicate autopha-

gy-glycolysis crosstalk in obesity pathogen-

esis, potentially through the amplification

of inflammatory and fibrotic cascades. How-

ever, whether these five A&GRDEGs could

be applied clinically for the prevention and

treatment of obesity needs to be confirmed

through further investigations.

Among the five A&GRDEGs, TGFB1,

SNCA, and CXCR4 expression positively cor-

related with immune cell recruitment, while

FASN and EIF4EBP1 showed significant

inverse associations with immune infiltra-

tion levels. This dichotomous relationship

suggests differential roles in immunometa-

bolic regulation within obese adipose tissue.

TGFB1 showed the strongest positive corre-

lation with MDSCs, while EIF4EBP1 showed

the strongest negative correlation with Th1

cells. Th1 cells, playing a facilitatory role in

the pathogenesis of autoimmune diseases

and tissue inflammatory responses, is regu-

lated by cytokines IFN-γ 49. Furthermore, an

elevated Th1 cell count was linked with ele-

vated adipose tissue inflammation and insu-

lin resistance, with its cell count increasing

alongside increasing obesity 50. In vitro evi-

dence demonstrates that IFN-γ exacerbates

adipose tissue inflammation in obesity, while

IFN-γ knockout mice show improved meta-

bolic profiles, including enhanced insulin

sensitivity and reduced inflammatory mark-

ers in high-fat diet models 51. However, the

mechanistic relationship between EIF4EBP1

expression and Th1 cell infiltration in obese

adipose tissue remains unexplored. This

knowledge gap highlights the need for fo-

cused investigations into EIF4EBP1-mediat-

ed immunometabolic regulation in chronic

inflammation associated with obesity.

This study also has some limitations.

First, the results from the two datasets in-

cluded were not completely consistent, high-

lighting the need for experimental validation

and prospective clinical studies, which rep-

resent an important direction for our future

research. Second, although screening was

performed on obesity-related A&GRDEGs,

the specific mechanisms of action involved

were not clarified, which warrants further in-

vestigation.

In conclusion, levels of autophagy and

glycolysis exhibit substantial predictive value

for obesity development and mechanistically

contribute to disease pathogenesis through

immunometabolic dysregulation. These re-

sults provide a foundational framework for

understanding obesity-associated chronic in-

flammation, offering new molecular targets

for both basic research and potential thera-

peutic development.

Acknowledgements

The authors gratefully acknowledge the

data provided by patients and researchers

participating in GEO.

Funding

This research received no specific grant

from any funding agency in the public, com-

mercial, or not-for-profit sectors.

Conflict of interests

All authors declare that they have no

conflict of interest.

Ethics approval and consent

to participate

Not applicable, because GEO belongs

to public databases, the patients involved in

the database have obtained ethical approval,

users can download relevant data for free

for research and publish relevant articles,

and our study is based on open-source data,

and the The Fifth Affiliated Hospital of Sun

Yat-sen University does not require research

using publicly available data to be submit-

ted for review to their ethics committee, so

there are no ethical issues and other con-

flicts of interest.

404 Yang et al.

Investigación Clínica 66(4): 2025

Consent for publication

Not applicable.

Availability of data and materials

The original contributions presented in

the study are included in the article/Supple-

mentary material; further inquiries can be

directed to the corresponding author.

ORCID Numbers

• Simin Yang (SY):

0009-0008-4837-9124

• Rexidanmu Hudabai (RH):

0009-0005-8911-5916

• Xinwei Su (XS):

0009-0001-4289-7018

Authors’ contributions

SY designed the research, collected the

data, performed the statistical analysis, and

drafted the manuscript. RH collected the

data and performed the statistical analysis.

XS supervised the statistical analysis, re-

viewed, and edited the manuscript. All au-

thors have approved the final manuscript.

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