Abstract
Background
Lung adenocarcinoma (LUAD) is one of the most prevalent types of lung
cancer globally; it is characterized by high incidence and mortality
rates and contributes to over 1.8 million deaths annually. PSMB7, a
crucial component of the 20S proteasome involved in protein degradation
and antigen presentation, has been implicated in various cancers;
however, its specific function in LUAD remains inadequately explored.
Methods
This research aimed to investigate the expression of PSMB7 in LUAD and
its clinical significance using real-time quantitative PCR,
immunohistochemistry, differential expression analysis, pathway
enrichment analysis, immune cell infiltration, and DNA methylation.
Results
PSMB7 expression levels in LUAD tissues were considerably higher than
those in the surrounding normal lung tissues and were associated with
advanced pathological stages and poorer clinical outcomes. High PSMB7
expression was correlated with reduced overall and disease-specific
survival. Functional enrichment analysis indicated that the
differentially expressed genes associated with PSMB7 were mainly
involved in protein–DNA complex assembly and chromatin remodeling.
Moreover, LUAD tissues showed lower DNA methylation in PSMB7 promoters
than that in normal lung tissues, which was correlated with reduced
survival rates. A negative correlation was observed between PSMB7
levels and immune cell infiltration, particularly for effector memory
T, B, follicular helper T, and mast cells.
Conclusions
We identified PSMB7 as a promising biomarker for LUAD prognosis because
of its strong association with tumor progression and immune
microenvironment modulation. Future studies should explore therapeutic
strategies targeting PSMB7 to improve patient outcomes for LUAD.
Keywords: Lung adenocarcinoma, PSMB7, Biomarker, Prognosis, Immune
microenvironment
Introduction
Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell
lung cancer (NSCLC), accounting for approximately 40% of all lung
cancer cases ([36]Kishikawa et al., 2022; [37]Li et al., 2023). LUAD
ranks among the leading causes of cancer-related deaths globally, with
approximately 1.8 million fatalities reported each year ([38]Ahn, Choi
& Kim, 2021). The incidence of LUAD has been increasing, particularly
in nonsmokers and women ([39]Shan et al., 2013). Despite some progress
in diagnostic and treatment approaches, the prognosis for LUAD remains
grim, with a 5-year survival rate <20% ([40]Tong et al., 2020). LUAD is
a multifaceted disease marked by a variety of characteristics and a
wide range of molecular changes. This complexity suggests that tumors
in different patients can have unique features, resulting in different
responses to treatment ([41]Hua et al., 2020). Although diagnostic and
treatment methods have improved, many patients diagnosed with LUAD
still have a bleak prognosis. This indicates an urgent need to develop
and implement more effective diagnostic and treatment strategies.
The proteasome subunit beta type-7 (PSMB7) gene is a key component of
the proteasome complex, which is crucial not only for protein
degradation but also plays an important role in various cellular
processes, such as cell cycle regulation, apoptosis, and DNA repair
([42]Ma et al., 2017). Although the role of PSMB7 as a key subunit of
the proteasome complex in various tumors has gradually gained attention
([43]Begum, Thota & Batra, 2023), its function and mechanism of action
in LUAD have not yet been systematically elucidated. Currently, a
significant knowledge gap exists regarding the expression pattern of
PSMB7 in LUAD, its clinical relevance, and relationship with tumor
progression, and its specific role in regulating the tumor immune
microenvironment. Despite sporadic reports suggesting that PSMB7 is
associated with the occurrence and development of LUAD, its mechanisms
of action in tumor cell immune evasion, immune cell infiltration, and
regulation of DNA methylation remain unclear. Furthermore, existing
studies lack a systematic validation of the applicability of PSMB7 as a
prognostic biomarker and its potential value in immunotherapy
([44]Zhang et al., 2024). Therefore, a thorough investigation of the
expression characteristics of PSMB7 in LUAD, its clinical significance,
and its interactions with the immune microenvironment, as well as
clarification of its molecular mechanisms, will provide an important
theoretical basis for revealing new mechanisms of LUAD development and
identifying potential therapeutic targets.
In this study, we aimed to systematically assess the transcriptional
and protein expression levels of PSMB7 in LUAD; analyze its association
with the patients’ clinicopathological features, prognosis, and tumor
immune infiltration; and explore the impact that DNA methylation
modifications have on expression and prognosis. Through these
multidimensional, comprehensive analyses, this study aimed to fill the
knowledge gap regarding PSMB7 in the field of LUAD, reveal its
potential as a prognostic and immune-related molecular biomarker, and
provide experimental evidence for subsequent therapeutic strategies
targeting PSMB7.
Materials & Methods
Sample collection
From April to May 2025, anatomically matched tumor and adjacent normal
tissues were collected from 15 treatment-naïve patients with LUAD who
underwent surgical resection at the Thoracic Surgery Department of
Daping Hospital in Chongqing, China. Individuals with an unclear
pathological diagnosis and insufficient clinical information were
excluded. The freshly excised specimen is immediately fixed in 10%
neutral formalin. The consistency of the histopathological diagnosis
was confirmed through a dual-blind pathological review performed by two
independent, certified pathologists. The clinicopathological
characteristics are shown in [45]Table S1. The Ethics Committee of the
Third Affiliated Hospital of the Army Medical University approved this
study (no. 300, 2024), and all patients provided signed informed
consent. The methods used in this study strictly adhered to the
principles of the Declaration of Helsinki, as revised in 2013.
Real-time fluorescent quantitative PCR detection
Total RNA was extracted from paraffin-embedded tissues using nucleic
acid extraction reagents (FFPE DNA/RNA; Aide Biotechnology, Guangzhou,
China), according to the manufacturer’s protocol. The purity and
concentration of RNA were measured using a NanoDrop spectrophotometer
(Thermo Fisher Scientific, Waltham, MA, USA), with all samples having
an A260/A280ratio of >1.9. First-strand cDNA was
synthesized from 150 ng total RNA in a 20-µL reaction volume using RT
Master Mix for qPCR II (MedChemExpress, Monmouth Junction, NJ, USA).
Gene-specific primers were designed using Primer-BLAST (NCBI) and
synthesized by Sangon Biotech (Shanghai, China), followed by
purification using polyacrylamide gel electrophoresis (>99% purity).
The primers used were as follows: PSMB7 forward
(5′-CAACTGAAGGGATGGTTGTTGC-3′) and PSMB7 reverse
(5′-GCACCAATGTAACCTTGATACCT-3′); GAPDH forward (5′-CAGGAGGCATTGCTGA
-TGAT-3′) and GAPHD reverse (5′-GAAGGCTGGGGCTCATTT-3′). Specificity:
melt curve analysis confirmed single peak amplification (Tm: PSMB7 =
86.5 ± 0.3 °C; GAPDH = 87.1 ± 0.2 °C) with no primer dimer peaks
(<80 °C). Efficiency: the standard curves generated from a 5-fold
series of cDNA dilutions (five points) provided amplification
efficiencies of 98.5% (R^2 = 0.999) for PSMB7 and 101.2% (R^2 = 0.998)
for GAPDH (optimal range: 90–110%). The quantitative PCR (qPCR)
amplification reactions were performed in a 20-µL volume, with samples
run on the Applied Biosystems 7500 real-time PCR system (Thermo Fisher
Scientific) under the following conditions: initial denaturation at
95 °C for 2 min, followed by 40 cycles of amplification, denaturation
at 95 °C for 10 s, and annealing at 60 °C for 30 s. Melt curve analysis
was performed at the end of the PCR cycles: 60–95 °C (increment:
0.5 °C/s). Gene expression was normalized to that of GAPDH (reference
gene) and calculated using the 2^−ΔΔCt method. Technical
reproducibility: the intragroup coefficient of variation (CV) of the
ΔCt values was <1% in three replicates. Biological reproducibility:
three independent biological replicates were analyzed (intergroup CV of
ΔΔCt < 5%) (raw data in [46]Table S2).
Immunohistochemistry
Immunohistochemistry (IHC) staining was conducted on lung tissue
sections that had been fixed in formalin and subsequently embedded in
paraffin, as well as on paired normal adjacent tissue sections, with a
thickness of 3 µm. The sections were stained with an anti-human PSMB7
antibody (1:2000; mouse; TA504368S; OriGene, Wuxi, China), following
the manufacturer’s instructions. Finally, the sections were
counterstained with hematoxylin. IHC staining results were observed
under an Olympus optical microscope (Olympus BX41; Olympus, Tokyo,
Japan). Each LUAD sample was evaluated by using the H-score calculation
method. The calculation formula used is as follows: (percentage of weak
intensity cells ×1) + (percentage of moderate intensity cells ×2) +
(percentage of strong intensity cells ×3). The numbers 0, 1, 2, and 3
represent the intensities of cell staining. The H-score value ranged
from 0–3.
Bioinformatics analysis
All bioinformatics analyses were carried out using the Xiantao Academic
Network Server ([47]https://www.xiantaozi.com/) based on R software
(v4.2.1; [48]R Core Team, 2022). The following key R packages were
used: ggplot2 v3.4.4 (visualization), stats v4.2.1 (statistical tests),
survival v3.3.1 (KM/Cox models), GSVA v1.46.0 (immune infiltration),
and clusterProfiler v4.4.4 (functional enrichment). For input
normalization, raw count data from The Cancer Genome Atlas (TCGA)/GTEx
were converted to TPM values using the Toil pipeline ([49]Vivian et
al., 2017), followed by log2(value + 1) transformation. We also
analyzed the protein expression levels of PSMB7 using the Human Protein
Atlas (HPA) database ([50]Digre & Lindskog, 2021). All raw data used in
this study can be accessed at
[51]https://www.jianguoyun.com/p/DQekKeUQ3fe8DRi0_oQGIAA.
PSMB7 expression profile analysis
Analysis specific to pan-cancer and LUAD: the RNA-seq TPM data of PSMB7
was log2-transformed. We used the Wilcoxon rank-sum test to compare
differences in expression between the tumor and normal tissues; R
packages used: ggplot2 v3.4.4, stats v4.2.1, and car v3.1-0. A paired
t-test was used to compare the paired samples of TCGA-LUAD tumor and
adjacent normal tissues. Diagnostic receiver operating characteristics
(ROC) curves were generated using pROC v1.18.0.
Clinical relevance analysis
We used the Kruskal–Wallis test to determine how PSMB7 expression
correlates with the clinical features of LUAD. The RNA-seq and clinical
data were filtered to exclude normal samples and cases with missing
clinical records.
Survival analysis
The Kaplan–Meier curve (R packages used: survival v3.3.1 and survminer
v0.4.9) was used to evaluate overall survival (OS), and the patients
were risk-stratified based on the optimal cutoff value. After screening
the variables through univariate Cox regression (threshold
p-value < 0.1), multivariate Cox regression (rms v6.3-0) was applied to
establish a prognostic model, and the results were visualized using a
forest plot. The primary clinical endpoint, complete response (CR), was
strictly defined according to the RECIST 1.1 criteria ([52]Eisenhauer
et al., 2009) as the complete disappearance of all target lesions, with
data sourced from the TCGA clinical annotations. CR status, which
reflects treatment efficacy, was also included as a key indicator for
survival prediction in solid tumors ([53]Schwartz et al., 2016), and
the other covariates (TNM staging and ECOG performance status) were
extracted from electronic medical records through standardized
processes. The expression level of PSMB7 was log2(TPM + 1)-normalized
and input as a continuous variable into the model, with the
proportional hazards assumption verified through Schoenfeld residual
tests. Additionally, subgroup survival analyses stratified by clinical
variables were performed to further assess the robustness of prognostic
factors.
Differential analysis and functional enrichment
When focusing on PSMB7, a volcano plot was used to display
differentially expressed genes (DEGs), with a gray dashed line marking
the threshold (plotted using ggplot2). We selected DEGs based on the
following criteria: —log2 FC—>0.5 and adjusted p-value (FDR) < 0.05
(DESeq2 v1.38.0). We identified key pathways using co-expression
heatmaps (Spearman correlation) and Gene Ontology/Kyoto Encyclopedia of
Genes and Genomes (GO/KEGG) enrichment analysis (clusterProfiler
v4.4.4, GOplot v1.0.2) combined with z-value calculations. GSEA was
used to display the enrichment patterns.
Functional enrichment analysis
For the GO/KEGG analysis, the following clusterProfiler parameters were
used: pAdjustMethod = “BH,” p value Cutoff = 0.01, and q value Cutoff =
0.05. GSEA analysis: hallmark gene sets (MSigDB v7.5), parameters: min
GS Size = 10, max GS Size = 500, and FDR < 0.25.
Methylation and immune infiltration analysis
We used the UALCAN database to analyze the methylation level of the
PSMB7 promoter region, and MethSurv was used for methylation-mRNA
correlation analysis. The TCGA-LUAD HM450 methylation data were
obtained using the UALCAN ([54]https://ualcan.path.uab.edu/) and
MethSurv ([55]https://biit.cs.ut.ee/methsurv) databases. The ssGSEA
algorithm (GSVA v1.46.0) was used to assess immune cell infiltration,
and the association with PSMB7 expression was displayed using lollipop
plots and heatmaps. Based on the ssGSEA algorithm ([56]Hänzelmann,
Castelo & Guinney, 2013), the GSVA parameters were set as follows: gene
set, 24 immune cell markers from [57]Bindea et al. (2013). Core
parameters: method = “ssgsea,” kcdf = “Gaussian,” and tau = 0.25 (rank
normalization weighting factor). We calculated immune infiltration
status using the 24 immune cell markers obtained from the Immunity
article ([58]Bindea et al., 2013), including activated dendritic cells
(DCs), B cells, CD8 T cells, cytotoxic cells, DCs, eosinophils,
immature DCs, macrophages, mast cells, neutrophils, natural killer (NK)
CD56bright cells, NK CD56dim cells, NK cells, plasmacytoid DCs, T
cells, T helper cells, T central memory cells, T effector memory (Tem)
cells, T follicular helper (TFH) cells, T gamma delta cells, Th1 cells,
Th17 cells, Th2 cells, and T regulatory cells.
Validation of the nomogram
Nomograms are statistical prognostic models that present data using
simple graphics. Nomograms are used to assess risk and prognosis based
on specific patient characteristics or biomarkers. In a nomogram, each
indicator of a sample corresponds to a predicted value on a specific
axis, and the final total score can predict survival rates at 1-, 3-,
and 5-years. A nomogram was constructed using the “rms” and “survival”
packages in R, based on independent prognostic factors. Calibration
plots were used to validate the nomogram’s effectiveness, and the
concordance index was then calculated.
Statistical analysis
Statistical analyses of intergroup comparisons were performed using
Wilcoxon and Kruskal–Wallis tests, as well as paired t-tests. Spearman
correlation was used to assess co-expression relationships. A Cox
regression model was used to analyze the survival correlation (p
< 0.05, considered significant).
Results
Elevated PSMB7 expression in LUAD
A comprehensive pan-cancer investigation indicated that the expression
levels of PSMB7 were substantially increased in most tumor types,
including bladder, breast, and colorectal cancers, compared with those
in normal adjacent tissues ([59]Fig. 1A). The concentration of PSMB7 in
LUAD tissues was markedly elevated compared with that observed in
normal tissues ([60]Fig. 1B). In 58 LUAD tissues, the expression level
of PSMB7 was markedly elevated compared with that in the corresponding
adjacent tissues ([61]Fig. 1C). ROC curve analysis suggested that the
expression level of PSMB7 could accurately predict LUAD, with an area
under the curve of 0.712 ([62]Fig. 1D).
Figure 1. The expression level of PSMB7 in LUAD.
[63]Figure 1
[64]Open in a new tab
(A) PSMB7 is highly expressed in many solid tumors, including LUAD
tissues (B, C). The ROC curve area was 0.712 (D), indicating that PSMB7
may serve as a diagnostic biomarker for LUAD. P-values were calculated
using a two-tailed unpaired Student’s t-test, with ^∗p < 0.05,
^∗∗p < 0.01, ^∗∗∗p < 0.001. LUAD, lung adenocarcinoma; PSMB7,
proteasome subunit beta type-7; ROC curve, Receiver Operating
Characteristic curve.
Real-time qPCR (RT-qPCR) and IHC analyses were conducted to assess the
expression levels of PSMB7 in LUAD tissues. Fifteen LUAD tissue samples
and their adjacent normal tissues were randomly selected for RT-qPCR.
The expression level of PSMB7 in LUAD tissues was substantially higher
than that measured in the paired, adjacent, and normal tissues
([65]Fig. 2A). The analytical data obtained from the HPA website
([66]Figs. 2B, [67]2C) and IHC validation ([68]Figs. 2D, [69]2E)
indicated that the expression level of PSMB7 in LUAD samples far
exceeded that in normal tissues.
Figure 2. RT-qPCR and IHC were used to detect the level of PSMB7 in LUAD.
[70]Figure 2
[71]Open in a new tab
RT-qPCR (A) The results showed that the PSMB7 level in LUAD tissues was
higher than that in adjacent tissues. (B and C) PSMB7 protein
expression levels from the Human Protein Atlas database. (D and E) The
differential expression of PSMB7 protein was verified by
immunohistochemistry. LUAD, lung adenocarcinoma; IHC,
immunohistochemistry; RT-qPCR, Reverse Transcription Quantitative
Polymerase Chain Reaction; HPA, Human Protein Atlas. Representative
RT-qPCR and IHC results in 15 samples. (Comment: expanded by Zhu
Guihua).
Association between PSMB7 expression and clinical pathological variables
The elevated expression of PSMB7 was strongly correlated with advanced
pathological stages, especially when contrasting stage III with stage I
([72]Fig. 3A), T4 with T1 ([73]Fig. 3B), and N2 with N0 ([74]Fig. 3C).
Elevated expression levels of PSMB7 were strongly correlated with
adverse clinical outcomes, particularly in the comparison between CR
and progressive disease ([75]Fig. 3D), as well as OS and
disease-specific survival (DSS) ([76]Figs. 3E, [77]3F).
Figure 3. Associations between PSMB7 expression and clinicopathological
characteristics in LUAD.
[78]Figure 3
[79]Open in a new tab
Data are shown for (A and B) pathological stage, (C) N stage, (D)
primary therapy outcome, (E) OS event, and (F) DSS event. P-values were
calculated using a two-tailed unpaired Student’s t-test, with
^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001. CR, complete response;
LUAD, lung adenocarcinoma; SD, stable disease; OS, Overall Survival;
DSS, Disease-Specific Survival.
Prognostic value of PSMB7 in LUAD
The KM method was used to assess the relationship between PSMB7 levels
and LUAD prognosis. To categorize patients according to PSMB7
expression, a minimum p-value was set as the threshold, and the
patients were then divided into high- and low-expression cohorts. High
PSMB7 expression correlated with a substantially poorer OS ([80]Fig.
4A) and DSS ([81]Fig. 4B). Furthermore, individuals exhibiting elevated
PSMB7 expression experienced poorer prognoses at different stages of
pathology (particularly between stages I and III and stages II and
III), different tumor size categories (notably T1 vs. T4 and T1 vs.
T3), the presence or absence of lymph node metastasis (N0 vs. N2), the
presence or absence of distant metastasis (M0 vs. M1), clinical
treatment results in progressive disease versus CR, individuals aged
>65 years, and smokers ([82]Fig. 5). Univariate and multivariate Cox
regression analyses were performed to elucidate prognostic factors for
patients with LUAD. In patients with LUAD, the expression of PSMB7, N2
stage, stable disease condition, and CR were identified as independent
factors in determining OS ([83]Fig. 4C).
Figure 4. The impact of PSMB7 levels on prognosis in lung adenocarcinoma
patients was evaluated using Kaplan–Meier analysis.
[84]Figure 4
[85]Open in a new tab
(A) OS and (B) DSS in patients with lung adenocarcinoma with high and
low PSMB7 expression levels. (C) Forest plot depicting OS outcomes in
patients with lung adenocarcinoma based on multivariate Cox regression
analysis. DSS, disease-specific survival; OS, overall survival.
Figure 5. The impact of PSMB7 levels on prognosis across different subgroups
of patients with LUAD as assessed by Kaplan–Meier analysis.
[86]Figure 5
[87]Open in a new tab
(A–I) OS curves of stage I and III , stage II and III , T1 and T3, T1
and T4, N0 and N2, M0 and M1, PD and CR, smoker, age, between high- and
low- PSMB7 patients with LUAD. LUAD, lung adenocarcinoma; OS, overall
survival; PD, Progressive Disease; CR, Complete Response.
Functional enrichment analysis of DEGs associated with PSMB7
Differential analysis using DESeq2 identified 424 differentially
expressed coding genes between the high and low PSMB7 expression
groups, including 205 upregulated and 219 downregulated genes (adjusted
p-value <0.05, —log2 FC—>0.5). Spearman correlation analysis of the top
10 most significant DEGs (TSPAN6, TNMD, DPM1, etc.) with PSMB7
expression showed a significant synergistic or negative correlation
([88]Figs. 6A, [89]6B). The GO enrichment analysis results indicated
that the aforementioned DEGs were significantly enriched in biological
processes related to protein–DNA complex assembly, chromatin assembly,
and chromatin remodeling; cellular components mainly involved
nucleosomes and protein–DNA complexes; and molecular functions included
protein heterodimerization and transcriptional repression activity.
KEGG pathway analysis revealed that the differential genes were
primarily enriched in pathways related to olfactory transduction,
systemic lupus erythematosus, and neutrophil extracellular trap
formation ([90]Fig. 6C; [91]Table S3). The GSEA analysis results
further suggested that the high PSMB7 expression group was
significantly enriched in biological processes, such as the DNA
double-strand break response ([92]Fig. 6D).
Figure 6. DEGs related to PSMB7 and its functional enrichment analysis
utilizing GSEA, GO, and KEGG.
[93]Figure 6
[94]Open in a new tab
(A) Blue and red dots indicate significantly downregulated and
upregulated DEGs in the volcano plot, respectively. (B) The top 10 DEGs
were positively correlated with PSMB7 levels. (C) KEGG, GO, and GSEA.
(D) Analyses of DEGs. DEG, differentially expressed gene; GO, Gene
Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia
of Genes and Genomes. DEGs, Differentially Expressed Genes.
Relationship between PSMB7 expression and methylation status
The methylation analysis results from the UALCAN database indicate that
the methylation level of the PSMB7 promoter region in LUAD tissues were
significantly lower than those in normal pulmonary tissues (p < 0.001;
[95]Fig. 7A). Further survival analysis revealed that the OS rate of
patients with LUAD in the low methylation group of PSMB7 was
significantly lower than that in the high methylation group ([96]Figs.
7B, [97]7C), suggesting that methylation modifications play an
important role in the regulation of PSMB7 expression and prognosis of
LUAD.
Figure 7. DNA promoter methylation levels of PSMB7 and its impact on
prognosis in patients with LUAD.
[98]Figure 7
[99]Open in a new tab
(A) Promoter methylation level of PSMB7 in LUAD was significantly lower
than that in normal lung tissue. (B) The correlation between PSMB7 mRNA
expression and promoter methylation. (C) Kaplan–Meier survival curves
for different PSMB7 methylation sites. P-values were calculated using a
two-tailed unpaired Student’s t-test. LUAD, lung adenocarcinoma.
Relationship between PSMB7 and immune infiltration
Immune cell infiltration analysis showed that the expression level of
PSMB7 had a significantly negative correlation with the infiltration of
certain immune cells, especially Tem, B, TFH, and mast cells ([100]Fig.
8A). The ssGSEA results indicated that enrichment scores of the
aforementioned immune cells in the high PSMB7 expression group were
significantly lower than those in the low expression group ([101]Figs.
8B–[102]8I). Additionally, the expression level of PSMB7 was positively
correlated with PD-L1 (CD274) (p < 0.05; [103]Fig. S1), suggesting that
it may be involved in regulating the immune microenvironment in LUAD.
Figure 8. Correlation between PSMB7 level and immune cells infiltration in
LUAD.
[104]Figure 8
[105]Open in a new tab
(A) Correlation between PSMB7 expression and 24 types of immune cells.
(B–E) Comparison of immune infiltration levels of immune cells
(including Tem, TFH, B cells and Mast cells) between high and low PSMB7
level groups. (F–I) The expression of PSMB7 was negatively correlated
with the level of infiltrating immune cells, including Tem, TFH, B
cells and Mast cells. P-values were calculated with two-tailed unpaired
Student’s t-test, ^∗ p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. LUAD, lung
adenocarcinoma; Tem, effector T memory cell; TFH, follicular T helper
cell.
Development and validation of a nomogram based on independent variables
A nomogram incorporating various independent variables was created to
estimate the clinical outcomes of patients diagnosed with LUAD. As the
total score on the scale increased, the adverse factors worsened,
indicating a poorer prognosis for patients with LUAD ([106]Fig. 9A).
Calibration curves were used to evaluate the precision and
dependability of the predictive capabilities of the nomogram
([107]Figs. 9B–[108]9D). The results demonstrated that PSMB7 is an
effective and independent prognostic marker of the outcome in
individuals diagnosed with LUAD.
Figure 9. Calibration curves and nomograms for predicting OS rates in
patients with LUAD.
[109]Figure 9
[110]Open in a new tab
(A) The nomogram chart provides a visual representation of the
predicted OS rates for patients with LUAD at specific time intervals,
such as one, three, and five years. (B–D) Calibration curves are
graphical tools used to assess the accuracy of survival rate
predictions at specific time points in patients with cancer. LUAD, lung
adenocarcinoma; OS, overall survival.
Discussion
In recent years, the diagnosis and treatment of LUAD have heavily
relied on the discovery and application of biomarkers. Traditional
driver genes, such as EGFR and ALK, play a core role in the molecular
classification and targeted therapy of LUAD, particularly as EGFR
mutations and ALK rearrangements guide the clinical application of
targeted drugs, including tyrosine kinase inhibitors, achieving
significant progress in improving the prognosis of some patients.
However, these molecular markers primarily target specific molecular
subgroups and have a limited impact on immune regulation and the tumor
microenvironment. Additionally, Ki67, a classic marker related to cell
proliferation, is often used to assess tumor growth rate and assist in
prognosis judgment, but its specificity and independence are often
limited in multivariate analyses.
In contrast, PSMB7, a proteasome subunit-related molecule, plays an
important role in tumor protein degradation, antigen presentation, and
immune regulation. The present study found that high PSMB7 expression
in LUAD tissues was closely related to advanced pathological staging
and independently predicted poor patient survival outcomes. Notably,
the expression level of PSMB7 had a significantly negative correlation
with tumor immune cell infiltration, particularly in Tem, B, TFH, and
mast cells, suggesting its unique value in regulating the tumor immune
microenvironment. Compared to molecular markers such as EGFR and ALK,
the clinical application prospects of PSMB7 are more focused on
prognostic assessment and the prediction of responses to immunotherapy,
with broader applicability to populations and potential significance as
a combined target for immunotherapy ([111]Zhang et al., 2024).
Furthermore, compared to proliferation markers such as Ki67, PSMB7 not
only reflects the biological behavior of tumors but also participates
in regulating tumor-associated immune mechanisms, allowing for a
complementary role in prognosis assessment and clinical
decision-making.
Therefore, PSMB7 is expected to serve as a novel molecular marker for
prognostic assessment and the personalized treatment of patients with
LUAD, especially in the era of immunotherapy, where its role in the
regulation of the tumor microenvironment and immune evasion mechanisms
warrants further in-depth study. Future multicenter, large-sample,
mechanistic studies would help clarify the combined application value
of PSMB7 with existing biomarkers, providing a more solid theoretical
foundation and practical basis for the precise diagnosis and treatment
of LUAD.
Our findings indicated that high PSMB7 expression was strongly
associated with several negative clinicopathological factors, including
advanced tumor stage, lymph node metastasis, and reduced OS. Moreover,
an elevated PSMB7 level is an independent indicator of poor prognosis
in patients with LUAD. These results suggest that PSMB7 plays a role in
LUAD progression ([112]Zhang et al., 2024). One potential reason for
the link between high PSMB7 expression and unfavorable prognosis in
patients with LUAD is its involvement in various biological processes
through its catalytic function, which includes the regulation of the
stability and degradation of intracellular proteins ([113]Huang et al.,
2024). This regulation can influence the proliferation and
differentiation of tumor cells. The structural features of PSMB7 allow
it to interact with other proteins, forming complex signaling networks
that may contribute to cancer development.
Increased PSMB7 expression might aid tumor progression by promoting
immune evasion within the tumor microenvironment and diminishing T
cell-mediated immune responses ([114]Xing et al., 2022). PSMB7 affects
anti-tumor immune responses by hindering the effective presentation of
tumor cell antigens, which may enable tumor cells to evade immune
surveillance and proliferate. Tem cells are important components in
mediating anti-tumor immune responses, are capable of rapidly
recognizing and killing tumor cells, and play a core role in tumor
immune surveillance and immune therapy responses. The negative
correlation observed between high PSMB7 expression and the level of Tem
cell infiltration suggests that PSMB7 may weaken local anti-tumor
immune effects by affecting the recruitment, survival, or function of
Tem cells in the tumor microenvironment, thereby promoting immune
evasion and tumor progression. Further investigation into the specific
molecular mechanisms by which PSMB7 regulates Tem cell infiltration and
function is needed, such as through in vitro cell experiments and
animal models ([115]Yan et al., 2025). Furthermore, high PSMB7 levels
may promote the M2 polarization of tumor-associated macrophages, which
are typically linked to tumor growth and metastasis. Elevated PSMB7
expression correlated with increased PD-L1 expression, which
potentially facilitates tumor immune escape by modulating immune
responses in the tumor microenvironment ([116]Hong et al., 2023).
Specifically, PSMB7 may influence the stability and expression of PD-L1
by altering intracellular protein degradation pathways ([117]Ma et al.,
2021). In the current study, the expression level of PSMB7 was
positively correlated with that of PD-L1, indicating that PSMB7 may
substantially contribute to the immune evasion observed in LUAD
([118]Zheng et al., 2023). This suggests that increased PSMB7 levels
are not only linked to the inherent characteristics of tumor cells but
are also closely associated with alterations in the surrounding immune
microenvironment. Such immune suppression could enhance the resistance
of tumor cells to both chemotherapy and immunotherapy, complicating
efforts to effectively target and eradicate these cells. This mechanism
may also explain the correlation between elevated PSMB7 expression
levels and adverse outcomes in patients with LUAD.
Abnormal methylation patterns are closely related to the occurrence and
development of various cancers, including LUAD, which is a prevalent
form of NSCLC characterized by a complex interplay of genetic and
epigenetic factors ([119]Fu et al., 2024). Methylation affects not only
the growth, movement, and invasive properties of tumor cells but also
contributes to modulating the immune response within the tumor
microenvironment, ultimately influencing patient outcomes ([120]Luo et
al., 2022). In the present study, we observed that increased PSMB7
expression was associated with lower methylation levels of its DNA.
Specifically, patients with LUAD with low PSMB7 methylation levels had
a poorer prognosis than those with high methylation levels. The
methylation level of PSMB7 in LUAD tissues was lower and correlated
with adverse clinicopathological features of the tumor. Generally, a
low methylation status corresponds with the upregulation of PSMB7
expression and has the potential to boost the susceptibility of tumor
cells to drugs used against cancer, thereby affecting patient
prognoses. The methylation status of the PSMB7 gene holds promise as a
potential biomarker for LUAD; by measuring its methylation levels, we
may gain insights into the tumor malignancy and survival prospects of
patients.
PSMB7 has different biological functions and therapeutic significance
in various malignancies. In LUAD, high PSMB7 expression is
significantly associated with advanced pathological staging, immune
microenvironment remodeling, and poor prognosis. It drives immune
evasion by reducing Tem cell infiltration, promoting PD-L1 expression,
and modulating epigenetic regulation (hypomethylation), suggesting that
it may serve as a potential predictive biomarker for immunotherapy
responses. In contrast, in multiple myeloma, PSMB7 primarily mediates
bortezomib resistance through the compensatory upregulation of
proteasome activity (especially in synergy with PSMB5/PSMB6) and
activates the NF-κB pathway to maintain malignant plasma cell survival,
with its dynamic expression characteristics closely associated with
disease staging ([121]Wu et al., 2021). In breast cancer, the
pro-cancer role of PSMB7 is prominently reflected in the independent
prognostic value of chemotherapeutic drug (anthracyclines/paclitaxel)
resistance and shortened disease-free survival, with its functional
characteristics manifested in the regulation of multidrug resistance
pathways rather than direct immune modulation ([122]Munkácsy et al.,
2010). In terms of therapeutic prospects, PSMB7, as one of the key
subunits of the proteasome, has potential value as an important drug
target. Proteasome inhibitors (such as bortezomib) have been widely
used in the treatment of diseases like multiple myeloma, and future
specific inhibitors targeting PSMB7 are expected to expand new
treatment strategies for solid tumors such as lung adenocarcinoma.
However, the application of proteasome inhibitors in solid tumors still
faces numerous challenges, including drug sensitivity, resistance
mechanisms, and side effects. Relevant research foundations and
clinical translation efforts need to be continuously advanced to
explore more precise and effective treatment methods.
This study has certain limitations. Although we have expanded the
sample size of wet experiments to 15 groups of LUAD and paired adjacent
tissues, further enhancing the reliability of the results, the overall
sample size is still limited, which might impact the study’s
statistical power and how widely the conclusions can be applied.
Additionally, the AUC value for PSMB7 from large sample data is 0.712,
suggesting that its effectiveness as a single-molecule diagnostic tool
has limitations. In our study, we further validated its expression
characteristics by increasing the clinical sample size and
supplementing wet experiments. The results showed that PSMB7 is
continuously highly expressed in LUAD tissues, giving more support for
its potential clinical use. Future research could look into combining
various molecular markers to explore joint diagnostic models to enhance
the sensitivity and specificity of clinical diagnosis. Meanwhile, this
study mainly focused on the expression characteristics of PSMB7, its
prognostic value, and its correlation with the immune microenvironment
and methylation levels, without exploring how PSMB7 is involved in
immune evasion or chromatin remodeling, which should be confirmed with
in vivo and in vitro experiments. Furthermore, the prognostic nomogram
model constructed in this study was just validated internally in the
TCGA database and lacks further validation in external independent
cohorts. In the future, it is necessary to combine multi-center
clinical samples to refine this model and boost its clinical
usefulness. So, even though the results of RT-qPCR and IHC experiments
match up well with the results of bioinformatics analysis, future
studies should expand the sample size to further enhance the
reliability and persuasiveness of the results. Secondly, this study
mainly focused on the relationship between PSMB7 expression and
prognosis, missing detailed studies on the specific functions of PSMB7
in tumor development and its interactions with the tumor immune
microenvironment.
Future research should focus on the following directions. First,
further in vivo and in vitro functional experiments are needed to
elucidate the specific molecular mechanisms of action of PSMB7 during
LUAD development, immune regulation, and drug resistance. Second,
integrating multi-omics data for the systematic analysis of
PSMB7-related pathways and their interactions with other key molecules
is recommended to enhance our overall understanding of the biological
characteristics of LUAD. Third, promoting the standardization of PSMB7
detection technology, developing clinical detection methods with high
sensitivity and specificity, and conducting prospective validation in
multicenter large cohorts are necessary to assess the clinical
application value of PSMB7 as a diagnostic, prognostic, and treatment
response predictive biomarker. Additionally, future studies could
involve constructing multidimensional predictive models that combine
various biomarkers (including immune-related, metabolism-related, and
traditional tumor markers) to improve the accuracy of LUAD diagnoses
and prognostic assessments. Finally, targeted therapies based on PSMB7
and combined immunotherapeutic strategies are worth further
development, which would be aimed at providing more precise and
personalized treatment options for patients with LUAD.
In summary, our findings indicate that high PSMB7 expression levels are
associated with advanced pathological stages and poor clinical
outcomes, and that PSMB7 is important in tumor progression, the immune
microenvironment, and methylation status.
Conclusions
In brief, our study indicates that PSMB7 is upregulated in LUAD, and
that high PSMB7 expression levels can predict a poor prognosis,
providing important insights into the prognosis and treatment of
patients with LUAD.
Supplemental Information
Supplemental Information 1. Clinicopathological characteristics.
[123]peerj-13-19958-s001.xlsx^ (11.5KB, xlsx)
DOI: 10.7717/peerj.19958/supp-1
Supplemental Information 2. RT-qPCR raw data.
[124]peerj-13-19958-s002.csv^ (2.4KB, csv)
DOI: 10.7717/peerj.19958/supp-2
Supplemental Information 3. GOKEGG.
Functional enrichment analyses.
[125]peerj-13-19958-s003.xlsx^ (46KB, xlsx)
DOI: 10.7717/peerj.19958/supp-3
Supplemental Information 4. PSMB7 and CD274 are positively correlated
in LUAD.
Correlation analysis between the level of PSMB7 and CD274 using
bioinformatics techniques. PSMB7 and CD274 are positively correlated in
LUAD.
[126]peerj-13-19958-s004.pdf^ (38.2KB, pdf)
DOI: 10.7717/peerj.19958/supp-4
Supplemental Information 5. Raw data.
[127]peerj-13-19958-s005.zip^ (192KB, zip)
DOI: 10.7717/peerj.19958/supp-5
Supplemental Information 6. MIQE checklist.
[128]peerj-13-19958-s006.xlsx^ (14.9KB, xlsx)
DOI: 10.7717/peerj.19958/supp-6
Supplemental Information 7. Author addition.
[129]peerj-13-19958-s007.docx^ (14.4KB, docx)
DOI: 10.7717/peerj.19958/supp-7
Acknowledgments