Immune-related LncRNAs scores predicts chemotherapeutic responses and prognosis in cervical cancer patients

Abstract

Background
Long non-coding RNAs (LncRNAs) regulating the immune microenvironment of cancer is a hot spot. But little is known about the influence of the immune-related lncRNA (IRlncRs) on the chemotherapeutic responses and prognosis of cervical cancer (CC) patients. The purpose of the study was to identify an immune-related lncRNAs (IRlncRs)-based model for the prospective prediction of clinical outcomes in CC patients.

Methods
CC patients’ relevant data was acquired from The Cancer Genome Atlas (TCGA). Correlation analysis and Cox regression analyses were applied. A risk score formula was formulated. Prognostic factors were combined into a nomogram, while sensitivity for chemotherapy drugs was analyzed using the OncoPredict algorithm.

Results
Eight optimal IRlncRs(ATP2A1-AS1, LINC01943, AL158166.1, LINC00963, AC009065.8, LIPE-AS1, AC105277.1, AC098613.1.) were incorporated in the IRlncRs model. The overall survival (OS) of the high-risk group of the model was inferior to those in the low-risk group. Further analysis demonstrated this eight-IRlncRs model as a useful prognostic marker. The Nomogram had a concordance index of survival prediction of 0.763(95% CI 0.746–0.780) and more robust predictive accuracy. Furthermore, patients in the low-risk group were found to be more sensitive to chemotherapy, including Paclitaxel, Rapamycin, Epirubicin, Vincristine, Docetaxel and Vinorelbine.

Conclusions
An eight-IRlncRs-based prediction model was identified that has the potential to be an important tool to predict chemotherapeutic responses and prognosis for CC patients.

Full text

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Discover Oncology
Research
Immune‑related LncRNAs scores predicts chemotherapeutic responses
andprognosis incervical cancer patients
WeijieTian1· SongsongTan1· JunWang1· PingShen1· QingfenQin1· DanZi1
Received: 12 September 2023 / Accepted: 10 April 2024
© The Author(s) 2024 OPEN
Abstract
Background Long non-coding RNAs (LncRNAs) regulating the immune microenvironment of cancer is a hot spot. But little
is known about the inuence of the immune-related lncRNA (IRlncRs) on the chemotherapeutic responses and prognosis
of cervical cancer (CC) patients. The purpose of the study was to identify an immune-related lncRNAs (IRlncRs)-based
model for the prospective prediction of clinical outcomes in CC patients.
Methods CC patients’ relevant data was acquired from The Cancer Genome Atlas (TCGA). Correlation analysis and Cox
regression analyses were applied. A risk score formula was formulated. Prognostic factors were combined into a nomo-
gram, while sensitivity for chemotherapy drugs was analyzed using the OncoPredict algorithm.
Results Eight optimal IRlncRs(ATP2A1-AS1, LINC01943, AL158166.1, LINC00963, AC009065.8, LIPE-AS1, AC105277.1,
AC098613.1.) were incorporated in the IRlncRs model. The overall survival (OS) of the high-risk group of the model was
inferior to those in the low-risk group. Further analysis demonstrated this eight-IRlncRs model as a useful prognostic
marker. The Nomogram had a concordance index of survival prediction of 0.763(95% CI 0.746–0.780) and more robust
predictive accuracy. Furthermore, patients in the low-risk group were found to be more sensitive to chemotherapy,
including Paclitaxel, Rapamycin, Epirubicin, Vincristine, Docetaxel and Vinorelbine.
Conclusions An eight-IRlncRs-based prediction model was identied that has the potential to be an important tool to
predict chemotherapeutic responses and prognosis for CC patients.
Keywords Cervical squamous cell cancer· Immune· Long non-coding RNA· Prognosis· Chemotherapeutic Responses
Abbreviations
CC Cervical cancer
CSCC Cervical squamous cell carcinoma
FIGO International Federation of Gynaecology and Obstetrics
HPV Human papillomavirus
IRlncRs Immune-related long noncoding RNAs
lncRNAs Long noncoding RNAs
Weijie Tian, Songsong Tan should be considered similar in the author order of the study.
Qingfen Qin and Dan Zi are contributed equally.
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s12672- 024-
00979-1.
* Qingfen Qin, tanqingfeng@gz5055.com; * Dan Zi, zidangy08@163.com | 1Department ofGynecology, Guizhou Provincial People’s
Hospital, Medical College ofGuizhou University, Guiyang, Guizhou, People’sRepublicofChina.

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ncRNAs Noncoding RNAs
OS Overall survival
sIRlncRs Survival-related IRlncRNAs
TCGA The Cancer Genome Atlas
TMM Trimmed mean of M-values
PCA Principal components analysis
GSEA Gene set enrichment analysis
ROC Receiver operating characteristic
AUC Area under the curve
IC50 Half-maximal inhibitory concentration
1 Introduction
Cervical cancer (CC) is still the common cause of disease-related mortalities in women, leading to nearly 300,000 deaths
worldwide [1]. Cervical squamous cell carcinoma (CSCC) is the primary pathological subtype and comprises the most CC
cases [2]. Despite the utilization of treatment modalities such as surgery, radiotherapy, and chemotherapy, these therapies
have shown limited ecacy in patients with advanced-stage disease [3–5]. The International Federation of Gynaecology
and Obstetrics (FIGO) stage is the primary prognostic indicator for CC. However, the FIGO stage cannot dierentiate the
various heterogeneity of CC in terms of clinical behavior. Patients with the same FIGO stage may often present obviously
dierent clinical outcomes. Therefore, identifying new prognostic indicators reecting the heterogeneity of CC is essential
and can facilitate individualized treatments for patients with an otherwise poor prognosis.
Increasing evidence showed that immune system disruption might lead to tumour progression and metastasis [6, 7].
Malignant cells can escape immunosurveillance by reducing the expression of major histocompatibility complex class
I molecules. Cervical adenocarcinoma has impaired recruitment of CDC1 and CD8 + T cells [8, 9]. Higher CCL22 + cell
inltration is negatively associated with prognosis in CC patients [9]. LINC00240 promotes natural killer T cell cytotoxic
activity in CC and enhanced the growth, invasion, and migration of CC cells [10].
LncRNAs are a class of non-coding RNAs (ncRNAs) with longer than 200 nucleotides. With the development of tran-
scriptome sequencing, it is clear that over 70% of the genome is transcribed into RNA, and the majority of them are
ncRNAs [11]. lncRNAs are involved in various transcriptional and post-transcriptional gene regulatory processes and play
crucial roles in the tumour immune response, including immune recognition and immune inltration [11]. Numerous
tumor-associated lncRNAs have been recognized as tumor cell factors that regulate tumor cell escape of immunosur-
veillance. These immune-related lncRNAs (IRlncRs) may play essential parts in immunotherapy resistance and further
impact the prognosis of cancer patients [12]. Specically, a subset of lncRNAs act as immune-related lncRNAs (IRlncRs)
by regulating immune responses in the tumor microenvironment. This subset of lncRNAs has been shown to play a key
role in modulating tumor immunosurveillance, immune cell inltration into the tumor microenvironment, and sensitiv-
ity of cancer cells to immunotherapy treatment [12, 13]. Cao etal. identied an immune-related ve-lncRNAs signature
positively correlated with tumour immune cell inltration and the poor prognosis in bladder cancer patients. The asso-
ciation of IRlncRs expression and the clinical outcomes of CC patients is reported, but the results lack validation [12].
The development of new prognostic markers is essential considering the inherent heterogeneity of cervical cancer,
guiding personalized treatment strategies. Reliable assessment of chemotherapy responses and determination of prog-
nostic risk would enable physicians to tailor more accurate treatment plans to improve outcomes. The IRlncRs signature
holds strong potential in risk stratication and chemotherapy selection for cervical cancer patients. By evaluating prog-
nosis and chemotherapy sensitivity, the IRlncRs model can provide a basis for clinical decision-making, oering patients
the most likely successful treatment strategies based on their molecular risk proles. To achieve this, we developed an
innovative IRlncRs model and performed preliminary invitro validation, demonstrating its ability to distinguish high-
risk and low-risk cervical cancer patients with signicant dierences in overall survival. Further analysis conrmed the
prognostic predictive capability of this model. Additionally, this model demonstrated utility in predicting chemotherapy
response, with high-risk patients showing resistance to several commonly used drugs.

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2 Materials andmethods
2.1 Patient datasets
CC transcriptome RNA-seq data in the format of Fragments Per Kilobase Million and the corresponding clinical data of
The Cancer Genome Atlas (TCGA) (GDC, https:// gdc. cancer. gov/) were downloaded.
2.2 Data preprocessing andnormalization
The raw RNA-sequencing count data from TCGA cervical cancer cohorts was preprocessed to ensure normalization
and integrity for downstream analyses. Briey, quality control was rst performed using FastQC to assess attributes like
guanine-cytosine content, overrepresented sequences, and duplication levels. Reads were then trimmed and ltered
to remove adapters and low-quality bases using Trimmomatic.
The RNA-seq pre-processed data was quantied and normalized using the Trinity pipeline (https:// github. com/
NCIP/ Trini ty_ CTAT) to generate normalized gene-level count data. This pipeline maps reads, assembles transcripts,
estimates abundances, and extracts dierentially expressed features. Normalization was conducted using the trimmed
mean of M-values (TMM) method to account for dierences in sequencing depth between samples using the EdgeR R/
Bioconductor package. TMM normalization controls for library size variability via scaling based on the ratio of read counts
between samples. Normalized expression data is represented as counts per million (CPM).
Samples with > 50% missing lncRNA expression data were excluded from analysis to avoid technical bias. For the
remaining samples, missing values were imputed using a k-nearest neighbor algorithm with k = 10 neighbors. Imputation
was conducted using the Bioconductor impute package. This allowed retention of samples with some missing data
rather than complete exclusion.
Additional ltering of lncRNAs was conducted to restrict analysis to those with evidence of abundance and variation
across samples. LncRNAs expressed at ≥ 0.5 CPM in at least 10% of samples and with an interquartile range greater than
0 were retained.
2.3 LncRNA profile mining
Three gene sets, “immune response(M19817)”, “immune system development(M3457)”, and “immune system
process(M13664)”, were acquired from the Molecular Signatures Database. LncRNAs with abundance lower than 0.5
and lncRNAs of normal tissues were excluded. Then, immune-related genes were acquired from the above three gene
sets. The Pearson correlation test analyses the correlation between the immune-related genes and lncRNAs. Absolute
value of correlation coecient > 0.5 and p < 0.001 were dened as IRlncRs.
2.4 Real‑time quantitative PCR
Total RNA from cell lines (Hela cell and HCerEpiC cell) was isolated using Trizol reagent (Invitrogen, USA) according
to the manufacturer’s instructions. cDNA Synthesis Kit (TaKaRa, Japan) was utilized to generate cDNA. 4.5μL diluted
cDNA (1:50) was used as the template in a 10μL qPCR reaction using the ABI 7500 fast real-time PCR system (Applied
Biosystems). GAPDH was used as a reference. The relative expression level was calculated by the 2−ΔCt method. Table1
shows the sequences of the forward and reverse primers of eight examined IRlncRs (ATP2A1-AS1, LINC01943, AL158166.1,
LINC00963, AC009065.8, LIPE-AS1, AC105277.1, AC098613.1.).
2.5 Model development
Univariate and multivariate Cox regression models were utilized to identify lncRNAs prognostic of overall survival and
build a predictive risk score formula. The Cox proportional hazards regression model was selected because it allows
assessment of the association between continuous gene expression data and censored survival outcomes while adjust-
ing for the eects of other covariates. Univariate Cox regression was carried out to extract IRlncRs correlated with the
OS of patients with CC at p < 0.05. Next, only the IRlncRs with a statistical signicance of p < 0.01 were further enrolled
in the stepwise multivariate Cox regression analysis to extract optimal IRlncRs independently associated with prognosis

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at p < 0.05. Finally, the survival-related IRlncRs(sIRlncRs) model (risk score) was constructed according to the regression
coecients with lncRNA expression. In other words, the prognostic risk score was formulated based on a linear combi-
nation of the expression level of theses IRlncRs multiplied by the regression coecients derived from the multivariate
Cox regression analysis, as mentioned above [14]. Patients were grouped into a high- and low-risk group according to
the median value of risk scores.
Kaplan–Meier analysis was employed to validate survival dierences between the high-risk and low-risk groups
stratied by the risk score formula. This non-parametric analysis was chosen because it is well-suited for estimating
group survival functions over time while accounting for censoring, which was essential for the overall survival endpoint
that had censored observations (patients still alive at last follow-up). The Kaplan–Meier estimator also provides median
survival times and key quantied survival statistics for each risk group. Using this method enabled validation of the risk
score by verifying poorer survival prognosis in the high-risk group compared to the low-risk group in a time-to-event
analysis context.
2.6 Independent prognostic analysis
We applied both single and multifactorial analyses to validate the validity of the risk score being an independent
prognostic marker for CC. The receiver operator characteristic (ROC) curve was utilized to assess whether the risk score’s
predictive power was reliable. The relationship between clinical traits and sIRlncRs was also studied. We also employed
the PCA (principal components analysis) method to demonstrate the distribution patterns between the low- and high-
risk groups. GSEA was applied to explore the distinct functional phenotypes between the high-risk and low-risk groups.
2.7 Construction apredictive nomogram
A prognostic nomogram including risk scores and clinical features for predicting the likelihood of 3-, and 5-year OS was
developed by R “rms” package. The calibration curves and C-index were used to evaluate the predictive accuracy of the
nomogram [15].
2.8 Prediction ofchemotherapeutic response
The clinical response of each CC patient in high- and low-risk groups to chemotherapy was estimated based on
the Genomics of Drug Sensitivity in Cancer (GDSC; https:// www. cance rrxge ne. org/) data. Twenty commonly used
chemotherapy drugs of CC, were selected for the chemotherapeutic response prediction through the ridge regression
Table 1 The forward and
reverse primer sequences
of eight examined IRlncRs
(ATP2A1-AS1, LINC01943,
AL158166.1, LINC00963,
AC009065.8, LIPE-AS1,
AC105277.1, AC098613.1.)
for performing real-time PCR
assay
Gene symbol Primer Primer Sequence (5′-3′)
ATP2A1-AS1 Primer_F GAG GAG AAT CCG CAC CAG GA
Primer_R TAG CCA CAA AGT CTT GGG TGT
LINC01943 Primer-F CAG GAA GCG TGA GGA CAG AA
Primer-R AAC CAG ACT GAT GCC ACA GG
AL158166.1 Primer-F TGA GCA TAG CCT CCA CTC CT
Primer-R AGA CAG CAC TGT CAG TCA CG
LINC00963 Primer-F GAA CTG CCT TTG GAA GCA AG
Primer-R AGG AGT TCG AGG CTG CAG TA
AC009065.8 Primer-F TTA GCT GGG CTG CGT TTA CA
Primer-R CCA CTC TCC CAC CTC CCT TA
LIPE-AS1 Primer-F CTC TGT CTC CGC CCC CTA AT
Primer-R TTC TCA AGC ATG CGT CGT TC
AC105277.1 Primer-F GTG ACC AGG TAC TGG GGA AA
Primer-R AAT GAG GTT CCA CAC CTG CT
AC098613.1 Primer-F GGG GAA AAT CAT CTC CCA TT
Primer-R TCA CAT TGC TCT GCC TCA TC

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using the “OncoPredict” R package [16]. The half-maximal inhibitory concentration (IC50) predicted of each CC patient
was used to assess dierential chemotherapeutic response.
2.9 Statistical analysis
Statistical analyses were carried out by the R statistical programming environment (version 4.0.2). Correlations between
the immune-related genes and lncRNAs were tested using the Pearson correlation test. Sensitivity and specicity of
signature were determined by ROC curves representing its power to dierentiate the dierent groups. The R package
“survivalROC” was used to calculate the area under the curve [17], and the “survival” R package was loaded to gure
survival analysis [18].
3 Results
3.1 The analysis process ofthis study
Figure1 displays the analysis process of our study. We downloaded transcriptome RNA-seq data and corresponding
clinical data of 289 cases of CC from the TCGA database. Among these cases, there were 253 CSCC patients, 33 cervical
adenocarcinoma patients, and 3 healthy control patients (Additional file3: TableS1). Then, the RNA-seq data were
Fig. 1 Analysis of the work-
ow of this study

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divided into mRNA and lncRNAs data. LncRNAs with an abundance less than 0.5 and normal tissue lncRNAs were
excluded. We identified 331 immune-related genes from gene sets of MSigDB, of which 255 lncRNAs were IRlncRs
validated by correlation analysis (Additional file1: Fig. S1). Next, we identified 28 IRlncRs that were associated with the
prognosis of CC. We further optimized theses IRlncRs by stepwise multivariate Cox regression, and eight sIRlncRs were
utlized to formulate the risk score model. Finally, we utilized the risk score model for a series of subsequent analyses,
including survival analysis, risk score analysis, clinicopathological characteristics, ROC curve analysis, PCA, and GSEA.
3.2 Construction ofanIRlncRs‑based risk score model
Of the 28 IRlncRs related to the prognosis of CC (p < 0.01), 25 were low-risk factors, and 3 were high-risk factors
(Additional file3: TableS2). Eight sIRlncRs were finally incorporated to formulate the risk score model, including
ATP2A1-AS1, LINC01943, AL158166.1, LINC00963, AC009065.8, LIPE-AS1, AC105277.1, AC098613.1 (Table2). All CC
samples were categorized into low-and the high-risk groups using the median risk score as a boundary (Fig.2A). The
vital status of each patient was plotted. The proportion of death events in different risk groups was also analyzed. The
mortality rate increased faster in the high-risk group than in the low-risk group (Fig.2B). The differentially expressed
genes (DEGs) displayed that the expression levels of AL158166.1 and AC105277.1 had a positive coefficient and acted
as risk factors. The other six sIRlncRs showed negative coefficients, including ATP2A1-AS1, LINC01943, LINC00963,
AC009065.8, LIPE-AS1, AC098613.1, and served as protective factors (Fig.2C).
To verify the clinical value of the selected sIRlncRs in predicting prognosis, we compared the expression levels of
the eight sIRlncRs in cervical cancer cells to that in normal human cervical epithelial cells. As illustrated in Fig.2D,
five of the six sIRlncRs serving as protective factors showed a significant decrease in the cervical cancer cells. One of
the two IRlncRs acting as risk factors showed a significant increase in the cervical cancer cells.
Moreover, Kaplan–Meier survival analysis was used to evaluate the above prognosis model’s impact on CC patients’
survival. Survival was inferior in the high-risk group than in the low-risk group (Fig.3).
3.3 Independent prognostic analysis
To explore the relationship between the selected IRlncRs and clinical features of CC, the potential association of
the eight IRlncRs with the clinicopathological features, including T-stage, N-stage, and tumor grading, was investi-
gated. The results presented that the expression level of LINC00963 negatively correlated with the grading, while
AL158166.1 was positively related to advanced grading (Fig.4A). The expression of LINC00963 and AC105277.1
decreased with progressive T-stages (Fig.4B), and the expression of LIPE−AS1 increased with the progression of the
N-stage (Fig.4C). We then performed independent risk analysis, and it showed the risk score model, N-stage, and
T-stage were negatively related to the OS in univariate analysis (p < 0.05) (Fig.5A). The results were further confirmed
in the multivariate analysis showing that the risk score model, N-stage, and T-stage were significantly associated with
OS (p < 0.05) (Fig.5B). The ROC (Receiver Operating Characteristic) curve analysis validated this finding, demonstrat-
ing the predictive accuracy of the model. The AUC values for grade, T-stage, N-stage and risk score model were 0.516,
0.704, 0.633, and 0.710, respectively (Fig.6). These results demonstrated the risk score model as an independently
reliable prognostic factor.
Table 2 Eight immune-related
lncRNAs identied from
multivariate Cox regression
analysis
Gene symbol Ensembl ID coef HR Low95 High95 p-value
ATP2A1-AS1 ENSG00000260442 −0.36 0.7 0.48 1 0.05
LINC01943 ENSG00000280721 −0.94 0.39 0.14 1.11 0.08
AL158166.1 ENSG00000227076 0.57 1.76 1.15 2.69 0.01
LINC00963 ENSG00000204054 −0.48 0.62 0.39 0.98 0.04
AC009065.8 ENSG00000261532 −0.56 0.57 0.3 1.07 0.08
LIPE-AS1 ENSG00000213904 −0.58 0.56 0.29 1.09 0.09
AC105277.1 ENSG00000232453.7 0.83 2.29 1.36 3.85 0
AC098613.1 ENSG00000121797 −0.85 0.43 0.14 1.3 0.14

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Fig. 2 Construction of an IRlncRs-based risk score model. A The eight IRlncRs-based risk score distribution; B The eight-IRlncRs-based risk
score distribution for CC patient survival status. C Heatmap of the eight-IRlncRs expression proles in the high-risk and low-risk subgroups;
D Relative expression of the 8 IRlncRs

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3.4 Construction ofthenomogram
The factors of age, grade, T-stage, N-stage and risk score were further combined to construct a compound nomogram for
predicting the OS of patients with CC at 3- and 5-year (Fig.7A). The points for the factors indicated their corresponding
contribution to the survival probability. The total points of each patient provided the estimated 3- and 5-year OS. The
C-index of our nomogram was 0.763(95% CI 0.746–0.780, p < 0.05). The actual recurrence rate and nomogram-predicted
survival rate matched well at 3years (Fig.7B) and 5years (Fig.7C), as shown by the calibration curves (Fig.7B, C).
3.5 The immune status ofthelow andhigh‑risk groups
We performed PCA to explore the dispersion of the low-and high-risk groups based on genome-wide expression sets
and the immune gene sets. Considering the immune gene sets, the low-and high-risk groups showed clustering (Fig.8A),
although there was no signicant separation of the two groups based on the genome-wide expression proles (Fig.8B).
The GSEA further veried the dierences in functional annotation. As shown in Fig.8C, D, the low-risk group’s genes
were predominantly mapped to the immune-related activities, such as immune response and immune system process.
However, there was no gene enriched in the high-risk group (p > 0.05).
3.6 Analysis ofchemotherapeutic responses inhigh‑ andlow‑risk groups
A total of 198 drugs were analyzed, and drug response to twenty commonly used chemotherapy drugs for CC were
analyzed using the Wilcoxon rank-sum test. There were signicantly lower IC50 levels for Paclitaxel, Rapamycin, Epiru-
bicin, Vincristine, Docetaxel, and Vinorelbine in the low-risk group compared with the high-risk group (Fig.9, p < 0.05),
indicating that the low-risk group was more sensitive to these drugs. Among the 20 drugs, only docetaxel and lapatinib
showed no signicant dierence in IC50 values (Additional le2: Fig. S2), which indicated that our IRlncRs-based risk
model might act as a potential predictor for chemosensitivity.
4 Discussion
Eight IRlncRs correlated with the overall survival of CC patients were identied. The risk score model based on these
eight IRlncRs demonstrated a strong ability to distinguish CC patients into low- and high-risk groups, which exhibited
signicant dierences in OS. Further multivariate analysis showed that the eight-IRlncRs model is a valid marker of OS
when accounting for other clinical characteristics, including T-stage and N-stage. The prognostic factors were further
analyzed and integrated into a well-designed nomogram that demonstrated high potential for clinical application.
Fig. 3 Survival curve of
CC patients. Kaplan–Meier
survival curve of OS among
CC patients from the low-risk
groups and high-risk groups.
The high-risk group show the
poorer prognosis

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Therefore, the eight-lncRNA model demonstrated promising value as a prognostic predictor of chemotherapeutic
responses in CC. PCA based on the immune gene sets demonstrated that the low- and high-risk groups exhibited
distinct immune statuses, with more abundant immune-related processes and responses observed in the low-risk group.
Finally, OncoPredict analysis revealed that the tissues from the high-risk group were resistant to six commonly used
chemotherapy drugs for CC.
Patients in the low-risk group may possess a better immune status, making them more sensitive to chemotherapy
drugs; conversely, patients in the high-risk group might exhibit an immunosuppressed state, leading to resistance
against chemotherapy drugs. The low-risk group presented enhanced antitumor immune pathways in the tumor
Fig. 4 The relationships between the sIRlncRs and clinical features. A grading; B T-stage; C N-stage

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microenvironment. Higher expression of immunostimulatory molecules promotes inltration and activity of immune cells
such as T cells and natural killer (NK) cells [19]. Robust immune activation better sensitizes tumor cells to chemotherapy
through increased antigen presentation and vulnerability to immune-mediated killing [20]. In contrast, the high-
risk group exhibited an immunosuppressed state. Downregulation of critical immune modulators reduces tumor
immunogenicity through diminished antigen presentation and a decreased presence of cytotoxic lymphocytes [21].
This allows for immune evasion and subsequent resistance against chemotherapy, which relies on the immune system
recognizing and responding to cancer cells damaged by drug treatment [22].
Certain immune cell and mRNAs predictors have been studied to predict treatment outcomes of gynecological cancer.
Several risk score models based on dierentially expressed genes have been developed to assess the outcomes of women
with female reproductive cancers. Pan etal. reported 149 genes that were correlated with the survival of CSCC patients,
and most of these genes were closely related to T cell activation [23]. Mairinger etal. developed a predictive scoring
Fig. 5 Cox regression. A Uni-
variate Cox regression showed
that the T stage, N stage, and
risk score model were corre-
lated with the prognosis of CC
patients. B Multivariate Cox
regression showed that the T
stage, N stage, and risk score
model were an independent
risk factor for CC patients
Fig. 6 Receiver operating
characteristic (ROC) curve.
ROC curves demonstrated the
prognostic value of the inde-
pendent prognostic factors

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system based on immune-related genes to predict the therapy response and prognosis of epithelial ovarian cancer;
however, the system was not validated for OS prediction in two datasets [24]. Yang etal. utilized 11 immune-related
genes to formulate an immune signature for predicting clinical outcomes and the response to immune checkpoint
inhibitors in CC patients [25]. Although several prediction models based on immune-related genes have been previously
developed [23, 24], they face challenges such as a large number of genes that aect their practical utility [23] or a lack of
validation across multiple datasets [24]. Compared to other mRNA-based prediction models, the lncRNA-based model
constructed in this study exhibits higher specicity and provides a more precise reection of the actual tumor condition
[26]. Moreover, this study goes beyond constructing a prediction model and delves into the dierences in immune status
and chemotherapy sensitivity between high-risk and low-risk groups, thereby supporting the clinical application of
this model. The nomogram demonstrated excellent predictive performance (C-index of 0.763), highlighting its robust
potential for clinical application. Additionally, among the eight immune-related lncRNAs identied in this study, only
LINC00963 and AC098613.1 have been previously reported [27, 28], while the other six have not. The current study
represents the rst discovery of their association with cervical cancer prognosis.
Fig. 7 The Nomogram for predicting overall survival of CC patients. A The Nomogram integrating the signature risk score with the clinical
characteristics for predicting OS. B The calibration curve for the Nomogram in TCGA cohort for predicting 3-year overall survival. C The cali-
bration curve for the Nomogram in TCGA cohort for predicting 5-year overall survival

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lncRNAs may have more specicity in presenting the actual tumor condition than other types of markers. A ten-lncRNA
signature for predicting the survival of patients with CC showed potential value as a prognostic biomarker for CC patients
(He etal. [27]). Compared to regular lncRNAs, immune-related lncRNAs (IRlncRs) are highly associated with the immune
system and exhibit distinct functions in the development of tumors [29]. Regular lncRNAs primarily regulate biological
processes such as tumor cell growth, proliferation, apoptosis, and migration, directly inuencing tumor formation and
progression. In contrast, the key role of IRlncRs lies in regulating the immune response in the tumor microenvironment
and participating in the process of determining whether tumor cells can evade immune surveillance. Specically, IRlncRs
can impact antigen expression on tumor cell surfaces, alter the local immune microenvironment of tumors, and thereby
aect whether tumor cells can be recognized and eliminated by immune cells. This is quite dierent from the direct eects
of regular lncRNAs on the biological functions of tumor cells [12, 30]. Therefore, the expression levels of IRlncRs often
correlate with clinical outcomes such as sensitivity to immunotherapy and prognosis [13], highlighting their advantage
as tumor biomarkers.
LINC00963 participates in the progression of several types of cancers, including lung cancer [31], prostate cancer
[28], and breast cancer [32]. LINC00963 can activate the oncogenic AKT/mTOR signaling pathway or EGFR signaling
pathway to enhance cancer cell metastasis [28]. AC098613.1 was also included in a four-lncRNA risk score serving as
an independent marker to predict the survival of bladder urothelial cancer patients [27]. However, the remaining six
Fig. 8 Principal components analysis (PCA) and gene set enrichment analysis (GSEA). A PCA plot showing high-risk group and low-risk
groups based on the immune-related gene sets. B PCA plot showing high-risk group and low-risk group based on the whole protein-coding
gene sets. C, D GSEA implied remarkable enrichment of immune-related phenotype in the low-risk group;

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IRlncRs have not been reported in the literature to date, and GSEA was conducted to predict their potential functional
annotations. The results showed more abundant immune-related processes in the low-risk group compared to the high-
risk group. Consistent infection by human papillomavirus (HPV), the primary etiology of CC, can lead to the shutdown
of host immune detection and the establishment of a local immunosuppressive status in HPV-associated CC [33]. These
six IRlncRs may play a signicant role in regulating these immune-related processes, and their modes of action warrant
further research.
This study primarily utilized bioinformatics analysis methods to establish the association between immune-related
long non-coding RNAs (IRlncRs) and the prognosis of CC, based on the reported lncRNA expression prole data from TCGA
database. The use of this large-scale database signicantly reduces the experimental workload and allows for ecient
identication of candidate biomarkers associated with prognosis. However, relying solely on bioinformatics predictions
has certain limitations, including potential biases resulting from selective population sampling, the retrospective nature
that may overlook important variables, and the absence of extensive external validation to ensure wider applicability.
Furthermore, the investigation of IRlncRs as prognostic markers shows promise but is still in its early stages, requiring
further research to understand their complex mechanisms and interactions in CC. The insucient comprehensive
metastasis data, the necessity for more rigorous experimental validation to conrm quantitative polymerase chain
reaction (qPCR) results, and the evaluation of clinical usefulness comprise the limitations and constraints of this study,
providing directions for future work.
Further studies could also consider validating the accuracy of this model in peripheral blood samples. Compared to
tissue samples, peripheral blood samples are more readily accessible and provide a comprehensive reection of the
body’s immune status, potentially resulting in higher accuracy of the predictive model [34]. However, the consistency
of lncRNA expression patterns between tumor tissue and peripheral blood may vary depending on the cancer type
and specic lncRNA. Certain lncRNAs have been explored as potential blood-based biomarkers for various cancers.
Fig. 9 Dierential chemotherapeutic responses of 6 drugs in low- and high-risk CC patients (A–F)

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For example, HOTAIR and LINC00152 show high specicity in identifying colorectal and gastric cancers, respectively.
However, it is notable that the diagnostic performance of many circulatory lncRNAs is still relatively poor when detected
individually, suggesting dierences in their expression patterns between blood and tumor tissue [34]. Despite the
challenges, developing predictive models using circulatory immune-related lncRNAs should be feasible, and this requires
a multidisciplinary approach involving molecular biology, bioinformatics, clinical research, and ethical considerations.
5 Conclusion
In conclusion, we identied an eight-IRlncRs signature that has the potential to be an important prognostic tool for CC
patients. We expect this IRlncRs model to be practical for forecasting clinical behaviour and guide precision medicine
approaches.
Acknowledgements The contents of the manuscript do not previously appear online. The authors would like to thank the TCGA databases
for the availability of the data.
Author contributions Collection and assembly of data: Wei-Jie Tian. Data analysis and interpretation: Songsong Tan and Jun Wang. Manuscript
writing: Weijie Tian, Qingfeng Qin and Ping Shen. Paper revision: Dan Zi, Qingfeng Qin. All authors have read and agreed to the published
version of the manuscript.”
Funding The present study was supported by the Science and Technology Program of Guizhou Province, China (Grant numbers: Qian Ke He
Ji Chu-ZK[2023] Yi Ban 204) and the Talent Project for Guizhou Provincial People’s Hospital(Grant numbers: Yuan Ren Cai Project [2022] -31).
Data availability IThe data used to support the ndings of this study are available from the TCGA open database (https:// tcgad ata. nci. nih.
gov/ tcga/; LUAD).
Declarations
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation,
distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. or g/ lic en ses/ b y/4. 0/.
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