An Efficient Five-lncRNA Signature for Lung Adenocarcinoma Prognosis and Its Overall Survival Prediction

doi:10.21203/rs.3.rs-1348662/v1

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Research Article

An Efficient Five-lncRNA Signature for Lung Adenocarcinoma Prognosis and Its Overall Survival Prediction

https://doi.org/10.21203/rs.3.rs-1348662/v1

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Background

Lung adenocarcinoma (LUAD) is a sex-biased and easily metastatic malignant disease. A signature based on 5 long non-coding RNAs (lncRNAs) has been established to promote the overall survival (OS) prediction effect on LUAD.

Methods

The RNA expression profiles of LUAD patients were obtained from The Cancer Genome Atlas. OS-associated lncRNAs were identified based on the differential expression analysis between LUAD and normal samples followed by survival analysis, univariate and multivariate Cox proportional hazards regression analyses. Prognostic lncRNA with sex dimorphism was determined based on the analysis of expression between men and women. Functional enrichment analysis of the Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed to explore the possible mechanisms of 5-lncRNA signatures.

Results

A 5-lncRNA signature (composed of AC068228.1, SATB2-AS1, LINC01843, AC026355.1, and AL606489.1) was found to be effective in predicting high-risk LUAD patients as well as applicable to female and male subgroups and < 65-year and ≥ 65-year age subgroups. The forecasted effect of the 5-lncRNA signature was more efficient and stable than the TNM stage and other clinical risk factors (such as sex and age). Functional enrichment analysis revealed that the mRNA co-expressed with these five OS-related lncRNAs was associated with RNA regulation within the nucleus. AL606489.1 demonstrated a sexual dimorphism that may be associated with microtubule activity.

Conclusion

Our 5-lncRNA signature could efficaciously predict the OS of LUAD patients. AL606489.1 demonstrated gender dimorphism, which provides a new direction for mechanistic studies on sexual dimorphism.

Lung adenocarcinoma (LUAD)

The Cancer Genome Atlas TCGA

lncRNA

gender dimorphism

prognostic prediction

Lung cancer is the leading cause of death in cancer patients across the world^[1]. Approximately 20% of the lung cancer cases are accounted for by small-cell lung cancer, while the remaining 80% are accounted for by non-small-cell lung cancer^[2]. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma are the most common subtypes of non-small-cell lung cancer^[3]. LUAD shifts earlier than lung squamous cell carcinoma^[4]; therefore, it is very important to have effective early diagnostic methods for LUAD. Epidemiological studies have revealed that LUAD varies between men and women, with the highest incidence of occurrence among never-smokers and women^[5]. Meanwhile, several other factors affect lung cancer^[6]. In addition to the well-known risk factors such as tobacco, a close association of genetic variants has been demonstrated in multiple studies with the risk of lung cancer^[7–9].

With the widespread development of the human genome program^[10], several genes have been highlighted as being probably related to the onset of lung cancer, including AKT^[11], BRD4^[12], FGFR1^[13], BRAF^[14], MET^[15], PIK3CA^[16], and EGFR^[17]. However, the current reports on the long non-coding RNA (lncRNA) remain inadequate. LncRNA refers to any polyadenylated RNA of length > 200 bp ^[18], which forms a transcript of a large portion of the eukaryotic genome^[19]. In recent years, lncRNA has received continuous attention from researchers for its important role in the eukaryotic gene expression and genome remodeling^[20,21], including from the tumor perspective^[22]. Up to 37595 non-coding genes^[23] have been identified, according to the latest data from the ENCODE Project Consortium 2018^[24]. Clearly, the number of current studies on lncRNA are insufficient compared to this large number of genes.

In numerous lncRNA-related studies, lncRNA has been widely reported as a biomarker in the diseases of multiple systems^[25,26]. Ideal biomarkers not only facilitate the early diagnosis of disease^[27] but also predict patient prognosis^[28] as well as become potential drug therapeutic targets^[29]. In the field of LUAD, the effect of lncRNA as a biomarker on tumor cells has been explored in terms of immunity^[30], ferroptosis^[31], and cell pyroptosis^[32]. However, for LUAD, as a typical sex-biased^[33] malignancy, no investigation has yet explored the possible mechanisms of sex-biased differences in LUAD through the biomarker role of lncRNA. The occurrence of cancer is affected by gender differences, which is a consistent finding in the field of cancer epidemiology^[34]. Liu et al. analyzed the sex differences in lncRNAs across different cancers and found that LINC 00263 acts as an oncogene associated with men and estrogens; these findings may help explore the differential gene regulatory mechanisms in sex-specific cancers^[35].

In conclusion, this study identified the prognostic models of LUAD through information mining from public databases and explored the possible mechanisms of sex differences in LUAD. Alternatively, as The Cancer Genome Atlas (TCGA) contains the most extensive lncRNA expression matrix^[36], we prefer to conduct experiments in the TCGA database.

Data sources

The lncRNA and mRNA expression dataset in the FPKM format as well as the clinical characters for 535 LUAD patients and 59 normal patients were directly downloaded from the TCGA (https://portal.gdc.cancer.gov/), updated until December 5, 2021.

Isolation of differentially expressed lncRNA (DEL)

DELs between the LUAD and normal samples were isolated from all lncRNAs using the R software. The p-value of each lncRNA in the LUAD and normal samples was calculated by the rank-sum test, and the P-values were rectified by the False Discovery Rate (FDR) method. Only the lncRNAs with adjusted P-values < 0.05 and log2 | fold change | values > 2 were defined as differentially expressed lncRNAs. Volcano plot and heatmaps were visualized by the "plot" function and the "heatmap" package of the R, respectively.

Isolation of Overall survival (OS)-related lncRNAs in LUAD patients

First, we removed LUAD patients with OS < 0 days. Next, we employed univariate Cox proportional hazards regression (CPHR) analysis and Kaplan-Meier analysis to assess the presence of any significant correlations between the expression of each DELs and the OS of LUAD patients. Only lncRNAs with P < 0.01 from both the analyses were considered with a logical agreement in expression and prognostic effect and selected as the candidate OS-related lncRNAs. Then, half of the patients were randomly assigned as the “primary dataset” after removing patients with incomplete clinical information; the original complete dataset was called the “entire dataset”. In addition to randomization, the criteria for grouping included no statistical differences in the clinical characteristics between the “primary” and “entire datasets. In order to fit the prediction model with the best-prediction effect, multivariate CPHR analysis (stepwise model) of candidate OS-associated lncRNAs was performed with the R software in the “primary dataset”. To ensure the goodness of fitting and to avoid overfitting, the Akaike information criterion (AIC) was computed, and the prediction model with the lowest AIC was considered as the most ideal. LncRNAs included in the best prediction model were selected as OS-related lncRNAs.

Calculation and evaluation of the OS-related lncRNA signature

We determined the coefficients for each lncRNA by another multivariate CPHR analysis in the “primary dataset”. Until this point, we confirmed a risk score formula with the expressions of the OS-related lncRNAs as the independent variables and weighted by the regression coefficients corresponding to the lncRNAs. The risk scoring formula used is given below:

$$Risk Score=({\beta }_{1}\times \text{E}\text{x}\text{p}\text{r}\text{e}\text{s}\text{s}\text{i}\text{o}\text{n} {gene}_{1})+({\beta }_{2}\times \text{E}\text{x}\text{p}\text{r}\text{e}\text{s}\text{s}\text{i}\text{o}\text{n} {gene}_{2})+\cdots +({\beta }_{n}\times \text{E}\text{x}\text{p}\text{r}\text{e}\text{s}\text{s}\text{i}\text{o}\text{n} {gene}_{n})$$

where β_i correspond to the correlation coefficient.

To determine whether the OS-related lncRNA signature was an independent predictor of OS, we applied both univariate and multivariate CPHR analyses of OS-related lncRNA signature and the routine clinical risk factors (such as sex, age, TNM stage, tumor stage, lymph node metastasis, and distant metastasis) in the LUAD patients. Next, we assessed whether the predictive effect of the OS-related lncRNA signature on OS was independent of the routine clinical risk factors by stratified analysis. Meanwhile, to evaluate the prognostic effect of the lncRNA-based classifiers across different time ranges, we plotted the time-dependent receiver operating characteristic (ROC) curves and then calculated the area under the time-dependent ROC curve (AUC) values for each dataset. Finally, the predictive effects of the 5-lncRNA classifier and the classifiers based on the other clinical risk factors were compared by AUC.

Identification of a prognostic lncRNA with gender dimorphism

Whether the OS-related lncRNA was differentially expressed between the male and female patients was determined by the t-test using P < 0.05 as the significance threshold. In both the male and female groups, the patients were assigned into two groups of high or low expression bounded by the median expression of an OS-related lncRNA, and the Kaplan–Meier curve was applied to analyze whether there were differences in survival time between the high and low expression groups. The lncRNA was considered to be with gender dimorphism if an OS-related lncRNA was differentially expressed in males and females while showing different prognostic effects in males and females.

Functional enrichment analyses with co-expressed mRNA

The co-expression degree of OS-related lncRNAs and mRNA was determined by Pearson’s correlational analysis. The mRNAs with a positive correlation coefficient > 0.5 with OS-related lncRNAs were employed in the next step of enrichment analysis. The “cluster profile” package in R software was used for the functional enrichment analysis using the Gene Ontology (GO) terms and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways, with P < 0.01 set as a significance threshold.

Statistical analysis

For the survival analysis, the survival curves were plotted by the Kaplan–Meier method, and the differential P-values were calculated by the log-rank test. The t-test was used to compare the presence of any significant differences between the “primary ” and “entire datasets”. Unless otherwise specified, P < 0.05 was considered to indicate a statistical difference.

Candidate OS-related lncRNAs in LUAD patients

The flow chart illustrated in Fig. 1 shows the overall design of this study and some of the main results. After data collation, we obtained the expression data of 14142 lncRNAs and 19658 mRNAs for 535 LUAD samples and 59 normal samples from the TCGA-LUAD database. Through statistical comparison, 1223 DELs in tumor samples and normal samples were identified with a log2 | fold change |> 2 and adjusted P < 0.05. Of these 1223 DELs, 1044 lncRNA were upregulated and 179 were downregulated in the LUAD patients. Next, volcano plots and heatmaps of the differential genes were drawn using the "plot" function and the "pheatmap" package in the R software, the results of which are illustrated in Fig. 2 (A, B).

After the exclusion of 45 LUAD samples with incomplete survival data, 490 LUAD samples were finally enrolled in the study. In these 490 samples, 1223 DELs were analyzed by the Kaplan–Meier method and univariate CPHR analysis, where OS served as the dependent variable and lncRNA expression as the independent variable. The results of the univariate CPHR analysis are depicted in Supplementary Table 1, and a total of 15 lncRNAs were found to be statistically significantly associated with OS in LUAD patients (all P < 0.01). Of this 15 lncRNA, the high expression of 13 lncRNAs (namely, LINC02081, AC010343.3, LINC02086, AC068228.1, AC022784.1, SATB2-AS1, AL138789.1, LINC01843, LINC00519, AL606489.1, DEPDC1-AS1, AC087588.2, and FAM83A-AS1) was associated with a shorter OS. In contrast, the high expression of AC026355.1 and AL031600.2 was associated with a higher OS. Moreover, as shown in Supplementary Fig. 1, the results of the Kaplan–Meier analysis conformed to those of the univariate CPHR analyses. To this point, 15 lncRNAs with some correlation between the gene expression volume and prognosis were included as the candidate OS-related lncRNAs.

Identification and evaluation of an OS-related lncRNA signature to predict the OS

After removing 11 samples without complete clinical features (such as TNM stage or age), 479 LUAD samples formed the “entire dataset”, of which 239 groups were randomly selected as the “primary dataset”. The differential analysis revealed no statistical differences in the baseline clinical risk factors between the “entire ”and “primary datasets” (all P > 0.05; Table 1).

Table 1

Baseline clinical characteristics between the “entire dataset” and the “primary dataset”.
Character	Primary dataset	Entire dataset	p value(t)
	n = 239	n = 479
Age(year)			0.35
≥ 65	145 (60.67%)	266 (55.53%)
༜65	94 (39.33%)	213 (44.47%)
Gender			0.80
female	135 (56.49%)	260 (54.28%)
male	104 (43.51%)	219 (45.72%)
TNM stage			0.68
I	130 (54.39%)	259 (54.07%)
II-IV	109 (45.61%)	220 (45.93%)
Tumor stage			0.80
TX	2 (0.84%)	3 (0.63%)
T1-T2	212 (88.70%)	415 (86.64%)
T3-T4	25 (10.46%)	61 (12.73%)
Lymph node metastasis			0.78
NX	5 (2.09%)	9 (1.89%)
no	152 (63.60%)	311 (64.92%)
yes	82 (34.31%)	159 (33.19%)
Distant metastasis			0.80
MX	72 (30.13%)	139 (29.02%)
no	156 (65.27%)	316 (65.97%)
yes	11 (4.60%)	24 (5.01%)

Candidate prognosis lncRNAs were further screened by multivariate-CPHR analysis (stepwise model) in the “primary dataset” using AIC to avoid overfitting. Five OS-related lncRNAs were picked with the largest fit and the lowest AIC values (Table 2), namely, is AC068228.1, SATB2-AS1, LINC01843, AC026355.1, and AL606489.1. Next, these 5 OS-related lncRNAs and their risk coefficients were integrated into the predictive signature to obtain a risk scoring using the following formula:

Table 2. Five OS-related lncRNAs in the “primary dataset”.

lncRNA	coefficients	HR (95% CI)	pvalue
AC068228.1	0.4537	1.5741 (1.2184-2.0337)	<0.001
SATB2-AS1	2.1929	8.9619 (2.2255-36.0894)	0.002
LINC01843	0.0824	1.0859 (1.0109-1.1665)	0.023
AC026355.1	-0.3963	0.6727 (0.5325-0.8500)	<0.001
AL606489.1	0.1383	1.1483 (0.9741-1.3537)	0.099

Abbreviations: HR, hazard ratio; CI, confidence interval.

Risk score = (0.4537× Expression AC068228.1) + (2.1929 × Expression SATB2-AS1) + (0.0824 × Expression LINC 01843) + (-0.3963 × Expression AC026355.1) + (0.1383 × Expression AL606489.1).

Next, we computed the risk score for LUAD patients in the “primary dataset” according to the 5 lncRNA signatures. Using the median risk score as the cut-off value, 239 LUAD patients were classified into high- (n = 119) or low- (n = 120) risk groups. The risk score distributions, OS status, and the 5 lncRNA expression profiles in the “primary datasets” are depicted in Figure 3 (A–C). OS-related lncRNAs expression heatmaps revealed that the 4 upregulated lncRNA (i.e., AC068228.1, SATB2-AS1, LINC01843, and AL606489.1) demonstrated higher expression levels in the high-risk group, and the AC026355.1 expression levels were lower in the high-risk groups. As shown in Figure 3 (D), the Kaplan–Meier curve obviously showed that the OS time in the high-risk group was less than that in the low-risk group (P = 1.071E-04, log-rank test). Subsequently, in the “primary dataset”, as shown in Figure 3 (E–G), the AUC of the time-dependent ROC curve was 0.768 at 1 year, 0.668 at 3 years, and 0.702 at 5 years.

To verify the prediction of 5-lncRNA signatures obtained from the “primary dataset”, we applied 5-lncRNA signatures to the “entire dataset” (n = 479). Similarly, 479 patients were classified into the high-risk (n = 244) and low-risk (n = 235) groups according to the median risk score in the “primary dataset”. The risk score distributions, OS status, and the 5 lncRNA expression profiles in the “entire dataset” are depicted in Figure 4 (A–C). The results from the “entire dataset” are consistent with those from the “primary dataset”. Meanwhile, the Kaplan–Meier curve (Figure 4 (D)) showed that the OS in the high-risk group (n = 244) was significantly shorter than that in the low-risk group (n = 235) (P = 5.587E-07, log-rank test). As shown in Figure 4 (E–G), the AUC of the time-dependent ROC curve was 0.738 at 1 year, 0.661 at 3 years, and 0.709 at 5 years. The 5-lncRNA signature showed a good prediction performance both in the “primary dataset” and the “entire dataset” of the LUAD patients.

The prognostic effect of the 5-lncRNA signature as an independent prognostic factor in LUAD patients

Next, to examine whether the prognostic performance of the 5-lncRNA features was independent of other conventional clinical risk factors, we performed multivariate CPHR analyses. The hazard ratio (HR) in the “entire dataset” (Table 3) was 1.085 (P < 0.001, 95% CI = 1.052 - 1.118), and in the “primary dataset” (Supplementary Table 2) was 1.065 (P < 0.001, 95% CI = 1.028 - 1.102). The abovementioned data indicates that these 5 lncRNA signatures could independently predict the prognosis of LUAD patients as an independent prognostic factor for LUAD.

Table 3

Univariate and multivariate Cox proportional hazards regression analyses results of 5-lncRNA signature and other clinical risk factors in the “entire dataset”.
Characteristic	Univariate analysis		Multivariate analysis
Characteristic	HR (95%CI)	P-Value	HR (95%CI)	P Value
Age	1.009(0.992–1.025)	0.266	1.016(1.000-1.033)	0.049
Gender (female vs. male)	1.009(0.992–1.026)	0.855	0.923(0.666–1.278)	0.631
TNM stage (I-Ⅳ)	1.659(1.430–1.924)	< 0.001	1.422(1.138–1.776)	0.001
Tumor stage (T1-T4)	1.495(1.223–1.828)	< 0.001	1.149(0.929–1.421)	0.199
Lymph node metastasis (N0-N3)	1.715(1.435–2.049)	< 0.001	1.221(0.952–1.565)	0.114
Risk score	1.101(1.070–1.133)	< 0.001	1.085(1.052–1.118)	< 0.001
Notes: Bold values indicate statistical significance (P < 0.05).
Abbreviations: HR, hazard ratio; CI, confidence interval.

To validate the scope of applicability of the risk score prediction, we conducted a stratified analysis of the “entire dataset”. First, considering the number of people, 479 LUAD patients were classified into stage I (n = 259; Fig. 5 (A)) and stage Ⅱ–Ⅳ (n = 220; Fig. 5 (B)) based on the TNM stage. Each subgroup was classified as the high-risk and low-risk groups and then Kaplan–Meier curves were accordingly plotted. Second, 479 patients were classified into no (n = 311, Fig. 5 (C)) or yes (n = 159, Fig. 5 (D)) subgroups according to the absence or presence of lymphoid tract metastasis, respectively. Next, 479 patients with LUAD were assigned into male (n = 219, Fig. 5 (E)) and female subgroups (n = 260, Fig. 5 (F)). Then, 479 patients with LUAD were assigned to the age ≥ 65 years (n = 266, Fig. 5 (G)) and < 65 years subgroups (n = 213, Fig. 5 (H)). Finally, we noted that, in all subgroups, the survival time was significantly lower in the high-risk group than that in the low-risk groups, albeit it was not statistically significant in the female (P = 0.08) subgroup.

Five-lncRNA signature-based signature has a better survival prediction effect than other clinical characters

We employed the time-dependent ROC curves to compare the predictive effects of different prognostic factors using the AUC as a comparison indicator. As shown in Figure 6, the stable predictive performance of the 5-lncRNA signature is more outstanding than the conventional clinical characters such as the TNM stage, and are efficient to predict the prognosis of LUAD patients.

AL606489.1, an OS-related lncRNAs, demonstrating gender dimorphism

Among the 5 OS-related genes, AL606489.1 was differentially expressed between men and women (P < 0.05; Figure 7 (A)). The Kaplan–Meier curves for the OS related with AL606489.1 expression in men (low = 109, high = 110) and women (low = 130, high = 130) are depicted in Figure 7 (B) and 7 (C), respectively. In men, the high expression of AL606489.1 and the shorter OS time were associated, whereas the high expression of AL606489.1 in women was not significantly associated with the OS time (P = 0.2704). Finally, to verify whether this discrepancy was attributable to AL606489.1 association with the gender, we noted no significant difference in the overall survival between men and women by the Kaplan–Meier analysis (Figure 7 (D)).

Functional characteristics of 5 OS-related lncRNAs

To determine the possible function of 5 OS-related lncRNAs in the tumorigenic development of LUAD tumors, we conducted an function enrichment analysis on mRNAs co-expressed with OS-associated lncRNAs in 490 LUAD samples. The levels of the 928 mRNA expressions were positively associated with the level of at least one OS-related lncRNA (co-expression coefficient >0.50). The GO analysis indicated that these co-expressed mRNAs were enriched in 52 GO terms (Supplementary Table 3). These GO terms were mainly enriched in regulating the mRNA metabolic processes, RNA splicing, and ubiquitin-specific protease activity (Figure 8 (A)). Similar findings were obtained from the KEGG pathway enrichment analysis (Figure 8 (B)), such as the ubiquitin-mediated proteolysis pathway. Therefore, the characteristics of 5-lncRNA mainly affected the gene expression within the nucleus and may be related to cell cycle regulation.

LUAD is one of the most widely diagnosed subtypes of lung cancer.^[37] Owing to the unknown pathogenesis and unsatisfactory treatment effect, the mortality of LUAD patients remains high.^[38] In recent years, lncRNA has been applied as a potential tumor marker with promising research progress in LUAD.^[39]

In this study, both univariate and multivariate CPHR analyses were performed to establish a 5-lncRNA signature. This model showed high accuracy in both the “entire”and “primary datasets”. In contrast, our prognostic model outperformed the other prognostic features. Risk stratification analysis suggested that our prediction model applied to different subgroups. Finally, we employed GO and KEGG to detect the biological function of our predictive model. Our results seemingly explored how these 5 OS-related lncRNAs are involved in tumor progression. Finally, the lncRNA AL606489.1 showed a possible association with sex dimorphism.

Our prognostic model consisted of 5 LncRNAs, 4 (i.e., AC068228.1, SATB2-AS1, AC026355.1, AL606489.1) of which have been previously reported to be related to the prognosis of LUAD. For instance, SATB2-AS1 has been reported to promote tumor cell growth in osteosarcoma^[40], and NSCLC^[41]. However, in colorectal cancer^[42], SATB2-AS1 has the effect of inhibiting tumor cell metastasis. Similar to our result, AC026355.1 was reported to be an immune-related gene with tumor suppressor effects by Li et al.^[30] In past studies, AL606489.1 has been reported to be associated with autophagy^[43], ferroptosis^[44], and pyroptosis^[32] processes in LUAD tumor cells. LINC 01843 was first shown to be associated with LUAD progression. These reports provide a new direction for gene sequence studies in LUAD.

In the GO and KEGG analysis results, the mRNAs co-expressed with prognostic-associated lncRNAs were associated with processing and RNA transport in the nucleus, such as in the regulation of mRNA metabolic process and the regulation of RNA splicing. Past studies have demonstrated that one of the prognostic-related genes, SATB2-AS1, acts as a miR-299-3p sponge,promoted the development of NSCLC. The underlying mechanism is the promotion of tumor cell proliferation, cell cycle progression, and survival^[41]. Thus, the results of GO and KEGG seem to appropriately reflect the place of action that was associated with prognosis, lncRNA affects the prognostic effect in patients with LUAD.

In the risk stratification analysis, this predictive model showed a slightly better performance in male patients (P< 0.05) than in female patients (P = 0.08), which prompted us to further explore the reasons for this discrepancy.

In our study, AL606489.1 was highly expressed in men relative to that in women. Moreover, on the premise that there is no significant difference in the prognosis between men and women with LUAD, AL606489.1 exhibited high levels of prognostic association in male patients, while showing no significant (P = 0.05) prognostic association in female patients. Therefore, we suggest that AL606489.1 demonstrates a gender dimorphism in terms of the prognostic effects in patients with LUAD.

A person’s gender is one of the key factors affecting the occurrence and development of cancer throughout his or her lifetime. In addition to the sex-specificity of ovarian cancer in women and prostate cancer in men, several tumors are associated with a significant sex bias in terms of incidence^[45], metastatic^[46], prognosis^[46], and therapeutic efficacy^[47]. As the attention to gender differences has increased, gender dimorphism has been mentioned in increasing studies^[33].

In LUAD, sex bias is also associated with patients’ acquired behavior. For instance, Henschke et al. reported that women smoking was associated with a higher risk of lung cancer compared to men smoking among age-matched population^[48].This difference may be attributable to the increased estrogen levels in women that promote lung cancer progression^[49].Multiple studies have demonstrated that sexual dimorphism may be due to differences in the estrogen content between men and women, which develops into different prognostic effects between male and female patients with LUAD. LncRNA LINC00263 has been implicated as an oncogene in men and estrogen by Liu et al^[35].However, the specific role of lncRNA in sex dimorphism has not been well studied. In the present case, AL606489.1 can hence be a breakthrough.

In our study, the action mechanism of AL606489.1 was explored by co-expression analyses. In the co-expression analysis, AL606489.1 was found to be highly correlated with the sarcolemmal membrane-associated protein (SLMAP) expression (correlation coefficient = 0.64) (Supplementary Table 3).A subform of the SLMAP has been reported to be a component of the microtubule (Mt) tissue center^[50].Mts is an important therapeutic target for tumor cells^[51]. Clinically, some compounds that break Mt dynamics are also some of the most effective chemotherapeutics for cancer, such as vincristine alkaloids and taxanes^[52].Similarly, the mt-targeted drugs (MTDs) form a major family of anticancer drugs with anti-mitotic and antiangiogenic properties that inhibit tumor progression, mainly by changing the Mt dynamics of the tumor and endothelial cells^[53].However, there are no reliable markers that can be used for the prediction of the development of cancer sensitivity and resistance during treatment. In this study, AL606489.1 was found to be highly co-expressed with SLMAP and highly correlated with LUAD prognosis, indicating its potential as a reliable marker. Alternatively, the differential expression of AL606489.1 in men and women may be responsible for the clinical emergence of sex-differential efficacy of anticancer drugs that disrupt the Mt dynamics^[54].

The limitations of the present study include the lack of external validation considering that the lncRNAs required in this study were inaccessible in the GEO database. Second, as RNA testing in the TCGA database is constantly updated, this study is slightly sample-limited. Finally, the preliminary conclusion that AL606489.1 demonstrates sexual dimorphism, as derived in this study, needs to be further validated through in vitro and in vivo biological experiments, if the external conditions support it.

Our 5-lncRNA signature (composed of AC068228.1, SATB2-AS1, LINC01843, AC026355.1, and AL606489.1) could effectively predict the OS of LUAD patients, indicating its positive role in early screening and prognosis prediction of LUAD. Moreover, AL606489.1 demonstrated gender dimorphism, thereby providing a new direction for mechanistic studies on sexual dimorphism.

LUAD, lung adenocarcinoma; lncRNAs, long noncoding RNAs; OS, overall survival; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; TCGA, The Cancer Genome Atlas; DEL, differentially expressed lncRNA; FDR, False Discovery Rate; CPHR, Cox proportional hazards regression; AIC, Akaike information criterion; ROC, receiver operating characteristic; AUC, area under the time-dependent ROC curve; HR, hazard ratio; CI, confidence interval.

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable

Availability of data and materials

The datasets presented in this study can befound in TCGA (https://portal.gdc.cancer.gov/), which belongs to publicdatabases. The retrieval strategy for obtaining the sample gene data is “Primary Site IS bronchus and lung AND Project Id IS TCGA-LUAD AND Workflow Type IS HTSeq - FPKM AND Data Category IS transcriptome profiling AND Data Type IS Gene Expression Quantification AND Experimental Strategy IS RNA-Seq”. The retrieval strategy for obtaining the sample clinical data is “Primary SiteISbronchus and lungANDProgram NameISTCGAANDProject IdISTCGA-LUADANDData CategoryISclinical”. The patients involved in the database have obtained ethicalapproval. Users can download relevant data for free for research and publishing relevant articles.

Competing interests

Our study is based on the opensource data, so there is no ethical issues and other conflicts of interest.

Funding

Not applicable.

Authors' contributions

JLperformed the statistical analysis and wrote the initial draft. WJ and HX provided advice and reviewedthe manuscripts. All authors read and approved the final manuscript.

Acknowledgements

The authors gratefully acknowledge the free online resources from TCGA.

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No competing interests reported.

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An Efficient Five-lncRNA Signature for Lung Adenocarcinoma Prognosis and Its Overall Survival Prediction

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Abstract

Background

Methods

Results

Conclusion

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Introduction

Materials And Methods

Data sources

Isolation of differentially expressed lncRNA (DEL)

Isolation of Overall survival (OS)-related lncRNAs in LUAD patients

Calculation and evaluation of the OS-related lncRNA signature

Identification of a prognostic lncRNA with gender dimorphism

Functional enrichment analyses with co-expressed mRNA

Statistical analysis

Results

Candidate OS-related lncRNAs in LUAD patients

Identification and evaluation of an OS-related lncRNA signature to predict the OS

Discussion

Conclusion

Abbreviations

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References

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