Introduction
Lung adenocarcinoma (LUAD) and lung squamous cell
carcinoma (LUSC) are the predominant subtypes of non-small cell
lung cancer (NSCLC), accounting for ~85% of all lung cancer cases
(1). LUAD and LUSC share similar
mechanisms in terms of tumorigenesis and progression, and are often
collectively analyzed as NSCLC. However, there are also numerous
distinct gene expression patterns and developmental mechanisms
observed between these two histological subtypes. For example, a
previous study demonstrated distinct T-lymphocyte infiltration
profiles and prognostic implications in LUAD compared with LUSC
(2). Consequently, it is necessary
to distinguish the pathological types of lung cancer when exploring
the molecular biological mechanisms of the occurrence and
development of lung cancer.
The Hippo signaling pathway serves a pivotal role in
regulating cell proliferation, apoptosis, migration and organ size
control (3). As a core kinase
within this pathway, large tumor suppressor kinase 2 (LATS2) has
been implicated in the pathogenesis of various malignancies
(4). Previous studies have revealed
that LATS2 inhibits the nuclear transcriptional activity of
downstream effectors through phosphorylation, thereby modulating
cell cycle progression, promoting apoptosis and suppressing cell
migration and invasion (5,6). Consequently, reduced LATS2 expression
has been observed in multiple types of cancer, including breast and
esophageal cancer (7,8). Current investigations of LATS2 in lung
cancer have predominantly focused on NSCLC as a collective entity.
While some studies suggest that low LATS2 expression is associated
with a poor prognosis (8,9), analysis using biological information
databases, such as the Gene Expression Profiling Interactive
Analysis database (10), has
revealed that patients with LUAD with high LATS2 expression have
worse survival time than those with lower expression (http://gepia.cancer-pku.cn/detail.php?gene=LATS2).
However, the expression patterns, biological functions and
underlying mechanisms of LATS2 across different lung cancer
subtypes remain poorly characterized.
The advent of bioinformatics has revolutionized
tumor research by enabling large-scale gene expression profiling
using public databases. The Cancer Genome Atlas (TCGA), which
houses extensive genomic data and clinical annotations from diverse
tumor types, provides an invaluable resource for such
investigations (11). Leveraging
the existing bioinformatics tools such as TCGA, the present study
aimed to comprehensively analyze LATS2 expression profiles,
prognostic importance and subtype-specific differences in LUAD and
LUSC. Furthermore, in vitro experiments were performed to
investigate the impact of LATS2 modulation on the biological
behaviors of LUAD and LUSC cell lines, thereby shedding light on
the potential mechanisms underlying LATS2-mediated tumor
suppression in lung cancer.
Materials and methods
Data acquisition
Raw RNA sequencing expression profile data of LUAD
and LUSC samples were obtained from TCGA (https://portal.gdc.cancer.gov; data 43.0; release
date: May 7, 2025). The LUAD dataset comprised 600 samples from 517
patients, including 59 normal samples and 541 tumor samples, while
the LUSC dataset comprised 561 samples from 501 patients, including
51 normal samples and 510 tumor samples. The data were downloaded
and cleaned using the R program (R 4.4.1; Posit Software, PBC) and
were organized into expression matrices for LUAD and LUSC.
Differential expression analysis
Firstly, the Gene Set Cancer Analysis (GSCA)
platform (12) was utilized to
investigate LATS2 expression disparities between normal and tumor
tissues in LUAD and LUSC. Secondly, independent samples t-tests and
paired samples t-tests were employed to further compare LATS2
expression between normal and tumor tissues in LUAD and LUSC
expression matrices. Finally, the Human Protein Atlas database
(version 25.0; http://www.proteinatlas.org/) (13,14)
was consulted to retrieve and compare the expression levels of
LATS2 detected by immunohistochemistry in normal lung tissues, LUAD
tissues and LUSC tissues.
Clinical relevance analysis
LATS2 expression levels were compared across
subgroups categorized by tumor size (T; T0 vs. T1-4), lymph node
metastasis status (N; N0 vs. N1-3), distant metastasis status (M;
M0 vs. M1) and TNM stage (stage I/II vs. stage III/IV) in both LUAD
and LUSC. Survival data and tumor recurrence information were
compiled, and Kaplan-Meier analysis was performed to assess
associations between LATS2 expression and overall survival (OS) and
progression-free survival (PFS). Based on the clinical information
in TCGA, univariate survival analyses were conducted for age, sex,
smoking history, history of other malignancies, residual tumor,
adjuvant therapy, TNM stage and LATS2 expression level (converted
into binary variables based on the median value). Subsequently, the
variables with P<0.1 were included in multivariate regression
analysis to determine whether LATS2 served as an independent
prognostic factor for patient survival. To avoid setting the
P-value too strictly, which would result in a smaller number of
variables that can be included, the P-value was relaxed to 0.1
after multiple trials. Survival risk forest plots were drawn based
on the results.
Kyoto encyclopedia of genes and
genomes (KEGG) and gene ontology (GO) enrichment analysis of
LATS2-related genes
The R package ‘DESeq2’ (15) was employed to identify
differentially expressed genes between normal and tumor tissues
within the LUAD and LUSC expression matrices, with the fold change
threshold set at >2 (absolute value) and the significance
threshold set at <0.05. The ‘cor.test’ function of package
‘psych’ (https://cran.r-project.org/web/packages/psych/index.html)
was utilized to calculate correlations between LATS2 and each
differentially expressed gene. Based on the correlation coefficient
(R), the 100 genes (50 positive and 50 negative) with the strongest
correlation with LATS2 were selected. The 100 genes were included
in KEGG pathway analysis and GO functional enrichment analysis
using the Metascape platform (version v3.5.20260201; http://metascape.org/gp/index.html) (16) to elucidate the primary biological
functions and signaling pathways associated with LATS2-related
genes in LUAD or LUSC.
Immune infiltration analysis
The correlation between the expression levels of
LATS2 and 22 types of immune cells was analyzed using the CIBERSORT
algorithm (17) in the R program,
with the LM22 gene set as the reference. Furthermore, the GSCA
database (http://guolab.wchscu.cn/GSCA/) (12) was used to analyze the correlations
between LATS2 expression and CD4 and CD8+ T cell
infiltration in patients with LUAD or LUSC online.
Human sample collection
Patients with LUAD or LUSC who underwent lung cancer
radical surgery at the Second Affiliated Hospital of Zhengzhou
University (Zhengzhou, China) between July 2022 and January 2023
were included. Inclusion criteria included the following: i) Age
between 18–80 years; ii) diagnosed with primary LUAD or LUSC or TNM
Stage I–IIIb; iii) underwent lung cancer radical surgery without
prior systemic chemotherapy, radiotherapy or immunotherapy; and iv)
Eastern Cooperative Oncology Group score 0–2 and expected survival
>6 months. Exclusion criteria included the following: Patients
with severe infectious diseases, immune system diseases, blood
system diseases, active tuberculosis, heart, liver and kidney
diseases and other organic disorders. A total of 42 lung cancer
samples were collected, 20 LUAD and 22 LUSC, 26 men and 16 women,
aged 63.4±5.6 years. The present study was approved by the Ethics
Committee of the Second Affiliated Hospital of Zhengzhou University
(approval no. 2022243). Written informed consent for the
acquisition and use of samples was obtained from patients or their
legal guardians, and all experiments were carried out in accordance
with The Declaration of Helsinki and guidelines and regulations of
Zhengzhou University.
Cell transfection
The A549 and SK-MES-1 cells (Procell Life Science
& Technology Co., Ltd.) were divided into BC (blank control
(BC), negative control (NC) and overexpression (OE) groups. All
cells were cultured in RPMI-1640 medium (Gibco; Thermo Fisher
Scientific, Inc.) containing dual antibodies (Gibco; Thermo Fisher
Scientific, Inc.) and fetal bovine serum (Gibco; Thermo Fisher
Scientific, Inc.) at a temperature of 37°C, with 5% CO2
concentration and 75% humidity. The NC and OE groups were
transfected with empty lentivirus and LATS2-overexpression
lentivirus (Guangzhou Ruibo Biotechnology Co., Ltd.), respectively.
At 24 h before cell transfection, the cells were seeded at
1×105 cells/well in a 6-well plate and then transferred
to an antibiotic-free medium. At the beginning of transfection, the
cells were washed and 1 ml serum-free medium was added. Then, 1.6
µg plasmid (2nd generation system; interim cell line, 293T provided
by Procell Life Science & Technology Co., Ltd.;
lentivirus:packaging plasmid:envelope plasmids=4:3:1; MOI=10) and 4
µl the transfection complex composed of Lipofectamine 3000 (Thermo
Fisher Scientific, Inc.) were added. The cells were cultured at
37°C for 6 h, then the medium was changed to a complete medium
containing serum, and the cells were further cultured for 72 h
before the detection was performed.
Quantitative PCR (qPCR)
Cells were collected 72 h post-transfection, and
qPCR was conducted to determine the level of LATS2. The primer
sequences for LATS2 were as follows: Forward,
5′-TGTAGACCGCGCCCCT-3′ and reverse,
5′-TCTTTCCTTCCATTTTTGTAGTTCC-3′. GAPDH was used as the internal
control, with the following primers: Forward,
5′-GGAGCGAGATCCCTCCAAAAT-3′ and reverse,
5′-GGCTGTTGTCATACTTCTCATGG-3′. The qPCR reaction conditions were as
follows: Initial denaturation at 95°C for 5 min, 40 cycles of
denaturation at 95°C for 15 sec and annealing/extension at 60°C for
20 sec (with fluorescence detection). The relative gene expression
levels were calculated using the 2−ΔΔCq method (18).
Western blot
At 72 h post-cell transfection, the culture medium
was discarded. Total protein was extracted using RIPA lysis and
extraction buffer [Thermo Fisher Scientific, Inc.; containing 25 mM
Tris-HCl (pH 7.6), 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate,
0.1% SDS)]. The protein concentration was determined using a BCA
assay (Thermo Fisher Scientific, Inc.), and 30 µg total protein was
loaded per lane. A 10% SDS-PAGE gel was prepared, and a 0.45 µm
PVDF membrane (Beyotime Biotechnology) was transferred at a
constant current of 300 mA for 90 min. The membrane was blocked
with 5% BSA at room temperature for 2 h. Subsequently, the
anti-LATS2 primary antibody (diluted 1:1,000; Abcam; cat. no.
ab243657) or anti-GAPDH primary antibody (1:1,000; Abcam; cat. no.
ab243657) was added and incubated overnight at 4°C. After washing,
a goat anti-rabbit secondary antibody (1:2,000; Abcam; cat. no.
ab6721) was added and incubated at room temperature for 1 h. The
membrane was scanned after developing, and the bands were
quantitatively analyzed using Image J software (version 1.8.0;
National Institutes of Health). GAPDH was used as the internal
reference for normalization.
MTT assay
The A549 or SK-MES-1 cells after 72 h of
transfection were prepared into a suspension with RPMI-1640 medium
(Gibco; Thermo Fisher Scientific, Inc.) containing 10% fetal bovine
serum and inoculated into 96-well plates (200 µl) at
5×104/well. After 24, 48 and 72 h, 20 µl MTT solution
was added to each well. The culture was terminated after 4 h
incubation (37°C; 5% CO2), centrifuged at 440 × g for 5
min at 4°C and the supernatant was discarded. After adding 150 µl
DMSO and oscillating for 10 min, absorbance values were observed at
490 nm wavelength, and cell growth curves were drawn.
Flow cytometry
For cell apoptosis rate determination, cells were
collected after transfection for 72 h and adjusted to
1×105 cells/ml. In a 4°C dark environment, 5 µl
AnnexinV-FITC was added to the cells for a 15 min staining period,
followed by the addition of 5 µl PE staining period for 5 min. The
stained cells were then analyzed using a flow cytometer (CytoFLEX;
Beckman Coulter, Inc.) to determine the cell apoptosis rate.
Flow cytometry was also used to determine the
proportions of CD4+ and CD8+ subsets of
tumor-infiltrating lymphocytes (TILs) in lung cancer tissues: The
tissues were thawed, minced and washed in RPMI-1640 medium
containing 10% FBS, then digested in trypsin digestion solution
(Gibco; Thermo Fisher Scientific, Inc.) at 37°C on a shaker for 4
h. FACS lysis solution (Gibco; Thermo Fisher Scientific, Inc.) was
added to remove red blood cells. Ficoll-Paque PLUS separation
solution (Cytiva) was added to collect mononuclear cells and
prepare single-cell suspensions. A total of 1×106 cells
per tube were collected, and CD45, CD3, CD4, CD8 antibodies (BD
Biosciences; cat. nos. 572781, 572799, 572785 and 572798,
respectively) were added. The tubes were incubated in the dark at
4°C for 30 min and then analyzed via a flow cytometer (BD FACS
Canto, BD Company) to determine the proportions of CD4+
and CD8+ cells and calculate the
CD4+/CD8+ ratio. The gating path (BD FACS
Diva 8.0.2; BD Biosciences) was set as follows: Lymphocyte
population (FSC-A vs. SSC-A) → CD45+ → CD3+ →
CD4+/CD8+.
Wound healing assay
Cells were collected 72 h post-transfection and
seeded into 6-well plates at 5×105 cells per well. After
overnight incubation, a straight line was scratched across the cell
monolayer perpendicularly using a pipette tip. The cells within the
scratch were washed away. Subsequently, serum-free medium was
added, and the cells were cultured at 37°C in a 5% CO2
incubator. Images were captured (Zeiss LSM880) at 0 and 24 h. Lines
were drawn along the cell edges on the images, and the distance
between the two lines was defined as the scratch width (D). The
average of three measurements was taken. The cell migration rate
after 24 h was calculated using the formula: (D0
h-D24 h)/D0 h ×100%.
Transwell assay
The upper surface of the Transwell chambers was
coated with diluted Matrigel at 37°C for 3 h, followed by the
addition of 50 µl serum-free medium containing BSA. Cells collected
72 h after transfection were resuspended in serum-free medium
(Gibco; Thermo Fisher Scientific, Inc.) and adjusted to
1×105 cells/ml. A 100 µl aliquot of the cell suspension
was added to the upper chamber of the Transwell insert, while 500
µl RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Inc.) was
added to the lower chamber. After 24 h incubation, the Matrigel and
the cells in the upper chamber were wiped away. The cells were then
stained with 0.1% crystal violet at room temperature for 15 min and
observed and images captured under a microscope (Olympus GX53). The
average number of cells in five fields of view was calculated.
Statistical analysis
The statistical tools used in the present study
included IBM SPSS 20.0 (IBM Corp.), GraphPad Prism 8 (Dotmatics)
and R 4.4.1. Inter-group comparisons were conducted using unpaired
sample t-tests or paired sample t-tests. Comparisons among multiple
groups were conducted using ANOVA (parametric) or Kruskal-Wallis
test (non-parametric), followed by a Student-Newman-Keuls post hoc
test. The survival time between two groups was compared using
Kaplan-Meier analysis, and differences were evaluated using a
log-rank test (performed using SPSS). Two-step tests were performed
using R 4.4.1 and the ‘survival’ package (19) to compare differences for the
survival curves with a late-stage crossover. Cox multivariate
regression analysis was performed using SPSS, and the differences
were examined using the log-rank method. P<0.05 was considered
to indicate a statistically significant difference.
Results
Low LATS2 expression in LUAD and
LUSC
GSCA database analysis revealed that LATS2
expression was downregulated in LUAD and LUSC compared with normal
tissues (Fig. 1A). Further analysis
of TCGA-LUAD and TCGA-LUSC data also demonstrated that the
expression levels of LATS2 in tumor tissues were significantly
lower than those in normal tissues (all P<0.05; Fig. 1B and C). Furthermore, LATS2
expression in LUAD tumor tissues was higher than that in LUSC
(P<0.05; Fig. 1D).
Immunohistochemical images obtained from the Human Protein Atlas
database indicated that the proportion of LATS2-positive cells was
markedly higher in normal lung tissues compared with LUAD and LUSC
tumor tissues (Fig. 1E).
Association between LATS2 expression
and clinical data
In patients with LUAD, the expression levels of
LATS2 in the stage N1-N3, stage M1 and stage III/IV groups were
significantly higher than those in the N0, M0 and stage I/II
groups, respectively (all P<0.05; Fig. 2A). By contrast, in patients with
LUSC, there were no statistically significant differences in LATS2
expression levels between the T0 and T1-4 groups, N0 and N1-3
groups, M0 and M1 groups or stage I/II and stage III/IV groups (all
P>0.05; Fig. 2B).
 | Figure 2.Correlation between LATS2 expression
and clinical characteristics in LUAD and LUSC. (A) Stratified
analysis of LATS2 expression across T, N and M stage and TNM stage
in LUAD. (B) Stratified analysis of LATS2 expression across T
stage, N stage, M stage and TNM stage in LUSC. All the data are
presented as mean ± SD. *P<0.05. LATS2, large tumor suppressor
kinase 2; LUAD, lung adenocarcinoma; LUSC, lung squamous cell
carcinoma; T, tumor size; N, lymph node metastasis; M, distant
metastasis; TPM, transcripts per million. |
Association between LATS2 expression
and patient survival
The Kaplan-Meier curves demonstrated that both OS
and PFS times were longer in the low LATS2 expression group
compared with the high LATS2 expression group in LUAD (both
P<0.05; Fig. 3A and B). In LUSC,
the OS time was longer in the low LATS2 expression group than in
the high LATS2 expression group (P<0.05; Fig. 3E), but there was no significant
difference in PFS between the two groups (P>0.05; Fig. 3F). The results of univariate and
multivariate Cox analyses demonstrated that in patients with LUAD,
age (≥65 years), T stage (T2-T4), N stage (N1-N3), TNM stage
(III/IV) and high LATS2 expression were independent risk factors
for OS (Table I; Fig. 3C) and age (≥65 years), T stage
(T2-T4), TNM stage (III/IV) and high LATS2 expression were
independent risk factors for PFS (Table
I; Fig. 3D). In patients with
LUSC, LATS2 expression was not an independent influencing factor
for OS or PFS (Table II; Fig. 3G and H).
 | Table I.Multivariate COX regression analysis
of OS and PFS in LUAD. |
Table I.
Multivariate COX regression analysis
of OS and PFS in LUAD.
|
|
| Univariate
analyses | Multivariate
analysis |
|---|
|
|
|
|
|
|---|
| Survival | Variables | P-value | HR (95% CI) | P-value | HR (95% CI) |
|---|
| OS | Age (≥65
years) | 0.058 | 1.462
(0.987–2.166) | 0.014 | 1.650
(1.106–2.461) |
|
| Sex (male) | 0.506 | 0.875
(0.590–1.297) | - | - |
|
| Smoking | 0.257 | 1.272
(0.839–1.929) | - | - |
|
| History of
malignant tumors | 0.040 | 1.691
(1.023–2.793) | - | - |
|
| Radiation
treatment | 0.015 | 1.851
(1.128–3.037) | - | - |
|
| Pharmaceutical
treatment | 0.622 | 1.112
(0.730–1.694) | - | - |
|
| Residual tumor | 0.010 | 2.795
(1.537–5.083) | - | - |
|
| T stage
(T2-T4) | 0.005 | 2.037
(1.247–3.329) | 0.031 | 1.741
(1.053–2.877) |
|
| N stage
(N1-N3) | 0.000 | 2.725
(1.843–4.028) | 0.002 | 2.054
(1.292–3.266) |
|
| M stage (M1) | 0.100 | 1.733
(0.900–3.335) | - | - |
|
| TNM stage
(III/IV) | 0.000 | 2.857
(1.911–4.272) | 0.020 | 1.747
(1.091–2.798) |
|
| LATS2 (high) | 0.004 | 1.805
(1.213–2.687) | 0.014 | 1.663
(1.109–2.496) |
| PFS | Age (≥65
years) | 0.006 | 1.643
(1.153–2.341) | 0.007 | 1.633
(1.144–2.330) |
|
| Sex (male) | 0.700 | 1.070
(0.757–1.513) | - | - |
|
| Smoking | 0.870 | 1.043
(0.633–1.716) | - | - |
|
| History of
malignant tumors | 0.613 | 1.116
(0.729–1.708) | - | - |
|
| Radiation
treatment | 0.596 | 1.097
(0.77–1.548) | - | - |
|
| Pharmaceutical
treatment | 0.419 | 0.864
(0.60–1.232) | - | - |
|
| Residual tumor | 0.005 | 0.464
(0.270–0.798) | - | - |
|
| T stage
(T2-T4) | 0.000 | 2.063
(1.384–3.074) | 0.002 | 1.931
(1.283–2.908) |
|
| N stage
(N1-N3) | 0.003 | 1.702
(1.199–2.415) | - | - |
|
| M stage (M1) | 0.142 | 1.772
(0.826–3.804) | - | - |
|
| TNM stage
(III/IV) | 0.001 | 2.047
(1.358–3.085) | 0.014 | 1.702
(1.115–2.597) |
|
| LATS2 (high) | 0.028 | 1.473
(1.043–2.080) | 0.042 | 1.438
(1.014–2.040) |
| Table II.Multivariate COX regression analysis
of OS and PFS in lung squamous cell carcinoma. |
Table II.
Multivariate COX regression analysis
of OS and PFS in lung squamous cell carcinoma.
|
|
| Univariate
analyses | Multivariate
analysis |
|---|
|
|
|
|
|
|---|
| Survival | Variables | P-value | HR (95% CI) | P-value | HR (95% CI) |
|---|
| OS | Age (≥65
years) | 0.049 | 1.381
(1.001–1.904) | 0.004 | 1.615
(1.167–2.235) |
|
| Sex (male) | 0.500 | 0.891
(0.637–1.246) | - | - |
|
| Smoking | 0.146 | 1.272
(0.839–1.929) | - | - |
|
| Radiation
treatment | 0.000 | 0.349
(0.259–0.470) | <0.001 | 0.345
(0.255–0.466) |
|
| Pharmaceutical
treatment | 0.000 | 0.530
(0.391–0.718) | - | - |
|
| Residual tumor | 0.020 | 1.890
(1.105–3.235) | - | - |
|
| T stage
(T2-T4) | 0.001 | 1.727
(1.237–2.411) | 0.011 | 1.548
(1.104–2.170) |
|
| N stage
(N1-N3) | 0.265 | 1.182
(0.881–1.585) | - | - |
|
| M stage (M1) | 0.115 | 2.227
(0.824–6.022) | - | - |
|
| TNM stage
(III/IV) | 0.003 | 1.639
(1.180–2.276) | - | - |
|
| LATS2 (high) | 0.062 | 1.313
(0.986–1.748) | - | - |
| PFS | Age (≥65
years) | 0.463 | 1.168
(0.771–1.769) | - | - |
|
| Sex (male) | 0.016 | 0.560
(0.350–0.897) | - | - |
|
| Smoking | 0.007 | 1.726
(1.163–2.561) | 0.007 | 1.767
(1.170–2.669) |
|
| Radiation
treatment | 0.002 | 0.462
(0.281–0.759) | - | - |
|
| Pharmaceutical
treatment | 0.866 | 0.966
(0.645–1.446) | - | - |
|
| Residual tumor | 0.000 | 3.490
(1.813–6.717) | 0.001 |
3.227(1.676–6.407) |
|
| T stage
(T2-T4) | 0.000 | 2.581
(1.663–4.007) | <0.001 | 2.320
(1.457–3.693) |
|
| N stage
(N1-N3) | 0.050 | 1.469
(0.999–2.160) | - | - |
|
| M stage (M1) | 0.009 | 3.870
(1.411–10.617) | 0.013 | 3.637
(1.309–10.103) |
|
| TNM stage
(III/IV) | 0.000 | 2.164
(1.404–3.336) | - | - |
|
| LATS2 (high) | 0.104 | 1.374
(0.937–2.016) | - | - |
Analysis of LATS2-related genes
The process of screening out genes related to LATS2
for GO and KEGG analysis is shown in Fig. 4A. The results of KEGG and GO
enrichment analyses revealed that in LUAD, LATS2-related genes were
mainly involved in biological processes such as ‘cell
morphogenesis’, ‘negative regulation of cellular component
organization’, ‘cell-cell junction’, ‘histone modifying activity’
and ‘cadherin binding’ (Fig. 4B),
as well as pathways such as the ‘Hippo signaling pathway
(hsa04392)’ and ‘pathways in cancer (hsa05200)’ (Fig. 4C). In LUSC, LATS2-related genes were
primarily associated with functions such as ‘positive regulation of
cell adhesion’, ‘negative regulation of cell population
proliferation’, ‘extracellular matrix’, ‘calcium ion binding’ and
‘enzyme activator activity’ (Fig.
4B), along with pathways such as ‘ECM-receptor interaction
(hsa04512)’, ‘MAPK signaling pathway (hsa04010)’ and ‘Th17 cell
differentiation (hsa04659)’ (Fig.
4C).
 | Figure 4.KEGG and GO analysis of
LATS2-correlated genes. (A) Workflow for identifying
LATS2-correlated genes prior to GO and KEGG enrichment analysis.
(B) GO enrichment analysis revealing biological processes, cellular
components and molecular functions associated with LATS2-associated
genes in LUAD/LUSC. (C) KEGG pathway analysis identifying signaling
pathways enriched in LATS2-correlated genes across LUAD/LUSC.
LATS2, large tumor suppressor kinase 2; LUAD, lung adenocarcinoma;
LUSC, lung squamous cell carcinoma; KEGG, Kyoto Encyclopedia of
Genes and Genomes; GO, Gene Ontology; BP, biological
process; CC, cellular component; MF, molecular function; ECM,
extracellular matrix. |
Immune infiltration analysis of
LATS2
The results of CIBERSORT algorithm analysis in the R
program revealed that the high LATS2 expression group exhibited
significantly higher infiltration levels of immune cell subsets
such as T cell CD4 memory resting, natural killer (NK) cell
resting, monocytes and macrophages M2, whereas the levels of cell
subsets such as plasma cells, T cells CD8, T cell regulatory, T
cell follicular helper and NK cell activated were significantly
lower (all P<0.05; Fig. 5A).
In LUSC, the high LATS2 expression group exhibited
significantly higher infiltration levels of immune cell subsets
such as T cell CD4 memory resting, and macrophages M0 and M2, while
the levels of cell subsets such as plasma cells, T cell CD4 memory
activated and T cells follicular helper were significantly lower in
the high LATS2 expression group (Fig.
5B). The results of the GSCA database analysis showed that
LATS2 expression was not correlated with CD4+ T-cell
infiltration (Fig. 5C) but was
negatively correlated with CD8+ T-cell infiltration in
LUAD (Fig. 5D). But in LUSC, LATS2
expression was positively correlated with the infiltration levels
of CD4+ T cells (Fig.
5E) but was not correlated with CD8+ T-cell
infiltration (Fig. 5F).
The data from the 42 patients included in the
present study revealed that the proportion of CD8+ cells
in patients with LUAD was lower (P=0.041) and the level of LATS2
was higher (P=0.042) compared with patients with LUSC (Fig. 6A-C). Correlation analysis revealed
that, in LUAD, LATS2 was positively correlated with the proportion
of CD4+ TILs and the CD4+/CD8+
ratio and negatively correlated with the proportion of
CD8+ TILs. In LUSC, LATS2 expression levels exhibited no
correlation with CD4+ TIL proportions, CD8+
TILs or the CD4+/CD8+ ratio (Fig. 6D). Based on the aforementioned
results, it can be concluded that LATS2 expression exhibited a
negative correlation with CD8+ TILs in LUAD, while it
exhibited a positive but weak correlation with CD4+ TILs
in LUSC.
Effects of LATS2 overexpression on the
biological behaviors of LUAD and LUSC cells
To further investigate the role of LATS2 in LUAD and
LUSC, the expression levels of LATS2 in the Beas-2B lung
epithelial, the A549 LUAD and the SK-MES-1 LUSC cell lines were
assessed. The results demonstrated that compared with Beas-2B
cells, the expression levels of LATS2 were significantly reduced in
both A549 and SK-MES-1 cells (P<0.001; Fig. 7A). After transfection with LATS2 OE
or NC lentivirus (Fig. 7D), there
was a significant increase in LATS2 expression in both A549 and
SK-MES-1 cells in the OE group compared with that in the BC group
(all P<0.05; Fig. 7B), while no
significant change was observed in the NC group (P>0.05;
Fig. 7B). The changes in
proliferation, apoptosis, migration and invasion of A549 and
SK-MES-1 cells were subsequently assessed. After overexpression of
LATS2, the proliferative activity of A549 cells in the LATS2 OE
group was markedly decreased (P<0.001, Fig. 7C), the apoptosis rate was increased
(P<0.001, Fig. 7E) and both the
migration rate (Fig. 8A, B and D)
and the number of invading cells were significantly reduced (all
P<0.001; Fig. 8C and E). Similar
changes were observed in SK-MES-1 cells. However, the SK-MES-1 OE
group exhibited significantly higher proliferative activity, a
lower apoptosis rate and a significantly higher migration rate and
number of invading cells compared with those in the A549 OE group
(all P<0.05). The overall changes in proliferation, apoptosis,
migration and invasion were more pronounced in A549 cells than in
SK-MES-1 cells following LATS2 overexpression.
 | Figure 7.Functional impact of LATS2
upregulation on LUAD/LUSC cell viability and apoptosis. (A)
Baseline LATS2 expression comparison in A549 (LUAD), SK-MES-1
(LUSC) and Beas-2B (normal bronchial) cell lines. (B) Western blot
validation of LATS2 protein overexpression post-transfection. (C)
Transfection efficiency confirmation via fluorescence labeling
(MOI=10; transfection rate=90.5 and 90% in LUAD and LUSC,
respectively). Scale bar, 100 µm. (D) qPCR quantification of LATS2
mRNA induction post-transfection. (E) MTT assay evaluation of cell
proliferation changes following LATS2 upregulation. (F) Flow
cytometry analysis of apoptosis rate alterations after LATS2
upregulation. Data are presented as mean ± SD and all comparisons
were conducted using independent samples t-test. *P<0.05,
**P<0.01 and ***P<0.001. LATS2, large tumor suppressor kinase
2; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma;
BC, blank control; NC, negative control; OE, overexpression; OD,
optical density; MOI, multiplicity of infection; qPCR, quantitative
PCR. |
Discussion
The Hippo signaling pathway is a kinase cascade
system that serves a core role in cell proliferation, apoptosis,
organ size and tissue regeneration (20), and has been demonstrated to be
closely related to the occurrence and development of lung cancer.
LATS2 is one of the core components of the Hippo signaling pathway
(21,22). Studies have shown that the
inactivation or functional deficiency of LATS1/2 in the Hippo
signaling pathway can lead to the dephosphorylation and nuclear
translocation of YAP/TAZ, which then binds to TEAD transcription
factors, promoting the proliferation, invasion and metastasis of
lung cancer cells (23,24). The present study aimed to
systematically explore the tumor-suppressing role of LATS2 in lung
cancer, its relevance for patient prognosis, and whether its
expression and functional roles are the same in different
pathological types of lung cancer. The results indicated that LATS2
was generally expressed at a low level in the tumors analyzed in
the present study, including LUAD and LUSC, which is consistent
with most previous studies (24,25).
For instance, in one study, Malik et al (24) observed low LATS2 expression in
66.66% of NSCLC tumor tissues. Matsuda et al (25) confirmed that the positive expression
rate of LATS2 was markedly lower in prostate cancer tissues than in
non-cancerous tissues. However, Zhang et al (26) found that LATS2 was positively
expressed in >80% of nasopharyngeal carcinomas. The authors also
revealed that, among patients with LUAD, those with larger tumor
sizes (≥3 cm), lymph node metastasis, distant metastasis and higher
TNM stages had higher LATS2 expression levels. Furthermore,
elevated LATS2 expression was an independent risk factor for OS and
PFS in these patients. By contrast, in LUSC, LATS2 expression
exhibited no significant association with clinical data; although
patients in the low LATS2 expression group had a longer OS time, it
was not an independent indicator. Therefore, the relationship
between LATS2 and LUAD in the present study is inconsistent with
that reported in existing studies. Matsuda et al (25) also confirmed that patients with
prostate cancer with negative expression of LATS2 have a
significantly higher risk of biochemical recurrence compared with
those with positive expression. Sun et al (27) also demonstrated that LATS2
expression was negatively associated with OS in gastric cancer.
In the present study, the bioinformatics analysis
based on TCGA data revealed that LATS2 was expressed at a low level
in LUAD and LUSC, but was a risk factor for patient survival,
raising doubts on the tumor suppressive role of LATS2. Therefore,
in vitro basic experiments were conducted to clarify this.
The results demonstrated that LATS2 expression was notably lower in
LUAD and LUSC cell lines than in normal lung epithelial cells.
Overexpression of LATS2 notably inhibited the proliferation,
migration and invasion of A549 and SK-MES-1 cells, and promoted
apoptosis. Wu et al (9)
found that patients with low LATS2 expression had a significantly
shorter OS time than those with high LATS2 expression, making it an
independent prognostic indicator for patients with NSCLC, and
overexpression of LATS2 could inhibit the migration of A549 and
H1299 cells. When comparing the present study with the study by Wu
et al (9), the common
conclusion is the observation that upregulation of LATS2 expression
markedly inhibits malignant behaviors of tumor cells. The
difference is that Wu et al (9) considered LATS2 to be positively
associated with prognosis, while the present results demonstrated
that high LATS2 expression was associated with worse survival. The
main reasons for the differences in these conclusions may be sample
size and histological subtypes. The study by Wu et al
(9) had a relatively small sample
size (73 cases) across all NSCLC subtypes, while the present study
used TCGA data for >500 LUAD or LUSC samples and focused
exclusively on either LUAD or LUSC as distinct histological
subtypes. The results of the in vitro experiments are
generally consistent with existing cell biology research results in
lung cancer cells (28,29), as well as hepatocellular carcinoma
cells (30) and glioma cells
(31). Therefore, the conclusion is
that, although the low expression of LATS2 in lung cancer and its
role as a risk factor for lung cancer appear contradictory, its
inhibitory effect on the growth of lung cancer cells is clear.
Thus, the mechanism by which its high expression becomes a risk
factor requires further exploration.
It is worth noting that the present study also
revealed that, compared with those in A549 cells, the effects of
LATS2 overexpression on SK-MES-1 cells were less pronounced,
suggesting that although LATS2 exerted tumor-suppressing effects in
both LUAD and LUSC, there may be different molecular mechanisms or
pathways involved. Other research results in the present study also
supported the differences in the roles of LATS2 in LUAD and LUSC.
GO analysis results showed that LATS2-related genes in LUAD were
involved in the biological process of negative regulation of cell
migration, consistent with the results of cell experiments showing
that LATS2 overexpression could inhibit the migration of A549
cells. By contrast, in LUSC, LATS2-related genes were involved in
the biological processes of positive regulation of cell adhesion
and negative regulation of cell motility, corresponding to the less
pronounced inhibitory effect of LATS2 overexpression on cell
migration and invasion. KEGG analysis results showed that
LATS2-related genes in LUAD were involved in pathways in cancer,
but no such involvement was found in LUSC.
For the analysis of immune infiltration, three
methods were employed: GSCA online analysis, the CIBERSORT
algorithm within the R program and actual clinical data analysis.
Overall, in LUAD, LATS2 expression was negatively correlated with
CD8+ TIL levels, whereas in LUSC, LATS2 expression was
positively correlated with CD4+ TIL levels, however the
correlation was relatively weak. CD8+ T cells serve
important roles in the tumor immune microenvironment (32). After tumor occurrence, tumor cells,
as foreign bodies, activate the immune system. Under normal
circumstances, the levels of both CD4+ and
CD8+ TILs increase (33); CD8+ T cells are the
direct effector cells that mediate tumor killing (34), whereas CD4+ T cells
generally assist in making the immune response as persistent as
possible (35). However, given a
certain level of T cells, an increase in CD4+ T cells
indicates a decrease in CD8+ T cell levels. The
association between CD4+ or CD8+ TILs and the
prognosis of patients with cancer has previously been suggested.
Giatromanolaki et al (36)
reported that elevated ratios of CD4+ and
CD8+ TILs in the tumor stroma predicted worse and
positive prognoses, respectively, and that a higher
CD4+/CD8+ ratio was an independent risk
factor for worse survival. Tao and Xie (37) found that, compared with patients
with low CD8+ T lymphocyte infiltration, those with high
CD8+ T lymphocyte infiltration had longer disease-free
survival and OS times. The results of the present study revealed
that the correlation between LATS2 expression and the infiltration
levels of CD8+ T cells was significantly stronger in
LUAD than in LUSC. Therefore, it can be hypothesized that in LUAD,
the positive association between high LATS2 expression and larger
tumor volume, lymph node metastasis, distant metastasis and worse
survival is due to LATS2 decreasing the levels of CD8+
TILs via pathways such as by activating YAP/TAZ to recruit
regulatory T cells (immune-suppressive cells), thereby weakening
the antitumor immune response, promoting tumor progression and
metastasis, and thus, affecting the survival of patients. In LUSC,
the association between LATS2 expression and immune infiltration is
weaker, its inhibitory effect on cell migration and invasion is not
as obvious as in LUAD and the prognostic significance is also
relatively limited. To confirm this conclusion, it is necessary to
clarify the detailed underlying mechanism of the role of LATS2 in
tumor immunity.
In summary, by integrating bioinformatics analysis
and in vitro experiments, the present study demonstrated
that LATS2 expression was generally low in both LUAD and LUSC.
LATS2 served a tumor-suppressive role in cancer cells, and this
effect was stronger in LUAD than LUSC. In LUAD, LATS2 served as an
independent risk factor for both OS and PFS, likely due to its
correlation with CD4+ and CD8+ TILs that
modulate tumor immune processes. Conversely, in LUSC, LATS2
expression exhibited weak associations with patient survival
outcomes and CD4+ and CD8+ TIL levels. These
findings provide a theoretical basis for a deeper understanding of
the role of LATS2 in lung cancer. The present study also has some
limitations. For instance, due to budget constraints, in
vitro LATS2 knockdown experiments were not performed, and the
correlation between LATS2 expression levels and T cell exhaustion
markers was not examined. In future research, these gaps can be
addressed by conducting LATS2 knockdown experiments in lung cancer
cell lines with relatively high LATS2 expression, and investigating
the in vivo correlation between LATS2 expression levels and
genes such as programmed cell death protein-1 and cell
immunoglobulin and mucin domain-containing protein-3 to further
elucidate the mechanistic role of LATS2 in the development and
progression of LUAD and LUSC. At the same time, relevant research
should be performed to determine the critical points for moderate
enhancement (such as gene therapy) and excessive expression (such
as autonomous activation of tumor cells) of LATS2 to avoid the
‘dose-effect’ trap in treatment. Simultaneously, the establishment
of subtype-specific immune microenvironment regulation networks
should be explored for differences in the role of LATS2 in
different subtypes to formulate more precise targeted treatment
strategies.
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
The data generated in the present study may be
requested from the corresponding author.
Authors' contributions
XZ and ZZ designed the research approach, XZ was
responsible for coordinating and supporting project resources; ZZ
and YX conducted the foundational experiments, CZ oversaw clinical
data acquisition; SZ performed the bioinformatics analysis and the
visualization of data; CZ, ZZ and SZ performed all data analysis
and result interpretation and confirm the authenticity of all the
raw data. ZZ completed the drafting and language translation of the
initial manuscript, and all authors read and approved the final
version of the manuscript.
Ethics approval and consent to
participate
The present study was approved by the Ethics
Committee of the Second Affiliated Hospital of Zhengzhou University
(approval no. 2022243). Written informed consent for the
acquisition and use of samples was obtained from patients or their
legal guardians, and all experiments were carried out in accordance
with The Declaration of Helsinki and guidelines and regulations of
Zhengzhou University.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
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