Construction of a five-disulfidptosis-related-lncRNA signature for predicting prognosis and immune activity in kidney renal clear cell carcinoma

doi:10.21203/rs.3.rs-3915249/v1

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Construction of a five-disulfidptosis-related-lncRNA signature for predicting prognosis and immune activity in kidney renal clear cell carcinoma

https://doi.org/10.21203/rs.3.rs-3915249/v1

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Background: Kidney renal clear cell carcinoma (KIRC) is a highly aggressive cancer. Disulfidptosis is a novel mechanism of programmed cell death. However, the role of disulfidptosis-related lncRNAs (DRlncRNAs) in KIRC remains unknown. This study aimed to develop a prognostic model based on DRlncRNAs and examine their prognostic value in KIRC.

Methods: RNA sequencing and relevant clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses and the lasso algorithm were used to identify prognostic DRlncRNAs and establish a prognostic model. Multiple methods were used to assess the reliability of the model. Gene set enrichment analysis (GSEA), immune infiltration analysis and somatic mutation analysis were performed to evaluate the predictive performance of the model, and anticancer drugs were predicted.

Results: The prognostic model was established based on five DRlncRNAs and was identified as a good predictor of the survival and prognosis of patients with KIRC. GSEA revealed that DRlncRNAs were associated with apoptosis and immune-related pathways. Immune analysis suggested that low-risk patients had better immunotherapeutic outcomes. Somatic mutation analysis revealed that low-risk patients had a lower somatic mutation rate and TMB score and a better prognosis. In addition, axitinib, ibrutinib, osimertinib and ruxolitinib were found to be more effective in low-risk patients, whereas crizotinib, lapatinib, linsitinib and nilotinib were found to be more effective in high-risk patients. Finally, qRT-PCR was performed to determine the expression of DRlncRNAs in normal kidney cells and KIRC cell lines.

Conclusion: We constructed a risk model and proposed a novel strategy for diagnosing and treating KIRC.

Kidney renal clear cell carcinoma

Disulfidptosis

LncRNA

Immunotherapy

Prognostic signature

Approximately 52,360 men and 29,440 women are projected to be diagnosed with kidney cancer in the United States of America in 2023. Kidney cancer is the 10th leading cause of death among men with cancer and the 11th leading cause of death among women with cancer. Survival rates are similar for black and white patients with kidney cancer [1]. Renal cell carcinoma (RCC) is one of the common tumours in the genitourinary system. It is highly aggressive and has a high mortality rate [2]. Kidney renal clear cell carcinoma (KIRC) is the predominant pathological type of RCC, accounting for approximately 75–80% of RCC cases [3, 4]. KIRC is heterogeneous and has a high metastatic rate and a worse prognosis [5]. Immunotherapy based on immune checkpoint inhibitors (ICIs) has benefited patients with kidney cancer and has significantly better efficacy than conventional cytokine therapy. However, long-term use of the same drugs inevitably generates drug resistance [6]. Therefore, investigating new mechanisms underlying the pathogenesis of kidney cancer and identifying more effective therapeutic targets are necessary approaches to successful treatment.

Disulfidptosis is a novel mechanism of programmed cell death (PCD). It differs from other mechanisms of PCD, such as necroptosis, apoptosis, autophagy and ferroptosis. Disulfidptosis is mediated by high levels of cysteine uptake coupled with inadequate NADPH supply. NADPH depletion leads to the abnormal formation of disulfide bonds in actin cytoskeletal proteins, followed by disulfide stress caused by excessive accumulation of disulfide molecules, which further affects actin cytoskeletal proteins and leads to the collapse of the actin network and eventually cell death [7]. Specifically, in SLC7A11^high cells, glucose depletion leads to a decrease in NADPH production. However, the concentration of cystine does not decrease, resulting in its accumulation. Eventually, abnormal disulfide bonds bind to the actin cytoskeleton, leading to cell death [8]. Actin is a multifunctional cytoskeletal protein that participates in several cellular events such as transcription, translation, cell morphogenesis, cell mechanics, intracellular transport and disulfide formation [9, 10]. Alterations in the cytoskeleton of plants and animals play an active role in the initiation and regulation of PCD [11, 12]. Therefore, understanding the mechanism of disulfidptosis in tumours is important for inducing cancer cell death.

With the continuous development of bio-sequencing technology, numerous non-coding RNAs (ncRNAs) have been identified. Long non-coding RNAs (lncRNAs) are one of the classes of ncRNAs that have received substantial attention from scholars in recent years [13, 14]. The expression of the lncRNA NEAT1 is downregulated in RCC and is associated with a poor prognosis. Methylation of NEAT1 results in the upregulation of its expression and decreases the proliferative and migratory abilities of cancer cells [15]. The lncRNA PLK1S1 leads to RCC progression and sorafenib resistance by inhibiting the miR-653/CXCR5 axis [16]. The lncRNA SNHG12 stabilises SP1 by binding to SP1 and prevents its ubiquitination-dependent hydrolysis, leading to the upregulation of downstream CDCA3, which leads to RCC progression and sunitinib resistance [17]. Chen et al. established a prognostic nomogram based on five m6A-related lncRNAs to predict the overall survival of patients with KIRC and examined the biological functions of m6A-related lncRNAs [18]. Zhang et al. constructed a prognostic signature comprising seven cuproptosis-related lncRNAs that had higher predictive efficiency than clinicopathological parameters and further examined immune characteristics and drug sensitivity [19]. In addition, necrosis-, autophagy- and hypoxia-related lncRNA models have been constructed to predict the prognosis of KIRC [20–22], providing important guidance for treatment. Although the role of numerous lncRNAs in KIRC has been reported, in-depth studies on lncRNAs in KIRC are warranted owing to the scarcity of clinical samples and the limitations of cellular and animal models.

In a study, mass spectrometry revealed that the tumour microenvironment (TME) of KIRC is characterised by the high infiltration of lymphocytes, particularly CD8⁺PD-1⁺ T cells [23]. Owing to the low sensitivity of conventional radiotherapy and chemotherapy to RCC, surgery is the primary treatment for RCC [24]. Immunotherapy based on ICIs, such as PD-1/PD-L1 and CTLA-4 inhibitors, mainly targets TME and has revolutionised the treatment of multiple cancers [25]. Changes in the TME of RCC are regulated by the interaction between EHBP1L1 and JAK1 [26]. In a study, single-cell RNA sequencing of KIRC tumours revealed that T-cell depletion was significantly associated with a poor prognosis and might serve as a critical factor in the immunosuppression of KIRC tumours [27]. In this study, we investigated the differences in different immune cells and immune functions in the KIRC TME by disulfidptosis-related lncRNAs (DRlncRNAs).

Relevant clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) database. The R software was used to construct a prognostic model based on DRlncRNAs to improve the prediction of patient prognosis. In addition, the biological functions and signalling pathways of DRlncRNAs, immune cell infiltration, somatic mutations and immunotherapeutic efficacy were examined and antitumour drugs were predicted in different risk groups. The findings of this study provide insights into the biological mechanisms of DRlncRNAs in KIRC and may guide the prognosis of patients with KIRC in clinical settings.

Data collection

The RNA-seq, clinical and somatic mutation data of patients with KIRC, including transcriptomic data from 541 tumour samples, 72 paracancerous samples and 532 clinical data, were extracted from TCGA database. All patients with KIRC were randomly assigned to the training (n = 373) and test (n = 159) groups. The chi-square test was used to estimate differences in the clinical characteristics of patients between the two groups. Disulfidptosis-related genes were obtained from one of the most recently published articles [7].

Construction of a risk model based on disulfidptosis-related lncRNAs

We identified 10 disulfidptosis-related genes by searching the relevant literature [7]. Then, the correlation of the expression pattern between lncRNAs and genes linked to disulfidptosis was scrutinized by Pearson correlation coefficients with P < 0.001 and |R|> 0.4. Subsequently, prognostically significant DRlncRNAs were identified via continuous dimensionality reduction. First, 86 DRlncRNAs associated with the prognosis of KIRC were identified via univariate Cox analysis. Second, the lasso algorithm was used to reduce dimensionality, and 19 DRlncRNAs associated with the overall survival (OS) of patients with KIRC were identified [28]. Third, five DRlncRNAs were identified as significant prognostic factors via multivariate Cox analysis, and a prognostic model was constructed. The risk score of each patient with KIRC was determined using a formula derived from the model, and the patients were subsequently divided into two risk groups based on the median risk score.

Assessment of the accuracy and independence of the risk model and construction of a nomogram

Kaplan–Meier (K-M) curves were plotted to assess the predictive ability of the risk model. Receiver operating characteristic (ROC) curves were plotted to examine the ability of the model to predict 1-, 3- and 5-year survival probabilities. Principal component analysis (PCA) was used for dimensionality reduction and visualising the results of different subgroups. The risk model was examined using univariate and multivariate Cox regression analyses to assess whether it was a reliable predictor of outcomes. In addition, a nomogram was constructed by integrating various clinical characteristics and risk scores, and calibration curves were plotted to assess the accuracy of the nomogram.

Functional enrichment analysis

Differentially expressed genes (DEGs) were identified between the risk groups based on the following criteria: |Log2FC| > 1.0, p < 0.05. Gene Ontology (GO) analysis was performed to examine the biological functions of DEGs. The analysis included three aspects, namely, biological processes (BPs), cellular components (CCs) and molecular functions (MFs). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to investigate signalling pathways associated with DEGs. Gene set enrichment analysis (GSEA) was performed to identify signalling pathways in the low- and high-risk groups.

Tumour microenvironment, somatic mutation analysis and drug prediction

The ESTIMATE algorithm was used to evaluate differences in TMEs between the two risk groups [29]. The CIBERSORT algorithm was used to evaluate the infiltration levels of 22 types of immune cells in the two risk groups [30]. The ssGESA algorithm was used to estimate differences in immune cells and function between the two risk groups [31]. The Tumor Immune Dysfunction and Exclusion (TIDE) scores were calculated to examine the immune escape of tumour cells and assess their response to ICIs in the two risk groups [32]. Somatic mutation data extracted from TCGA database were used to analyse the tumour mutational burden (TMB) of patients in the two risk groups and to classify patients into low- and high-TMB groups based on the median TMB score. The ‘oncoPredict’ R package was used to calculate the IC50 values of common antitumour drugs to predict the drug response of patients with KIRC in different risk groups.

Cell culture and quantitative reverse transcription polymerase chain reaction

A human KIRC cell line (786-O, Caki-1) and a human normal control cell line (HK-2) were purchased from Pricella (Wuhan, China). The cells were cultured in RPMI-1640 medium (BI, Israel) supplemented with 10% foetal bovine serum (BI, Israel) and 1% streptomycin and penicillin. The cells were cultured at 37°C in a humidified environment with 5% CO2. TRIzol (Invitrogen, USA) was used to extract total RNA, and qRT-PCR was performed to detect the expression of the five DRlncRNAs. PCR was performed using UltraSYBR (Cwbio, China) on an ABI 7500 system under conventional conditions. Glycerol-3-phosphate dehydrogenase (GAPDH) was used as an endogenous reference. The primer sequences used for PCR are listed in Table 1.

Table 1

Primer sequences for qRT-PCR
Primer name	Primer sequence (5'−3')	Length (bp)
SPINT1-AS1	F: 5' GAGACACACCTGATCAGCCC 3'	97
SPINT1-AS1	R: 5' CTCTTCGATCCTGGGGTGC 3'	97
AL161782.1	F: 5' TGCTGGAGAAGAGTGGCTTC 3'	84
AL161782.1	R: 5' GGATTCCAGGTCAGACGCTT 3'	84
OVCH1-AS1	F: 5' GCAACTTCCTTGTTGATGGTCC 3'	128
OVCH1-AS1	R: 5' GGTTCATAGCCCCCTGCAAA 3'	128
AC131009.3	F: 5' GAGCCGGAGAGAACAGATAGC 3'	85
AC131009.3	R: 5' GCACTCGGAAGAGCAAGGAT 3'	85
AC108673.3	F: 5' ACCTGCTCTCCAGTGAATCC 3'	108
AC108673.3	R: 5' GGCTTCAGGCAGCACTTTTG 3'	108
GAPDH	F: 5' CCCCACCACACTGAATCTCC 3'	90
GAPDH	R: 5' GTACATGACAAGGTGCGGCT 3'	90

Statistical analysis

All statistical analyses were performed using the R software (version 4.2.1). The t-test was used to analyse differences between groups. K-M analysis and the log-rank test were used to examine differences in survival between the risk groups. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors associated with KIRC. A p-value of < 0.05 indicated statistically significant differences.

Data of patients with kidney renal clear cell carcinoma

A flow chart demonstrating the study protocol is shown in Fig. 1. A total of 532 patients with KIRC were divided into training (373 patients) and test (159 patients) sets in a ratio of 7:3. The data of patients in the training set were used to identify prognostic DRlncRNAs and establish a prognostic model, whereas the data of patients in the test set were used to verify the accuracy of the model. The two groups did not significantly differ in terms of clinical characteristics, such as age, sex, grade, and TNM stage (p > 0.05) (Table 2).

Table 2

The clinical characteristics of KIRC patients in the training, validation and overall sets
	Overall	Validation	Training	P
Age
≤65	349(65.6%)	100(62.89%)	249(66.76%)	0.4479
>65	183(34.4%)	59(37.11%)	124(33.24%)
Gender
FEMALE	187(35.15%)	56(35.22%)	131(35.12%)	1
MALE	345(64.85%)	103(64.78%)	242(64.88%)
Grade
G1-G2	242(45.49%)	71(44.65%)	171(45.84%)	1
G3-G4	282(53.01%)	83(52.2%)	199(53.35%)
Unknow	8(1.5%)	5(3.14%)	3(0.8%)
Stage
Stage I-Stage II	323(60.71%)	87(54.72%)	236(63.27%)	0.0805
Stage III-Stage IV	206(38.72%)	71(44.65%)	135(36.19%)
Unknow	3(0.56%)	1(0.63%)	2(0.54%)
T classification
T1	272(51.13%)	72(45.28%)	200(53.62%)	0.1364
T2	69(12.97%)	18(11.32%)	51(13.67%)
T3	180(33.83%)	65(40.88%)	115(30.83%)
T4	11(2.07%)	4(2.52%)	7(1.88%)
M classification
M0	421(79.14%)	133(83.65%)	288(77.21%)	0.3283
M1	79(14.85%)	20(12.58%)	59(15.82%)
Unknow	32(6.02%)	6(3.77%)	26(6.97%)
N classification
N0	240(45.11%)	86(54.09%)	154(41.29%)	0.1029
N1	16(3.01%)	2(1.26%)	14(3.75%)
Unknow	276(51.88%)	71(44.65%)	205(54.96%)

Risk model based on DRlncRNAs for predicting the prognosis of KIRC

We obtained 10 disulfidptosis-related genes from the literature. Then, the Pearson correlation coefficient method analyzed the correlation between lncRNAs in the TCGA database and the disulfidptosis-related mRNAs, and 431 DRlncRNAs were identified (Supplementary Table S1). As shown in Fig. 2A, the Sankey diagram demonstrates 10 disulfidptosis-related genes and 431 DRlncRNAs involved in a co-expression network. Univariate Cox regression analysis of OS data from KIRC patients in the training set identified 86 DRlncRNAs associated with OS in KIRC (Supplementary Table S2). Subsequently, 19 DRlncRNAs that were markedly associated with the prognosis of KIRC were identified via lasso regression analysis (Figs. 2B, C). Finally, five prognostic DRlncRNAs were used to construct a multivariate Cox regression model, including SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3 and AC108673.3 (Fig. 2D). The risk score was calculated based on the expression and Cox regression coefficients of these lncRNAs as follows: risk score = SPINT1-AS1 × -0.284849287424872 + AL161782.1 × -0.558120637222694 + OVCH1-AS1 × -0.763337398302014 + AC131009.3 × 0.469858141530083 + AC108673.3 × 0.431142218409321.

Furthermore, the correlation between 10 disulfidptosis-related genes and the 5 DRlncRNAs was examined (Fig. 2E).The expression of the five DRlncRNAs in the low- and high-risk groups is demonstrated in Fig. 2F and analyzes the risk score distribution, survival time, and survival status of the two risk groups in the training set. The expression of SPINT1-AS1, AL161782.1 and OVCH1-AS1 was higher in the low-risk group than in the high-risk group, indicating that the three DRlncRNAs may serve as protective factors for the prognosis of KIRC. However, the expression of AC131009.3 and AC108673.3 was higher in the high-risk group than in the low-risk group, indicating that the two DRlncRNAs may serve as risk factors for KIRC. Subsequently, K-M curves were plotted to assess differences in OS and progression-free survival (PFS) between the two risk groups. The results indicated that OS and PFS were better in the low-risk group than in the high-risk group (p < 0.001 for all) (Figs. 2G–H).

Validation of the DRlncRNA-based risk model and PCA

The reliability of the risk model was verified in the test set. Risk curves and scatter plots demonstrated that patients in the low-risk group had lower risk scores, longer survival time and better prognosis than those in the high-risk group (Fig. 3A). In addition, the heatmap validated that SPINT1-AS1, AL161782.1 and OVCH1-AS1 were protective factors for KIRC, whereas AC131009.3 and AC108673.3 were risk factors for KIRC. K-M analysis revealed that OS was better in the low-risk group than in the high-risk group (p < 0.001) (Fig. 3B). The sensitivity and specificity of the risk score were assessed using ROC curves. The risk score had a larger area under the curve (AUC) than other clinical risk indicators (Fig. 3C).

The AUC values of the risk model for predicting 1-, 3- and 5-year OS were > 0.7 (Fig. 3D), suggesting that the risk model based on the five DRlncRNAs has good discrimination efficiency. Subsequently, PCA was performed to assess the predictive performance of 431 DRlncRNAs (Fig. 3E) and the risk model (Fig. 3F). The risk model was effective in distinguishing patients in the low-risk and high-risk groups, indicating a notable difference in sensitivity to disulfidptosis between the two groups.

Independent prognostic analysis and nomogram

Univariate and multivariate Cox regression analyses were performed to determine whether the five DRlncRNAs independently predicted the OS of patients with KIRC. The results of univariate Cox regression analysis showed that age, grade, stage and risk score were significantly associated with OS (p < 0.05 for all) (Fig. 4A). Subsequently, multivariate Cox regression analysis revealed that the risk score (P < 0.001) could independently predict OS in patients with KIRC (Fig. 4B). These results suggest that the risk model constructed based on the five DRlncRNAs is an independent prognostic factor for KIRC.

Furthermore, the risk model had higher AUC values (AUC = 0.760) than other clinical risk indicators (Fig. 4C). The AUC values of the risk model for predicting 1-, 3- and 5-year survival probabilities were > 0.7, demonstrating the predictive accuracy and reliability of the model (Fig. 4D). A nomogram was constructed by integrating multiple clinical factors and risk scores to predict the survival of patients with KIRC at 1, 3 and 5 years (Fig. 4E). Subsequently, a calibration curve was plotted to verify the accuracy of the nomogram (Fig. 4F). K-M curves were plotted to examine the efficiency of the risk model in predicting several clinical traits, such as age, sex and TNM stage. The results indicated that the low-risk group had a better prognosis (Fig. 5).

Functional enrichment analysis

GO and KEGG enrichment analyses were performed to examine the functions of 735 DEGs (Supplementary Table S3). The DEGs were enriched in immune-related BPs, including humoral immune responses, defence responses against bacteria and organic anion transport. In terms of CCs, the DEGs were significantly enriched in apical cells, collagen-containing extracellular matrix and immunoglobulin complexes. In terms of MFs, the DEGs were involved in receptor–ligand activity, antigen binding and immunoglobulin–receptor binding (Fig. 6A).

KEGG enrichment analysis indicated that the DEGs were mainly enriched in pathways associated with cytokine–cytokine receptor interaction, protein digestion and absorption, viral protein interaction with cytokines and cytokine receptors and complement and coagulation cascades (Fig. 6B).

In addition, GSEA revealed that pathways associated with primary immunodeficiency, taste transduction, haematopoietic cell lineage, nod-like receptor signalling and cytokine–cytokine receptor interaction were significantly enriched in the high-risk group (Fig. 6C), whereas pathways associated with apoptosis; drug metabolism cytochrome p450; fatty acid metabolism; PPAR signalling; valine, leucine and isoleucine degradation and other metabolism-related pathways were significantly enriched in the low-risk group (Fig. 6D). We speculate that disulfidptosis is closely related to immune-related pathways, metabolism and apoptosis.

Immune infiltration landscape analysis and immunotherapeutic efficacy

TME plays a crucial role in the progression and treatment of KIRC. Therefore, we examined TME in different risk groups using multiple algorithms. The results of the ESTIMATE algorithm revealed that the immune and estimate scores of patients were lower in the low-risk group than in the high-risk group (p < 0.05 for all) (Fig. 7A), whereas stromal scores did not differ between the two groups. Furthermore, the CIBERSORT algorithm was used to evaluate the proportion of 22 types of tumour-infiltrating immune cells. The distribution of the immune cells was different between the two risk groups (Fig. 7B). As shown in Fig. 7C, the box plot demonstrated that the abundance of CD8⁺ T cells, T follicular helper cells, regulatory T cells (Tregs), M0 macrophages, activated mast cells and neutrophils was lower in the low-risk group, whereas that of CD4 resting memory T cells, monocytes, M1 macrophages, M2 macrophages, resting dendritic cells, resting mast cells and eosinophils was higher in the low-risk group (p < 0.05 for all). In addition, the ssGESA algorithm was used to analyse immune cell infiltration and immune function in different risk groups. The abundance of iDCs, mast cells and neutrophils was higher in the low-risk group, and MHC class I and type II IFN responses were elevated in the low-risk group (p < 0.05 for all) (Fig. 7D). Finally, the TIDE algorithm was used to examine the relationship between the risk scores and immunotherapy responses. The results showed that the high-risk group had a significantly higher potential for immune escape and worse treatment outcomes (p < 0.001) (Fig. 7E).

Analysis of the somatic mutation landscape

On comparing the somatic mutation rate between the two risk groups, we found that the mutation rate was lower in the low-risk group (153/199 [76.88%] samples) than in the high-risk group (138/168 [82.14%] samples). The top 15 genes driving the mutations are shown in Fig. 8A. Furthermore, TMB scores were higher in the high-risk group than in the low-risk group (P = 0.012) (Fig. 8B). Based on the median TMB score, patients with KIRC were divided into low- and high-TMB groups. K-M analysis indicated that patients in the low-TMB group had significantly better OS than those in the high-TMB group (p < 0.001) (Fig. 8C). The TMB and risk scores were integrated to compare their efficiency in predicting the prognosis of KIRC. K-M analysis revealed that patients with low TMB and risk scores had the best OS rates, whereas those with high TMB and risk scores had the worst OS rates (p < 0.001) (Fig. 8D).

Analysis of drug response

Given the significant differences in prognosis, immune microenvironment and somatic mutation frequency between the two risk groups, we screened for drugs sensitive to patients in the two risk groups for precision therapy. The R package oncoPredict can be used to associate antitumour drugs with biomarkers and to easily predict the response of patients to many anticancer drugs [33]. We used oncoPredict to screen for antitumour drugs for patients with KIRC in different risk groups. The IC50 values of four drugs (axitinib, ibrutinib, osimertinib and ruxolitinib) were lower in the low-risk group, indicating that these drugs are more beneficial for patients with KIRC with low risk scores (Figs. 9A–D). The IC50 values of the other four drugs (crizotinib, lapatinib, linsitinib and nilotinib) were higher in the low-risk group, indicating that these drugs are more effective in patients with KIRC with high risk scores (Figs. 9E–H).

Assessment of the expression of DRlncRNAs via qRT-PCR

To validate the reliability of the model, qRT-PCR was performed to verify the expression of the five DRlncRNAs in normal human renal tubular epithelial cells (HK-2) and KIRC cells (786-O, Caki-1). The expression of SPINT1-AS1 and AL161782.1 was lower in KIRC cells than in normal kidney cells, whereas that of OVCH1-AS1, AC131009.3 and AC108673.3 was higher in KIRC cells than in normal kidney cells (Fig. 10). These results demonstrate the reliability of the risk model constructed based on the five DRlncRNAs.

KIRC is a relatively common type of cancer in adults. Because KIRC is not easily detected in the early stages, most patients with KIRC are diagnosed in the middle to late stages and have progressed to metastasis [34]. Few biomarkers are available for early diagnosis or treatment of KIRC, resulting in a relatively poor prognosis [35]. Immunotherapy can be used to treat patients with kidney cancer. However, two-thirds of patients with advanced disease respond poorly to ICI therapy possibly because of factors in TME that help tumour cells evade immunotherapy [36]. To improve the diagnosis of patients with early-stage KIRC and reduce the likelihood of metastasis, we screened for prognosis-related biomarkers for KIRC using bioinformatic tools. These markers may help to develop more effective immunotherapeutic strategies.

PCD is an active cell death mode mediated by specific genes and proteins. It plays an essential role in maintaining the growth and development of the body and the normal physiological functions of tissues and organs [37]. The PCD mechanisms reported in KIRC are ferroptosis, autophagy and apoptosis. Researchers have attempted to induce PCD through some targets and subsequently kill cancer cells to achieve the goal of treating tumours. Targeted inhibition of ISCA2 reduces HIF-1/2α levels, increases lipid peroxidation, induces iron death and inhibits tumour growth in KIRC [38]. Silencing of the obesity-associated protein FTO enhances N6-methyladenosine (m6A)-mediated autophagic flux and inhibits tumour growth and metastasis in KIRC [39]. In addition, knockdown of KEAP1 inhibits the resistance of KIRC cells to axitinib and promotes apoptosis [40]. Wang et al. constructed a 6-cuprotosis-related-gene signature to predict the prognosis of KIRC accurately [41]. Recent studies have proposed a novel disulfide stress-mediated PCD mode called disulfidptosis [7].

Excessive accumulation of intracellular disulfide molecules causes disruption of the actin network because actin cytoskeletal proteins are especially vulnerable to disulfide stress. Studies have demonstrated a close relationship between actin and apoptosis at the cellular and tissue levels. Actin binds to the α-actin protein, predisposing osteoblasts at focal adhesion sites to apoptosis [42]. Apoptotic cells can be expelled by the assembly of F-actin and myosin [43]. The precise alignment of disulfide bonds is essential for maintaining the structural integrity of intracellular cysteine-rich cytokines [44]. Oxidised cysteine residues in actin in sickle cell anaemia can form disulfide bonds that reduce the dynamics of actin filaments and lead to disintegration of the erythrocyte cytoskeleton [45]. The thiol–disulfide oxidoreductase PDI1;1 reduces and degrades disulfide bonds in the high-molecular-weight structure of rice actin [46]. In addition, the surface virulence factor IcsA plays an important role in actin-based motility in Shigella through three cysteine residues [47].

In-depth studies have revealed that lncRNAs can either promote or suppress tumours by participating in gene signalling regulation and can serve as promising potential biomarkers [48–50]. In a study, eight ferroptosis-associated lncRNAs were used to construct a predictive model that accurately predicted the prognosis of KIRC and suggested treatment options [51]. However, the role of DRlncRNAs in KIRC remains elusive. In this study, we identified DRlncRNAs based on disulfidptosis-related genes and investigated how lncRNAs regulating disulfidptosis affect tumour progression and prognosis in KIRC.

Stepwise dimensionality reduction was performed sequentially through lasso and Cox regression analyses to reduce the number of feature parameters and construct a prognostic model. This method has been reported in several studies [52, 53]. Five DRlncRNAs significantly associated with the prognosis of KIRC, namely, SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3 and AC108673.3, were identified and used to construct a prognostic model. Subsequently, a nomogram was developed to improve the prediction of OS of patients with KIRC at 1, 3 and 5 years. Of the five DRlncRNAs, three lncRNAs, namely, OVCH1-AS1, AC131009.3 and AC108673.3 have not been investigated previously, whereas the other two lncRNAs have been closely associated with cancer. The lncRNA SPINT1-AS1 has been extensively investigated. It is inconsistently expressed in different cancers and is essential for inhibiting or promoting cancer progression. Initially, the expression of lncRNA SPINT1-AS1 was found to be elevated in colorectal cancer (CRC), negatively associated with the mRNA expression of SPINT1 and associated with the prognosis of CRC [54]. Subsequent studies have demonstrated that SPINT1-AS1 promotes CRC progression by suppressing miR-214 and increasing HDGF expression [55]. In addition, SPINT1-AS1 is an important breast cancer (BC) regulator that may interact with miRNA let-7 a/b/i-5p, and its knockdown inhibits the proliferative and migratory abilities of BC cells and promotes the resistance of BC cells to lapatinib [56, 57]. SPINT1-AS1 is upregulated in cervical cancer, inhibits miR-214 via DNM3OS, activates the Wnt/β-catenin signalling pathway and is associated with a poor prognosis [58]. In addition, SPINT1-AS1 expression is downregulated in oesophageal squamous cell carcinoma (ESCC), and patients with low expression of SPINT1-AS1 have shorter OS [59]. Furthermore, the expression of SPINT1-AS1 is significantly increased in growth hormone-secreting pituitary adenomas (GH-PAs) and is strongly associated with the aggressiveness of cancer [60]. In a study, 14 immune-related lncRNAs, including SPINT1-AS1, were used to construct a risk model for predicting the prognosis of melanoma [61]. The lncRNA AL161782.1 is considered an m6A- and cuproptosis-related lncRNA in KIRC [62, 63]. In this study, AL161782.1 was identified as a DRlncRNA. We speculate that it is involved in multiple PCD modes.

The biological functions and signalling pathways of the five DRlncRNAs were examined via enrichment analysis. The results of GO and KEGG analyses indicated that the DRlncRNAs were associated with several immune and metabolic pathways. Several proteins involved in complement and coagulation cascade reactions are upregulated in the serum of patients with thymoma [64]. The DRlncRNAs were found to be associated with complement and coagulation cascades in this study. GSEA revealed that the DRlncRNAs can influence immune- and apoptosis-related pathways in KIRC. However, further studies are warranted to verify these findings.

Changes in the TME of kidney cancer may lead to disease progression [65]. We examined the relationship between risk scores and changes in the TME of KIRC. Both immune and estimate scores were significantly higher in the high-risk group. In addition, patients in the high-risk group had higher immune cell infiltration and enhanced immune function, which explains, to some extent, differences in the OS of patients with KIRC between the two risk groups. The NF-KB signalling pathway is significantly activated in infiltrating CD8⁺ T cells, inducing pro-apoptosis and leading to a worse overall prognosis [66], which is consistent with the results of this study. Tregs have been strongly associated with a poor prognosis and poor immunotherapy outcomes in KIRC [67]. Th2 cells belong to a subpopulation of CD4⁺ T cells that secrete IL-4 and IL-10 to promote tumour growth by suppressing the host immune system [68]. This phenomenon was observed in the high-risk group in this study. We speculate that disulfidptosis mediates alterations in immune function and hence influences the progression of KIRC. However, relevant basic studies demonstrating the relationship between disulfidptosis and immunity in KIRC are lacking. In this study, the abundance of most immune cells and immune function scores were higher in the high-risk group, suggesting that immune activity was stronger in patients with high risk scores. Furthermore, the TIDE algorithm was used to estimate the relationship between risk scores and response to ICIs. The results revealed that the high-risk group had a poorer response to ICI treatment, suggesting that DRlncRNAs are promising biomarkers for predicting response to ICIs in KIRC.

Higher TMB is associated with poorer survival outcomes and may inhibit immune infiltration in KIRC [69]. In this study, the risk scores were found to be significantly positively correlated with TMB scores. The high-risk-score and high-TMB-score groups had the shortest survival and the worst prognosis, whereas the low-risk-score and low-TMB-score groups had the highest survival rate and the best prognosis. This finding demonstrates the accuracy of the risk score in predicting immunotherapy outcomes in patients with KIRC. Furthermore, we evaluated the sensitivity of patients with KIRC to anti-cancer drugs in the two risk groups. Axitinib, ibrutinib, osimertinib and ruxolitinib were more effective in patients in the low-risk group, whereas crizotinib, lapatinib, linsitinib and nilotinib were more effective in patients in the high-risk group. These findings may help guide the treatment of patients with KIRC in clinical settings.

In summary, this study is the first to report the role of DRlncRNAs in KIRC. A prognostic model was constructed based on DRlncRNAs using data from TCGA-KIRC cohort. The findings of this study offer prospects for the diagnosis and treatment of KIRC.

Acknowledgements

The authors sincerely acknowledge the publicly available TCGA (https://portal.gdc.cancer.gov/) database.

Authors’ contributions

KX, WM, HS, and HD designed the study and drafted the manuscript. DL and KJ provided the statistical software and performed the data analysis. YZ, MZ, and HZ arranged the figures and tables. XH, JJ and ZZ reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was supported by the Major Project of Natural Science Research in Colleges and Universities in Jiangsu Province (20KJA320005), Natural Science Foundation of Jiangsu Province (BK20230842), the Open Project Program of Jiangsu Key Laboratory of Zoonosis (No. R2015), China Health Promotion Foundation (Public Hospital High Quality Development Research Public Welfare Project Fund), and the Research Development Fund of Kanda College of Nanjing Medical University.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Not necessary.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Construction of a five-disulfidptosis-related-lncRNA signature for predicting prognosis and immune activity in kidney renal clear cell carcinoma

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Abstract

Figures

Introduction

Material and methods

Data collection

Construction of a risk model based on disulfidptosis-related lncRNAs

Assessment of the accuracy and independence of the risk model and construction of a nomogram

Functional enrichment analysis

Tumour microenvironment, somatic mutation analysis and drug prediction

Cell culture and quantitative reverse transcription polymerase chain reaction

Statistical analysis

Results

Data of patients with kidney renal clear cell carcinoma

Risk model based on DRlncRNAs for predicting the prognosis of KIRC

Validation of the DRlncRNA-based risk model and PCA

Independent prognostic analysis and nomogram

Functional enrichment analysis

Immune infiltration landscape analysis and immunotherapeutic efficacy

Analysis of the somatic mutation landscape

Analysis of drug response

Assessment of the expression of DRlncRNAs via qRT-PCR

Discussion

Conclusion

Declarations

Acknowledgements

Authors’ contributions

Funding

Availability of data and materials

Ethics approval and consent to participate

Consent for publication

Competing interests

References

Additional Declarations

Supplementary Files

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