Abstract
Chromatin regulators drive cancer epigenetic changes, and lncRNA can
play an important role in epigenetic changes as chromatin regulators.
We used univariate Cox, LASSO, and multivariate Cox regression analysis
to select epigenetic-associated lncRNA signatures. Twenty-five
epigenetic-associated lncRNA signatures (CELncSig) were identified to
establish the immune prognostic model. According to Kaplan-Meier
analysis, the overall survival of the high-risk group was significantly
lower than the low-risk group. Receiver operating characteristic (ROC)
curves, C-index, survival curve, nomogram, and principal component
analysis (PCA) were performed to validate the risk model. In GO/KEGG
analysis, differentially expressed lncRNAs were correlated with the
PI3K−Akt pathway, suggesting that they were highly associated with the
metastasis of LUAD. Interestingly, in the immune escape analysis, the
TIDE score was lower, and the possibility of immune dysfunction is also
slighter in the high-risk group, which means they still have the
potential to receive immunotherapy. And CELncsig is highly correlated
with immune pathways T_cell_co-inhibition and Check-point. Also, the
IMvigor210 cohort analysis indicated that our risk-scoring model has
significant potential clinical application value in lung cancer
immunotherapy. And we also screened out ten potential chemotherapy
agents using the ‘pRRophetic’ package.
Introduction
Today, lung cancer remains the leading cause of cancer-related
mortality worldwide. A study examining 36 cancer types in 185 countries
reported 2.2 million new lung cancer cases in 2020, making it the
second most common cancer diagnosis. Concurrently, approximately 1.8
million individuals succumbed to lung cancer [[32]1]. Non-small cell
lung carcinoma (NSCLC) is a subtype of lung cancer with a poor
prognosis, boasting a mere 19% five-year survival rate [[33]2]. Lung
adenocarcinoma (LUAD) is a subtype of NSCLC. Most LUAD primarily
originates in the external area of the lung and tends to spread to
lymph nodes and peripheral tissues [[34]3]. Epigenetic changes to
tumor-associated genes are common in LUAD, especially the inactivation
of tumor suppressor genes [[35]4].
Long non-coding RNAs (lncRNAs) are non-coding RNAs consisting of
various RNAs that transcribe over 200 nucleotides in length and lack
protein-coding potential. LncRNAs are more active than mRNA in biology,
and surface lncRNAs play an essential role in biology [[36]5]. One of
the critical factors affecting cancer is the change in the cancer
microenvironment, and the complex and dynamic environment around the
tumor is called the tumor microenvironment (TME) [[37]6]. There is
considerable evidence that immune-related long non-coding RNAs play an
essential role in the TME and have significant potential in immune
regulation [[38]7, [39]8]. LncRNAs are integral to the genomic
regulatory network, particularly in transcriptomics. Compared to normal
or adjacent cancerous tissues, numerous differentially expressed
lncRNAs can be identified within tumor samples, which can be utilized
for subtype classification and prognostic prediction [[40]9]. Thus,
investigating lncRNAs associated with epigenetic regulatory factors is
essential for predicting tumor immunotherapy outcomes and developing
prognostic models.
According to a 2018 publication, cancer arises from a malignant
transformation driven by the accumulation of body-acquired inheritance
and epigenetic aberrations [[41]10]. In epigenetics, chromatin
regulators (CRs) are usually divided into three parts, DNA methylation
factors, histone modification, and chromatin remodeling factors.
Changes in the expression of a single CR may affect the function of one
or more components and potentially profoundly affect gene expression
[[42]11]. Presently, research on chromatin epigenetic-associated
lncRNAs in lung cancer remains limited. As such, establishing a model
of chromatin epigenetic-related lncRNA signature (CELncSig) is of great
importance for predicting tumor prognosis.
In this study, we constructed the CELncSig prognostic model and
explored its role in immune function and immunotherapy. The detailed
methodology is illustrated in [43]Fig 1.
Fig 1. Flowchart of the research process.
[44]Fig 1
[45]Open in a new tab
This includes data collection, identification of chromatin
epigenetic-related lncRNA signatures, clinical data validation,
prognostic prediction, and immunotherapy prediction.
Materials and methods
Ethical approval and consent to participate
The work was approved by the Guangdong Medical University Ethical
Committee (YS2021159). Informed consent forms are not required for
patient data extracted from public databases.
The data source
We downloaded RNA sequence transcription data (FPKM), RNA transcription
data (n = 19508), and lncRNA transcription data (n = 13481) from the
TCGA website ([46]https://portal.gdc.cancer.gov/). The Cancer Genome
Atlas (TCGA) database is a comprehensive collection of genomic,
epigenomic, and clinical data from cancer patients [[47]12, [48]13]. In
addition, we also obtained relevant clinical samples of lung cancer,
including the following information: age, sex, tumor grade, TNM, stage,
survival time, and survival status. “TNM” stands for primary tumor (T),
regional lymph nodes (N), and distant metastasis (M). Clinical examples
that did not meet the following conditions were removed: 1. non-primary
tumor; 2. data unrelated to survival indicators (survival status and
survival time) were not included. 302 LUAD clinical samples were
formally included ([49]S1 File). 8 LUAD samples were removed from the
analysis due to missing expression data files. Finally, we selected 294
LUAD samples and randomly divided them into a training cohort (n = 194)
and a validation cohort (n = 100). The correlation test of pathological
features is shown in S1 Table in [50]S3 File. Due to the retrospective
design of the study and the anonymous analysis of patients’ data,
obtaining informed consent was waived.
Epigenetic changes are driven by chromatin regulators (CRs). FACER
(Functional Atlas of Chromatin Epigenetic Regulators) is an approach to
prioritize the role of functional chromatin regulators in cancer. This
method screened applicable CRs from 10,969 tumors of 33 cancers. Our
genetic base is the sifting associated with lung cancer 225 functional
CRs [[51]10]. The 225 functional CRs in the gene set include 32
cancer-common CRs and 193 cancer-specific CRs ([52]S2 File). These
functional CRs play vital roles in histone modification, chromatin
remodeling, and DNA methylation [[53]10].
Establishment of the risk model
The Pearson correlation analysis (| cor | > = 0.4 and P< 0.001) was
used to extract the mRNA expression of chromatin epigenetic regulators
in LUAD, and the lncRNAs co-expressed with gene sets were identified by
the ’LIMMA’ package in R (version 4.1.3) and correlation test [[54]14].
To find lncRNAs associated with prognosis, a univariate Cox regression
analysis was performed (significance filter criteria P-value<0.05).
LASSO regression analysis was used to further reduce the independent
variables required by the model and prevent over-amplification of
prognostic features. Multivariate Cox regression analysis was performed
to establish a meaningful prognostic model. Finally, 25 selected
CELncSigs were used to construct the immunodiagnostic lncRNA model
(significance filtering criteria P-value<0.05) [[55]15]. We created a
method for calculating individual risk scores based on chromatin
epigenetic associated lncRNA (CELncSig) expression as our risk scoring
formula:
[MATH: CELncSig=∑i=1nCoefi×
exprlncRNAn :MATH]
Coef[i] is the regression coefficient in multivariate Cox regression,
and
[MATH: exprlncRNAn :MATH]
is the expression level of lncRNA. Patients with high-risk scores had
poorer expected survival [[56]16, [57]17]. We used the median risk
score as a cutoff to divide LUAD patients into high-risk and low-risk
groups.
The predictive ability of the CELncSig prognostic risk model and validation
of the model
Meanwhile, we used a heatmap to represent the relationship between the
model-related lncRNAs and the risk score. Subsequently, univariate and
multivariate Cox regression analyses were performed using
clinical-grade lung cancer data (age, sex, etc.). The purpose of the
Cox analysis is to evaluate the impact of several factors on survival
simultaneously.
In the evaluation of the model, we used ROC (Receiver operating
characteristic) analysis to evaluate the quality of the patient’s
clinically independent prognostic model. Furthermore, we used clinical
C-index curves to further evaluate the quality of patient-independent
prognostic models. Finally, we verify our model by using a nomogram and
calibration curve. The nomogram can be applied to the graphic
calculation of complex formulas with practical accuracy.
Validation of clinical grouping data and assessment of patient
differentiation
The ‘survival’ package in R (version 4.1.3) was used to verify the
grouped data of clinical data. The clinical indicators included stage,
age, gender, survival state (fustat), race, AJCC (The American Joint
Committee on Cancer) stages, primary tumor (T), regional lymph nodes
(N), and distant metastasis (M). Kaplan-Meier analysis was used to
analyze the survival parameters of risk groups. Finally, we used
three-dimensional principal component analysis (PCA) to analyze whether
there was a differentiation between coding genes, non-coding genes, and
all genes in the high and low-risk groups.
GO/KEGG pathway enrichment analysis of the risk model
We brought the LUAD-related lncRNAs into the high/low-risk model and
used the ‘LIMMA’ package in R (version 4.1.3) for differential
expression analysis. We also used the ‘clusterProfiler’ package in R to
perform GO (Gene Ontology) function annotation for 448 differentially
expressed lncRNAs. FDR of 0.05 was considered statistically
significant. We also used bar charts to specifically display
differentiated lncRNAs related to cell components (CC), molecular
functions (MF), and biological processes (BP). Meanwhile, we performed
the KEGG (Kyoto Encyclopedia of Genes and Genomes Enrichment Analyses)
pathway analysis.
Analysis of immune function and tumor mutation burden in risk groups
Based on specific immune genes, immune gene function can be classified
into 13 types of immune events [[58]18, [59]19]. To determine the
scores of high and low-risk groups in these 13 immune function
pathways, we utilized single-sample gene enrichment analysis (ssGSEA)
[[60]19, [61]20]. Recent studies have established a close association
between tumor mutation burden and the efficacy of anti-programmed cell
death ligand 1 (anti-PD-L1) therapy in several types of cancer [[62]21,
[63]22].
Subsequently, we employed the ’LIMMA’ package in R (version 4.1.3) to
analyze the difference in tumor mutation burden between the high and
low-risk groups of LUAD. Furthermore, we utilized the ’survival’
package to evaluate the overall survival rate between groups with high
and low tumor mutation burden.
LUAD immune escape analysis and identification of potential therapeutic drugs
Tumor immune dysfunction and exclude files (TIDE) score from the TIDE
website ([64]http://tide.dfci.harvard.edu). The immune biomarker
Interferon-gamma (IFNG) has been found to play a critical role in both
innate and adaptive immune responses, while at the same time,
T-cell-inflamed signature (Merck18) can contribute to T-cell
dysfunction, demonstrating the important roles of a series of immune
checkpoint biomarkers in cancer immune mechanisms [[65]23, [66]24]. We
compared the model high-low risk groups with eleven known tumor immune
markers, including Microsatellite Instability Score (MSI) [[67]25,
[68]26], the cluster of differentiation 8 (CD8), the cluster of
differentiation 274 (CD274) [[69]26–[70]28], Dysfunction, Exclusion,
tumor-associated macrophages M2 (TAMM2) [[71]29], Myeloid-derived
suppressor cell (MDSC) [[72]30], Cancer-associated fibroblasts (CAFs),
Merck18, IFNG to analyze the role of high and low-risk groups in tumor
immune escape and prediction model in the effect of immunotherapy
[[73]31].
After utilizing the ‘pRRophetic’ package to identify potential
therapeutic agents for LUAD, we quantified the sensitivity of high and
low-risk groups to chemical agents by using IC50 as a measure of drug
inhibition [[74]32]. The ‘pRRophetic’ package has the capability to
predict clinical chemotherapeutic response through the utilization of
tumor gene expression data [[75]22].
Prediction of immunotherapy effect and analysis of immunotherapy response of
the model
IMvigor210 cohort data were downloaded from the website
([76]https://clinicaltrials.gov/ct2/show/NCT02108652). Atezolizumab is
an anti-PD-L1 antibody immunotherapy agent and can be used as a
co-immune checkpoint inhibitor in the treatment of advanced or
metastatic bladder cancer [[77]19]. Programmed cell death ligand 1
(PD-L1) related genes are also expressed in LUAD. Therefore, the
immunotherapy result of the IMvigor210 cohort can be used to verify the
immunotherapy prediction ability of the risk model [[78]19, [79]33]. We
combined lncRNAs from the LASSO regression analysis with genes from the
IMvigor210 cohort to create a multivariate Cox risk model. And plot the
survival curves of high and low-risk groups. ROC curves were used to
evaluate the model’s predictive ability.
Stemness Indices is an index that describes the similarity of cancer
cells to stem cells and can be used as a prognostic indicator to
predict the risk of tumor recurrence and guide treatment [[80]34]. By
using a one-class logistic regression (OCLR) algorithm to train the
tumor stem cell (ESC, embryonic stem cell; iPSC, induced pluripotent
stem cell) classes and their differentiated ectoderm, mesoderm, and
endoderm progenitors, then applied to The Cancer Genome Atlas (TCGA)
dataset to calculate the mRNA gene expression-based stemness index
(mRNAsi) [[81]35]. Our index of mRNA expression of lung cancer stem
cells is based on the available data [[82]34]. We combined lung cancer
clinical data and lung cancer stem cell data and used the ‘survival’
package in R (version 4.1.3) for survival analysis of lung cancer
mRNAsi. Finally, we further studied the clinical relevance of mRNAsi.
Result
Identification of co-expressed lncRNAs in FACER gene sets
We extracted 225 Facer-related genes from the database and extracted
the mRNA expression levels of the gene sets. Then, we further extracted
3186 lncRNAs co-expressed with the gene set by Pearson correlation
analysis (corFilter = 0.4 and pvalueFilter = 0.001). We randomly
divided all selective patients (n = 294) into a training cohort (n =
194) and a validation cohort (n = 100). To verify our grouping, we
analyzed the relationship between patients’ randomization and P value >
0.05, indicating no significant difference between the two groups and
that the grouping results are promising (S1 Table in [83]S3 File).
Establishment of chromatin epigenetic-related lncRNAs prognostic model
We further combined gene-set-related lncRNAs and patient survival data,
conducted a univariate Cox analysis on gene-set-related lncRNAs, and
identified 206 lncRNAs associated with cancer prognosis and survival.
To reduce the number of independent variables, the LASSO regression
analysis was conducted first, then the multivariate Cox analysis.
Finally, we identified 25 lncRNAs significantly associated with lung
cancer prognosis (S2 Table in [84]S3 File).
Clinical assessment using risk models
We categorized the patients into high- and low-risk groups according to
the median risk score and compared the impact of differential
expression of lncRNAs on overall survival rate. Patients in the
high-risk group had significantly lower overall survival rates than
those in the low-risk group (P< 0.001) ([85]Fig 2). The high risk of
LUAD was associated with [86]AC138965.1 (HR = 3.41, 95% CI: 1.95–5.98,
P<0.01) and [87]AL162632.3 (HR = 3.49, 95% CI: 1.30–9.35, P = 0.01).
Moreover, [88]AC007686.2 was linked to a low risk of LUAD (HR = 0.16,
95% CI: 0.03–0.75, P = 0.002), which is protective in LUAD patients
([89]Fig 2).
Fig 2. Prognostic prediction based on the identified lncRNA signature
(CELncSig).
[90]Fig 2
[91]Open in a new tab
(A) Patients in the low-risk group showed longer overall survival (OS)
in the Kaplan-Meier analysis. (B, C) Survival status and risk scores
for each sample. (D) Heatmap shows the expression of CELncSigs, with
blue indicating low expression and red indicating high expression.
In univariate Cox analysis of clinical indicators, we identified that
stage (HR = 1.552, 95% confidence interval: 1.303–1.849, P< 0.001), T
(HR = 1.481, 95%CI: 1.162–1.888, P = 0.001), N (HR = 1.827, 95%CI:
1.454–2.296, p< 0.001) were associated with the high-risk subgroup,
while no significant results were found in multivariate Cox analysis
(S1 Fig in [92]S3 File). Furthermore, our results showed that patients
with higher risk scores had a greater likelihood of death, and the risk
of death increased from tumor stage I to stage III. Additionally, male
patients had a higher risk of death (S2 Fig in [93]S3 File) (P<0.05).
Evaluation and validation of the risk model
The C-index curve demonstrated that values of various clinical
indicators such as risk score, stage, regional lymph nodes (N), and
primary tumor (T) were greater than 0.5, which further verified the
excellent predictive ability of the model (S3 Fig in [94]S3 File).
Subsequent calibration curve verification revealed that the predicted
values of patients’ 1-year, 3-year, and 5-year survival rates were
close to the ideal curve, indicating that the model plays a crucial
role in predicting patients’ prognosis (S3 Fig in [95]S3 File). The ROC
curve analysis indicated that most indicators in the model, such as
risk, age, stage, T stage, M stage, and N stage, could serve as short-
and long-term prediction indicators, except for age, gender, and race
(S3 Fig in [96]S3 File). The model has good accuracy in prediction in
the short and long term (1-year AUC = 0.797, 3-year AUC = 0.813, 5-year
AUC = 0.830) (S3 Fig in [97]S3 File).
Subsequently, the Kaplan-Meier analysis showed that the overall
survival of the high-risk group was significantly lower than that of
low-risk patients over time (P<0.05) ([98]Fig 3). Additionally,
compared to patients with a low-risk score, patients with stage I-III,
patients of all ages, patients with white or black or African American
race, patients at T1-T3, male and female patients, patients with M0,
and patients with N0-N2 had significantly shorter overall survival
([99]Fig 3). Finally, the results of the principal component analysis
indicate good differentiation (S4 Fig in [100]S3 File).
Fig 3. Validation of the prognostic prediction.
[101]Fig 3
[102]Open in a new tab
(A, B) Calibration curves show the consistency between predicted and
observed OS. (C-N) Kaplan-Meier OS curves of subgroups stratified by
clinical factors, including AJCC stages (C-E), age (F, G), race (H, I),
T stage (J-L), gender (M, N), M stage (O), and N stage (P-R).
GO/KEGG pathway enrichment analysis
We found a significantly different in the high-low risk group (P<
0.05), ‘clusterProfiler’ in R was used to perform functional enrichment
analysis of differentially expressed lncRNA genes in the high-low tumor
risk model by GO and KEGG. GO enrichment results showed that most of
the differentially expressed lncRNAs were related to the movement of
microtubules, including DYNLRB2, CFAP91, and ZMYND10. And most of the
differentially expressed lncRNAs were related to the movement of motile
cilia, including DNAH9, CFAP91, etc. Most differentially expressed
lncRNAs are related to glycosaminoglycan binding, including ZMYND10 and
DNAI2 ([103]Fig 4). KEGG functional enrichment analysis showed that
most of the differentially expressed lncRNAs were associated with
replenishment and coagulation cascades (P< 0.01), such as SERPIND1, C7,
CR2, followed by the interaction with neuroactive ligand-receptor and
PI3K-Akt signaling pathway (P< 0.05) ([104]Fig 4).
Fig 4. GO and KEGG pathway analysis of the differentially expressed lncRNAs
between high- and low-risk groups.
[105]Fig 4
[106]Open in a new tab
(A, B) GO enrichment analysis. (C, D) KEGG enrichment analysis.
Analysis of immune function and TMB
Based on our immune function analysis, in the same immune cluster,
Cylolytic_activity, Inflammation-promoting, T_cell_co-inhibition,
Check-point, and High expression of t_cell_co-stimulation genes were
associated with the low-risk group (P< 0.05), which can be verified in
the results of the validation cohort and all cohort (S5 Fig in [107]S3
File). In the subsequent tumor mutation burden (TMB) analysis, our
results showed that the high/low-risk group was not correlated with the
TMB. However, we found that in the survival analysis, the high-risk
group with the high-tumor mutational burden group had a significantly
worse prognosis compared with the low-risk group with low TMB. The
survival probability was higher in the low-risk group with a high TMB
and in the low-risk group with a low TMB (P< 0.001) (S5 Fig in [108]S3
File).
Immune escape analysis of risk model
To study the immune escape mechanism of LUAD, we focused on evaluating
11 markers related to LUAD immunogenicity. We first focused on
mechanisms of internal tumor escape. There was no significant
difference between immune markers CD274 and CD8 in the two groups
([109]Fig 5). Interestingly, results showed that the TIDE score in the
high-risk group was significantly lower, indicating the tumor immune
escape potential was smaller ([110]Fig 5). In addition, compared with
the low-risk group, the high-risk group is more prone to immune
rejection, but the possibility of immune dysfunction is lower ([111]Fig
5).
Fig 5. Tumor immune escape analysis of the cohorts.
[112]Fig 5
[113]Open in a new tab
Comparison of CD274, CD8, TIDE, Dysfunction, and Exclusion scores
between high- and low-risk groups in the (A-E) training, (F-J)
validation, and (K-O) all cohorts. "*" means P value<0.05, "**" means P
value<0.01, "***" means P value<0.001. "ns" means no significance.
We then investigated the mechanisms of external tumor escape.
Interestingly, interferon scores were higher in the low-risk group,
prompting an increase in interferon may be associated with the low risk
of LUAD ([114]Fig 6). However, MDSC and CAFs ([115]Fig 6) were
associated with the high risk of LUAD.
Fig 6. Tumor immune escape analysis of the cohorts.
[116]Fig 6
[117]Open in a new tab
Comparison of IFNG, MDSC, and CAFs scores between high- and low-risk
groups in the training, validation, and all cohorts. "*" means that P
value<0.05, "**" means that P value<0.01, "***" means that P
value<0.001. "ns" means no significance.
Screening of potential chemotherapy agents
We then used ‘pRRophetic’ in R to screen for chemical drugs and
validated our results with the validation cohort. Our results showed
CMK, Bortezomib, and Bryostatin.1, Docetaxel, Doxorubicin, Elesclomol,
MS275, Mk2206, Methotrexate, and Lenalidomide significantly differ
between the two risk groups ([118]Fig 7). Among them are CMK,
Bortezomib, and Bryostatin.1, Docetaxel, Doxorubicin, Elesclomol, and
MS275 showed higher sensitivity in high-risk groups, suggesting that
these drugs have good efficacy in the treatment of the high-risk group.
Mk2206, Methotrexate ([119]Fig 7), and Lenalidomide were more sensitive
in the low-risk group, and these results were replicated in the
validation cohort and all cohorts (P<0.05).
Fig 7. Prediction of differential chemotherapy drug sensitivity between high-
and low-Risk groups.
[120]Fig 7
[121]Open in a new tab
CMK, Bortezomib, Bryostatin 1, Docetaxel, Doxorubicin, Elesclomol,
MS275, MK2206, Methotrexate, and Lenalidomide showed higher sensitivity
in the low-risk group. CMK, Bortezomib, Bryostatin 1, Docetaxel,
Doxorubicin, Elesclomol, and MS275 showed higher sensitivity in the
high-risk group.
Predicting the effect of risk models on immunotherapy
Our analysis of immunotherapy response revealed significant differences
in the risk scores of LASSO regression target genes among lung cancer
patients with different responses to bladder cancer immunotherapy
(P<0.05). The result indicates that the immunotherapy drug Atezolizumab
has an excellent therapeutic effect in our CELncSig model (S6 Fig in
[122]S3 File).
There was no significant difference in LASSO regression target gene
expression between the high and low-risk IMvigor210 cohort in LUAD
patients with bladder cancer, indicating the prognostic effect of the
IMvigor210 model was poor. However, we found that the target gene of
LASSO regression in lung cancer patients predicted better three-year
and five-year survival in the IMvigor210 cohort (AUC >0.5) (S6 Fig in
[123]S3 File).
Stemness indices analysis
The analysis of stem cell indices revealed no significant effect of
high or low stem cell indices on overall survival (S7 Fig in [124]S3
File). However, there was a significant difference in the stem cell
indices between lung cancer tissues and normal tissues (P<0.05).
Further analysis demonstrated a significant correlation between
clinical indicators and mRNAsi (S7 Fig in [125]S3 File).
Discussion
Over the past decades, various techniques have been employed to treat
NSCLC, including surgery, chemotherapy, radiotherapy, targeted therapy,
and immunotherapy. In immunotherapy, standard protocols emphasize
immune checkpoint inhibitors, such as Keytruda and Opdivo, which target
PD-1 or PD-L1 to activate the patient’s immune system [[126]36].
However, immunotherapy is not suitable for all patients, and the
effectiveness of immunotherapy depends on multiple factors, for
example, the infiltrating of immune cells, the expression level of
immune checkpoint genes, and somatic mutation status [[127]37].
Therefore, it is crucial to develop effective immune characteristic
models based on chromatin epigenetic regulator-related lncRNAs to
improve the prognosis and assist clinicians and researchers in
determining the appropriate immunotherapy approach.
CRs are essential to epigenetics, and tumor mutations are closely
related to epigenetic processes. Our study demonstrates that the
"CELncSig" chromatin epigenetic-related lncRNAs prognostic model is an
independent prognostic factor for lung cancer patients. Through
rigorous statistical analyses, we identified 25 lncRNAs significantly
associated with lung cancer prognosis. The risk model, which
incorporated these lncRNAs, effectively stratified patients into high-
and low-risk groups, with the high-risk group exhibiting significantly
lower overall survival. Univariate Cox analysis revealed that clinical
indicators, such as stage, T stage, and N stage, were associated with
the high-risk subgroup. Furthermore, the model demonstrated excellent
predictive ability, as evidenced by C-index values, calibration curve
verification, and ROC curve analysis. Kaplan-Meier analysis and
principal component analysis further validated the model’s performance.
Thus, the "CELncSig" risk model is a valuable tool in predicting
prognosis for lung cancer patients and could potentially guide
personalized treatment strategies to improve patient outcomes. An
inaccurate prognostic staging system of lung cancer negatively impacts
patient prognosis [[128]38]. Therefore, physicians can estimate
patients’ overall survival using the nomogram.
And [129]AL162632.3 has the highest HR (HR = 3.49) in our model. A
study has found that biomarker based on [130]AL162632.3 may relate to
immune functions such as human leukocyte antigen (HLA) and
Type_II_IFN_Reponse [[131]39]. And previous studies have demonstrated
the crucial role of [132]AL162632.3 in the cuproptosis model,
ferroptosis model, and molecular chaperone model, all of which are
based on LUAD samples [[133]39–[134]41]. This reveals the diverse
functions of [135]AL162632.3 in LUAD. Combining our research, we
believe that [136]AL162632.3 plays a vital role in LUAD, from the
epigenetic level to the molecular regulation level of tumor growth, and
is highly correlated with tumor cell death induction. Therefore,
[137]AL162632.3 may serve as an important biological marker and a
promising therapeutic target for immunotherapy and chemotherapy.
To further study the role of differentially expressed lncRNAs in tumor,
we discovered through GO enrichment analysis that most differentially
expressed lncRNAs were related to microtubule movement, including
DYNLRB2, CFAP91, and ZMYND10. KEGG enrichment analysis revealed that
differentially expressed lncRNAs were related to the PI3K−Akt signaling
pathway. India and Kinetochore Associated Complex Subunit 3 may
facilitate LUAD metastasis by activating the PI3K−Akt signaling
pathway, suggesting that PI3K−Akt has significant research value
[[138]42].
We examined the immune function of the prognostic model and found that
Cylolytic_activity, Inflammation-promoting, T_cell_co-inhibition,
Check-point, and T_cell_co-stimulation high expression of immune
function-related genes were associated with low tumor risk. The
relationship between Checkpoint and the risk model indicated that
CElncSig might serve as a novel immunotherapy biomarker since various
immune therapies have targeted the immune checkpoint of tumors
[[139]43].
Subsequently, we explored the mechanisms of tumor immune escape,
focusing initially on the internal escape mechanisms of tumors. These
escape mechanisms primarily include antigen-presenting ability inside a
tumor, the expression of immune checkpoints (ICPs), and tumor
immunogenicity [[140]31]. Intriguingly, our results contradicted
expectations, as the TIDE score was lower in the high-risk group,
indicating less tumor immune escape potential. Additionally, the
likelihood of immune dysfunction was also lower in the high-risk group.
These findings may explain why immune escape did not occur in the
high-risk group, suggesting that this group still has the potential to
benefit from immunotherapy.
Relevant studies indicate that the external immune escape mechanism
primarily comprises four aspects: leucocyte deficiency, a large number
of immunosuppressive cells, a high concentration of immunosuppressive
cytokines, and increased fibroblasts [[141]31]. Interferon (IFN-γ)
plays a multifaceted role in regulating the microenvironment of LUAD.
However, interferon scores were lower in the high-risk group,
suggesting that a decrease in interferon may be associated with an
elevated risk of lung cancer [[142]44]. We hypothesize that an increase
in interferon contributes to immune escape in the high-risk lung cancer
group.
We identified CMK, Bortezomib, and Bryostatin.1, Docetaxel,
Doxorubicin, Elesclomol, and MS275 as potential therapeutic drugs for
the high-risk group, and Mk2206, Methotrexate, and Lenalidomide as
potential treatments for the low-risk group.
Understanding the differential response to Atezolizumab treatment in
high and low-risk groups will enhance our comprehension of the role of
risk scores in lung cancer. The results demonstrated that high and
low-risk groups significantly differed in treating bladder cancer with
Atezolizumab (P <0.05), indicating that our risk-scoring model holds
considerable potential for clinical application in cancer
immunotherapy.
The mRNA stemness index (mRNAsi) of lung cancer tissue was highly
different from the normal tissue, indicating the potential of mRNAsi in
clinical classification. Also, mRNAsi is closely associated with
various clinical characteristics. Prior studies suggest that altered
expression of mRNAsi in tissue can serve as a biomarker for identifying
differentially expressed genes in cancer prognosis research [[143]35,
[144]45].
We acknowledge that our study has certain limitations, such as the need
to better integrate our model with clinical indicators. Therefore, more
clinical data is required to enhance our prognostic model.
In conclusion, we have established an immune prognostic model based on
chromatin epigenetic-associated lncRNAs signature, which plays a vital
role in predicting the overall survival of patients. The LUAD
subgroups, differentiated according to the prognostic model, display
distinct clinical, tumor immune escape, and biological function
heterogeneity. The model can be utilized to improve patient prognosis,
assess lung cancer stages, and screen chemotherapeutic drugs for lung
cancer, offering significant clinical application value.
Supporting information
S1 File
(XLSX)
[145]Click here for additional data file.^ (27.2KB, xlsx)
S2 File
(XLSX)
[146]Click here for additional data file.^ (23.7KB, xlsx)
S3 File. Contains all the supporting figures and tables.
(DOCX)
[147]Click here for additional data file.^ (1.3MB, docx)
Data Availability
All relevant data are within the paper and its [148]Supporting
Information files.
Funding Statement
This work was supported partly by The National Natural Science
Foundation of China (81541153). The funders had no role in study
design, data collection and analysis, decision to publish, or
preparation of the manuscript.
References