Abstract
Background
Little data is available on prognostic biomarkers and effective
treatment options for Kidney Renal Papillary Cell Carcinoma (KIRP)
patients, to find potential prognostic biomarkers and new targets was
an urgent mission for KIRP therapy.
Methods
The differentially expressed autophagy-related genes (DEARGs) were
screened out according to the RNA sequencing data in The Cancer Genome
Atlas database, then identified survival-related DEARGs to establish a
prognostic model for survival predicting of KIRP patients. Then we
verified the robustness and validity of the prognostic risk model
through clinicopathological data. At last, we evaluate the prognostic
value of genes that formed the prognostic risk model individually.
Results
We analyzed the expression of 232 autophagy-related genes (ARGs) in 289
KIRP and 32 non-tumor tissue cases, and 40 mRNAs were screened out as
DEARGs. The functional and pathway enrichment analysis was done and
protein–protein interaction network was constructed for all DEARGs. To
sift candidate DEARGs associated with KIRP patients’ survival and
create an autophagy-related risk prognostic model, univariate and
multivariate Cox regression analysis were did separately. Eventually 3
desirable independent prognostic DEARGs (P4HB, NRG1, and BIRC5) were
picked out and used for construct the autophagy-related risk model. The
accuracy of the prognostic risk model for survival prediction was
assessed by Kaplan–Meier plotter, receiver-operator characteristic
curve, and clinicopathological correlational analyses. The prognostic
value of above 3 genes was verified individually by survival analysis
and expression analysis on mRNA and protein level.
Conclusions
The autophagy-related prognostic model is accurate and applicable, it
can predict OS independently for KIRP patients. Three independent
prognostic DEARGs can benefit for facilitate personalized target
treatment too.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12885-021-08139-2.
Keywords: Kidney renal papillary cell carcinoma, Prognostic risk
signature, Autophagy-related genes, Survival prediction, Targeted
therapy
Background
Renal cell carcinoma (RCC) is the sixth/eighth most common tumor in men
and women. About 73,750 new cases of RCC and 14,830 RCC-related deaths
happened yearly in the United States [[29]1, [30]2]. Kidney Renal
Papillary Cell Carcinoma (KIRP) is the second most commonly diagnosed
subtype of RCC which accounting for 15–20% of RCC cases, and the most
common subtype is clear cell renal carcinoma (ccRCC) [[31]3, [32]4]. It
has been widely assumed that KIRP has a significantly better prognosis
than ccRCC in organ-confined stages [[33]5, [34]6]. Most KIRP were
manifested as localized disease and treated by partial nephrectomy, but
substantial numbers of patients will eventually relapse [[35]7]. About
1/3 of KIRP were manifested as metastatic KIRP (m-KIRP) [[36]8]. Most
m-KIRP patients needed systemic treatment eventually. In general,
m-KIRP had a worse prognosis than metastatic ccRCC [[37]9, [38]10]. As
most KIRP patients had a good prognosis before metastasis, and m-KIRP
patients are not so many in contrast to ccRCC, there is little data on
the efficacy of available treatment options and few prognostic
molecular markers have been discovered. Therefore, there is a need to
explore potential prognostic markers and new molecular targets for KIRP
therapy.
Traditionally, the TNM stage has been used to evaluate the risk of
tumor recurrence for all RCC subtypes. However, it has limited accuracy
[[39]11]. Now, some prognostic factors such as grade and pathology
stage are also used to evaluate the prognosis. However, these
prognostic models were often established for ccRCC only or all RCC
subtypes [[40]12, [41]13]. Therefore, there is a need to refine the
prognostic risk model of KIRP and build a more accurate approach for
managing this second commonest subtype of RCC.
Autophagy is a non-specific, lysosome-mediated degradation. The process
is beneficial for cells internally break down, clearance of damaged or
superfluous proteins, and recycle cellular components.
Autophagy-Related Genes (ARGs) participates in autophagy, for example,
ATG7 involved in energy metabolism is an ARG. Lots of researchers
proved that autophagy is associated with the progress of RCC
[[42]14–[43]16]. For example, inhibiting autophagy in RCC increases the
efficacy of many therapies [[44]17, [45]18]. However, whether the
expression level of ARGs has prognostic value is unknown. Hence, this
research utilized ARGs to establish the prognostic risk model of KIRP.
In this study, the relevance between differentially expressed
autophagy-related genes (DEARGs) and clinicopathological parameters in
321 KIRP patients from TCGA database were examined, and an
autophagy-related risk prognostic model was constructed as an
independent predictor for overall survival of KIRP patients. We
verified our risk score model from several aspects and confirmed it’s
available, and we hope to provide more helpful guidance for evaluated
prognosis and targeted treatment of KIRP with this novel prognostic
risk model.
Methods
Data acquisition
There are 232 genes presently known associated with autophagy were
downloaded from HADb (Human Autophagy Database,
[46]http://autophagy.lu/). The RNA-seq data and the corresponding
clinical data of 289 Kidney Renal Papillary Cell Carcinoma (KIRP)
patients and 32 non-tumor samples were obtained from TCGA database (The
Cancer Genome Atlas database,
[47]https://www.cancer.gov/about-nci/organization/ccg/research/structur
al-genomics/tcga).
DEARGs screening
The differentially expressed autophagy-related genes (DEARGs) between
KIRP tissues and adjacent non-tumor tissues were identified by the
Wilcoxon Rank Sum test. The filtering criteria were |log[2]FoldChange|
(|log[2]FC|) > 1 and false discovery rate (FDR) < 0.05.
Pathway enrichment analysis and Functional annotation for all DEARGs
To reveal the involved pathways and biological function of DEARGs, we
performed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and
Gene Ontology (GO) analysis with the clusterProfiler package of R
(version 4.0.1), and p-value < 0.05 was used as a strict cutoff.
Protein–protein interaction (PPI) networks construction
The functional protein–protein interaction (PPI) analysis is performed
by STRING database (Search Tool for the Retrieval of Interacting Genes,
[48]https://string-db.org/) for all DEARGs. The cut-off criterion of
interaction score is 0.4. We make use of the Cytoscape software to
search hub genes and achieve two-dimensional (2D) visualization of PPI
networks.
Identify the prognostic DEARGs
To identify the prognostic DEARGs whose expression profiles had notable
correlation with the overall survival (OS) of patients with KIRP, the
univariate Cox regression model was constructed. P < 0.05 is the
threshold. These selected DEARGs were regarded as candidate genes had a
correlation with patients’ survival.
Prognostic risk model construction and risk score calculation
The prognostic DEARGs identified by make use of the univariate Cox
regression analysis were subjected to a multivariate Cox proportional
hazards model to remove the genes that might not be an independent
indicator in prognosis predicting. After that, several optimal
independent survival-related DEARGs were obtained and the risk score
composed of expression value of these genes was established. We
calculated the risk score for each patient utilizing the regression
coefficients of the individual DEARGs obtained from the multivariate
Cox hazards model and the expression value of each of the selected
DEARGs.
[MATH: The risk score=∑i=1,2
,…,nregression
coefficientgenei×expression
value ofgenei :MATH]
The risk score was calculated based on a linear combination of the
relative gene expression level multiplied regression coefficients. The
regression coefficients are obtained from the multiple Cox analysis and
represents the relative weight of the genes. The risk score is a
measure of prognostic risk for KIRP patients. Patients were divided
into 2 groups by the median risk score as the critical value. High-risk
score group had worse prognosis than low-risk score group.
Assessment of prognostic risk model
To verify the robustness and validity of the prognostic risk model, we
plotted the survival curves and assessed the differences in the
survival rates between high-risk and low-risk groups using the log-rank
test. Then, we evaluated the survival prediction accuracy of the
prognostic risk model using receiver-operator characteristic (ROC)
curve. The area under the curve (AUC) of ROC curve is a discrimination
criterion, it ranges from 0.5 to 1.0, the higher the value, the more
accurate the model.
To explore whether the autophagy-related prognostic risk model could be
an independent predictor of OS not rely on other clinicopathological
parameters, we performed cox proportional hazard regression analysis.
The association between risk score and clinical traits were explored.
To validate the prognostic value of the risk score model, we took age,
sex, pathological stage, tumor grade and T classification (lymphatic
metastasis excluded) as candidate risk factors for univariate and
multivariate Cox regression analyses.
Evaluation of the prognostic value of 3 prognostic-related DEARGs
As described above, to validate the availability of the prognostic risk
score model, we compared the survival differences between high-risk
group and low-risk group, which grouping is based on risk scores.
Afterwards, we studied the association between the expression level of
3 prognostic-related DEARGs and KIRP patients’ survival individually.
KIRP patients’ survival data in TCGA were used for Kaplan–Meier
survival analyses.
The expression of 3 independent prognostic-related DEARGs (P4HB, NRG1
and BIRC5) were compared and validated between normal kidney tissues
and KIRP tissues in mRNA level and protein level. The mRNA expression
of 3 independent prognostic-related DEARGs in kidney and KIRP tissue
was analyzed utilizing cancer profiling database called Oncomine
([49]https://www.oncomine.org/resource/main.html). The protein level of
3 independent prognostic-related DEARGs on kidney and KIRP tissue were
obtained from The Human Protein Atlas database
([50]https://www.proteinatlas.org/).
Single-gene gene set enrichment analysis (GSEA) for 3 prognostic-related
DEARGs
To explore the roles of 3 prognostic-related DEARGs in KIRP, GSEA was
performed on these genes, respectively. We make use of the KEGG gene
sets biological process database (version c2.KEGG.v4.0) to do GSEA. The
database was affiliated with the Molecular Signatures Database (Msig
DB, [51]http://www.broad.mit.edu/gsea/msigdb/index.jsp). We exhibited
10 signal pathways containing top 5 up-regulated and top 5
downregulated signal pathways respectively, with p < 0.05 as a cutoff
criterion. It’s worth noting that, if up-regulated pathways less than
5, we exhibit more downregulated ones instead.
Results
Identification of DEARGs
We downloaded the mRNA sequencing data and corresponding clinical data
of 289 KIRP tissue samples and 32 normal kidney samples from TCGA
database. The gene expression profile and clinical follow-up
information of 265 KIRP patients were involved in our subsequent
analysis. We extracted expression profile of 232 ARGs. Finally, 40
DEARGs were screened out, involved 31 upregulated ARGs and 9
downregulated ARGs with |log[2] (FoldChange)| > 1 and FDR < 0.05 as
filter criteria. The flow chart of the overall procedures in this
manuscript was showed in Supplementary Fig. [52]1.
In Fig. [53]1a, the volcano plot exhibited the distribution of all
DEARGs. X-axis of the volcano plot is log[2]FoldChange and Y-axis
represents false discovery rate. The fold change patterns of 40 DEARGs
in 32 non-tumor tissues and 289 KIRP tissues were showed in a heat map
in Fig. [54]1b. The scatter plots in Fig. [55]1c visualized expression
of 40 DEARGs between KIRP and normal tissues. In Table [56]1, we
provide a detailed source of information on all DEARGs, including
log[2]FoldChange and statistical significance.
Fig. 1.
[57]Fig. 1
[58]Open in a new tab
Identification of differentially expressed autophagy-related genes
(DEARGs) from KIRP tissues and non-tumor kidney specimens. a Volcano
plot of 242 autophagy-related genes (ARGs). Up-regulated genes are
marked red, they are DEARGs which |Log[2]FoldChange| > 1.0 in mRNA
level; Down-regulated genes are marked green and they are also DEARGs
whose |Log[2]FoldChange| > 1.0. The genes whose |Log[2]FoldChange| ≤1.0
are pained black. b Heatmap of the expression levels of 40 DEARGs in
KIRP. The color depth represents the intensity of the gene expression
level. KIRP, Kidney Renal Papillary Cell Carcinoma. c Box plot of 40
DEARGs’ expression. Red box and green box represent KIRP or non-tumor
specimens respectively
Table 1.
All DEARGs, screened between normal kidney tissues and KIRP tissues
with criteria of FDR < 0.05 and | log[2]FoldChange| > 1
gene Log[2]FC p-Value FDR
PINK1 −1.256 7.74E-19 3.91E-17
IKBKE 1.232429 4.06E-14 4.83E-13
WIPI1 1.107571 8.71E-13 7.33E-12
FAS 1.400674 1.10E-10 4.72E-10
EIF4EBP1 1.228878 1.65E-10 6.54E-10
CDKN2A 4.530332 4.06E-18 1.27E-16
P4HB 1.112831 1.86E-18 7.52E-17
FOS −1.54152 7.37E-12 4.38E-11
PTK6 1.687081 0.000217 0.000362
CDKN1A 1.314733 8.48E-12 4.89E-11
RGS19 1.188735 1.46E-11 7.55E-11
SPHK1 2.867518 5.69E-13 5.23E-12
MYC 1.052152 9.64E-05 0.000171
NPC1 1.13245 5.33E-12 3.26E-11
TMEM74 −1.66969 1.35E-17 2.73E-16
ITGB4 1.32962 7.00E-07 1.72E-06
CASP1 1.236902 5.37E-10 2.01E-09
GRID2 −1.9432 3.12E-20 3.16E-18
FAM215A −4.04652 1.22E-20 2.47E-18
MAP1LC3C 2.597344 1.42E-05 2.86E-05
TP73 2.60006 3.76E-13 3.62E-12
FOXO1 −1.2677 5.67E-18 1.27E-16
PRKN −1.66938 5.48E-18 1.27E-16
CXCR4 1.496494 2.12E-07 5.50E-07
VMP1 1.752259 9.97E-15 1.34E-13
BAX 1.637643 1.35E-19 9.07E-18
DLC1 −1.28116 3.98E-16 5.75E-15
TNFSF10 1.281284 1.68E-06 4.09E-06
CASP4 1.024723 9.65E-13 7.80E-12
SERPINA1 1.359796 0.001698 0.002559
HSPB8 2.045846 1.16E-16 1.94E-15
GAPDH 1.105955 7.44E-17 1.37E-15
NRG1 1.062555 0.000569 0.000906
NRG3 1.341739 6.02E-05 0.000109
ITGA3 1.785065 2.48E-13 2.50E-12
BIRC5 2.995663 3.85E-16 5.75E-15
ATG16L2 1.310419 2.36E-08 6.70E-08
DRAM1 1.054383 1.18E-11 6.47E-11
APOL1 1.705075 1.93E-09 6.62E-09
ITPR1 −1.86234 5.02E-18 1.27E-16
[59]Open in a new tab
PPI network establishment and function annotation of all DEARGs
The interaction of all DEARGs were visualized in Fig. [60]2a, and there
are 12 hub genes arranged in a circle are DEARGs with interaction
degree > 15. Biological processes (BP) annotation reminded us that
DEARGs had a strong association with enzyme-related process, such as
autophagy, regulation of peptidase activity and macroautophagy. In the
aspect of the molecular function (MF), DEARGs seems played vital roles
in some protein binding related functions, for example, peptidase
regulator activity, ubiquitin protein ligase binding and protease
binding. Regarding the cellular components (CC), the DEARGs encoded
proteins constituted autophagosome membrane, vacuolar membrane,
endoplasmic reticulum-Golgi intermediate compartment and so on (Fig.
[61]2b). In Fig. [62]2c, the mainly pathways that had a positive
relation with screened DEARGs was showed, including hepatitis B,
pathogenic Escherichia coli infection, human cytomegalovirus infection
and Kaposi sarcoma-associated herpesvirus infection. Z-scores of
enriched pathways were all > 0, it means that all the pathways were
more likely to be enhanced.
Fig. 2.
[63]Fig. 2
[64]Open in a new tab
PPI network construction and functional annotation for 40 DEARGs. a PPI
network of 40 DEARGs. The color depth of nodes is based on
log[2]FoldChange, red nodes and green nodes denote up-regulated DEARGs
and down-regulated DEARGs respectively. The width of links between
nodes is positively correlated to combined score of protein
interaction. Nodes’ size is inversely related to p-value. Square nodes
are hub genes had most interactive protein, the number of interactive
protein is > 6. b Gene Ontology enrichment analysis for 40 DEARGs. c
KEGG pathway analyses for 40 DEARGs shows the top 10 signaling pathways
that 40 DEARGs involved in
Establishment of autophagy-related risk signature
To identify the relationship between the expression of 40 DEARGs and
overall survival in KIRP patients, we constructed the univariate Cox
proportional hazards model. The results showed that there are 14 DEARGs
significantly related to the prognosis of KIRP patients (p < 0.05)
(Fig. [65]3a). In order to raise the robustness, the screened 14
prognostic-related DEARGs were further included in the subsequent
multivariate Cox regression analysis. At last, 3 DEARGs (P4HB, NRG1 and
BIRC5) were filtered out and used for autophagy-related risk model
construction (Fig. [66]3b). The risk score for each patient was
calculated according to the following formula: risk score = (0.8658×
expression value of P4HB) + (0.3379× expression value of
NRG1) + (1.1201× expression value of BIRC5). Patients were divided into
high-risk (n = 132) and low-risk group (n = 133) by the median risk
score as the critical value. The risk score was calculated for each
patient and list of they belong to low or high-risk group
(Supplementary Table [67]1).
Fig. 3.
Fig. 3
[68]Open in a new tab
Identify survival related autophagy genes in KIRP patients and
development of prognostic model. We make use of univariate and
multivariate cox model to filtered out DEARGs whose expression had
positive relation with KIRP patients’ survival. a 14 DEARGs are
associated with survival of KIRP patients according to univariate cox
model. b 3 DEARGs are significantly related to survival of KIRP
patients based on multivariate cox model, we exhibited the regression
coefficients and p values
Validation of the risk signature
To measure the accuracy of the autophagy-related risk model to predict
the prognosis of KIRP patients, we draw Kaplan-Meier plotter to compare
the survival time difference between high-risk and low-risk group.
Low-risk group patients had more survival probability (p = 4.406E-05)
(Fig. [69]4a). Next, the ROC curves were employed to determine the
predictive performance of the prognostic risk model. In Fig. [70]4b
showed, the AUC value of risk score was 0.923, it was larger than AUC
values of other indicators except for the pathologic stage, which
confirmed that the autophagy-related prognostic model is an excellent
and independent prognostic predictor comparing with other
clinicopathology indicators. The risk scores of all KIRP patients were
visualized from small to large (Fig. [71]4c). With the increase of the
risk score, the death number of KIRP patients is more (Fig. [72]4d).
The expression patterns of 3 prognostic-related DEARGs in different
risk groups was exhibited in the heatmap (Fig. [73]4e).
Fig. 4.
[74]Fig. 4
[75]Open in a new tab
Validation of the prognostic model. a Kaplan-Meier plotter showed the
survival probability in different risk group for KIRP patients. b ROC
curves evaluated the accuracy of DEARGs-based risk scores for prognosis
predicting. c Scatter plot of KIRP patients distributed as risk scores
increase. d Scatter plot exhibited the survival status of KIRP patients
as risk scores increase, red plots and green plots represents
non-survivors and survivors. e Heatmap of 3 prognostic DEARGs which
composed prognostic model as risk scores increase
Clinical verification of prognostic model
To determine the relationship between the autophagy-related prognostic
risk model and clinicopathological features in KIPR patients, we put
several familiar clinicopathological factors and risk score to do
univariate and multivariate cox regression analyses (Fig. [76]5). Since
AUC values are often used to assess the performance of an individual
clinicopathological indicator, and the larger of the AUC value, the
more accurate of the indicator to predict prognosis. In our study, the
AUC values of the clinicopathological features including age and sex to
predict OS is less than 0.5 (Fig. [77]4b), demonstrated that age or sex
alone was unable to predict prognosis as an individual indicator. The
relationship between risk scores and age/sex was listed in Fig. [78]5a
and Fig. [79]5b. No difference in risk score was observed between elder
patients and younger patients (Fig. [80]5a). Instead, the AUC values of
the clinicopathological features (pathological stage) and risk score is
more than 0.9 (Fig. [81]4b), illustrated that no matter pathological
stage or risk score can make a comparatively accurate prediction KIRP
patients’ prognosis. Risk scores were lower in pathological stage I
than in pathological stage II-IV (p = 6.676e-05) (Fig. [82]5c), and
lower in T classification T1–2 than in T3–4 (p = 5.622e-04) (Fig.
[83]5d). The relationship between the expression of 3 genes composed
the risk model and 4 clinicopathological features in KIRP are exhibited
in Fig. [84]5a, b, c, d. The raw TCGA data that containing basic
information of all KIRP patients was listed in Supplementary
Table [85]2. In Table [86]2, pathological stage, T classification, and
risk score had obviously positive correlation with prognosis of KIRP
patients in univariate Cox analysis, in addition, risk score and
pathological stage were independent prognostic predictor of KIRP
patients in multivariate Cox analysis. All above results demonstrate
that the autophagy-related risk signature can be an excellent
prognostic predictor ifor KIRP patients.
Fig. 5.
[87]Fig. 5
[88]Open in a new tab
Clinical correlations between clinicopathological variables and the
risk score. The relationship between expression of 3 prognostic DEARGs
and clinicopathological features was also showed here. a Grade. b sex.
c pathological stage. d T classification
Table 2.
Univariate and multivariate cox regression analyses of riskscore and
clinicopathologic features in the TCGA group KIRP patients
Variables Univariate analysis Multivariate analysis
HR (95% CI) p-Value HR (95% CI) p-Value
RiskScore 1.041(1.028–1.054) < 0.001 1.020(1.004–1.037) 0.016
Age 0.986(0.956–1.016) 0.360 0.985(0.954–1.016) 0.342
Sex 0.694(0.320–1.505) 0.355 1.248(0.499–3.123) 0.636
Pathologic Stage 3.430(2.352–5.003) < 0.001 3.673(2.131–6.330) < 0.001
T classification 2.844(1.920–4.210) < 0.001 0.786(0.430–1.438) 0.435
[89]Open in a new tab
Validation of the function of 3 prognostic-related DEARGs in KIRP
According to our results, 3 prognostic-related DEARGs including P4HB,
NRG1 and BIRC5 were identified to develop the survival-related risk
prognostic model. We analyzed the correlation between 3
prognostic-related DEARGs that composed the prognostic model and
survival probability of KIRP patients. The results of the Kaplan-Meier
analysis showed that the upregulation of P4HB was obviously associated
with the low survival probability of KIRP patients. Also, NRG1 or BIRC5
overexpression leads to worse OS (Fig. [90]6a). To further compare the
expression difference of 3 prognostic-related DEARGs between KIRP and
normal tissues, we performed a clinical study using cancer microarray
database of Oncomine and human proteome database Human Protein Atlas.
The mRNA level of 3 prognostic-related DEARGs was verified and showed
in Fig. [91]6b. The expression trend of 3 prognostic-related DEARGs is
in accordance with our previous results obtained from TCGA database
which showed in Fig. [92]1. In Fig. [93]7a, results of
immunohistochemistry (IHC) confirmed the expression of P4HB, NRG1 and
BIRC5 protein are stronger in KIRP tissues than normal kidney tissues.
Single-gene GSEA of the 3 prognostic-related DEARGs explored the
potential roles of 3 prognostic DEARGs in KIRP (Fig. [94]7b).
Fig. 6.
[95]Fig. 6
[96]Open in a new tab
The correlation between 3 genes (P4HB, BIRC5 and NRG1) identified in
prognostic signature and KIRP patients’ survival (a). Comparing
expressions of 3 genes (P4HB, BIRC5 and NRG1) between normal kidney and
KIRP tissues in Oncomine database (b)
Fig. 7.
[97]Fig. 7
[98]Open in a new tab
Immunohistochemistry analysis of 3 genes (P4HB, NRG1 and BIRC5) which
used to develop the risk prognostic model (a). P4HB protein is detected
by antibody CAB012463 and HPA018884. BIRC5 protein is detected by
antibody HPA002830. Immunohistochemistry of NRG1 in database of The
Human Protein Atlas is absence. b Results of single-gene GSEA of 3
prognostic DEARGs which composed the risk signature in KIRP
Discussion
Autophagy is a dynamic and conserved process that can maintain cellular
homeostasis [[99]19]. Many researchers had proved autophagy played an
important function in cancer [[100]20–[101]22]. Some targeted agents
aimed at autophagy had been applied in the clinics in RCC patients
[[102]23–[103]25]. Therefore, we decided to develop an
autophagy-related prognostic risk model for prognosis predicting of
KIRP patients.
The autophagy-related prognostic model is comprised of 3 genes,
including P4HB, BIRC5, and NRG1. The 3 genes are all closely relevant
to clinicopathological features of KIRP patients, the expression of
them is either correlated with pathological stage or T classification
of KIRP patients (p < 0.05). The risk score obtained according to the
prognostic model is also significantly associated with
clinicopathological features of KIRP patients. Several assessment
methods confirmed the autophagy-related signature can be an
independently prognosis predictor for KIRP patients. Individual
assessment of the function of 3 prognostic-related DEARGs in KIRP
further proof P4HB, BIRC5, and NRG1 are up-regulated in KIRP, and
overexpression of them is associated with worse survival of KIRP
patients. All results proved that risk signature constructed through
P4HB, BIRC5, and NRG1 to evaluate the prognosis of KIRP patients is
clinically practicable.
P4HB encoded protein disulfide isomerase, it is an autophagy-related
gene, Xie et al proved that P4HB was a novel biomarker for ccRCC
diagnosis and prognosis predicting [[104]26]. BIRC5 also called
survivin, the encoding products of BIRC5 play a significant role in
negative regulation of apoptosis or programmed cell death. Philipp et
al demonstrated that BIRC5 is of importance for renal pathophysiology
and pathology [[105]27]. NRG1 is a growth factor of the epidermal
growth factor family, the relationship between NRG1 and RCC had not
been explained clearly, but Sushma et al thought that NRG1 fusion is a
low frequency event in most tumor types, including RCC [[106]28]. We
found that most of prognostic molecular indicators and therapeutic
targets were identified and verified in ccRCC only or all RCC subtypes,
there is an urgent need to explore possible prognostic molecular
indicators and novel targets for KIRP therapy.
Functional enrichment analysis exhibited that 40 DEARGs were mainly
involved in infection related pathway. It is generally acknowledged
that infection and inflammation are all correlated with carcinogenesis.
Kaymakcalan et al reported that RCC patients treated with mTOR
inhibitors had a risk of infection [[107]29], Alexander et al reported
that urinary tract infection history is positively associated with RCC
development [[108]30]. Hence, the affection of infection to KIRP
patients should be carefully assessed and managed.
This research constructed a novel prognostic risk model for prognosis
predicting of KIRP patients, and proved it is steady and credible by
verification with molecular signature combined with clinical features.
Several assessment methods confirmed the prognostic risk model is
obviously an independently prognosis predictor for KIRP patients. We
believe, apart from traditional clinicopathological features (including
pathologic stage, T classification and so on), risk score derived from
the autophagy-related genes signature could also be incorporated into
the clinical evaluation indicators to better predict clinical outcomes.
Individual assessment of the function of 3 prognostic-related DEARGs in
KIRP further proved P4HB, BIRC5, and NRG1 all play significant roles in
KIRP. The 3 prognostic-related DEARGs can benefit personalized target
therapy also. All results proved that the risk score calculated
according to expression level of P4HB, BIRC5, and NRG1 to evaluate the
prognosis of KIRP patients is reliable.
Conclusions
This research analyzed mRNA sequencing data of 289 KIRP tissue
specimens and 32 non-tumor specimens and assessed of 232 ARGs’
expression difference in the two groups. We screened out 9
down-regulated DEARGs and 31 up-regulated DEARGs in KIRP with the
threshold of |log[2]FC| > 1.0 and P < 0.05. From 40 DEARGs, 3
prognostic DEARGs (P4HB, NRG1, BIRC5) were determined to establish a
prognostic risk model, and the risk score was calculated according to
expression of the 3 prognostic DEARGs and fixed regression
coefficients. With verification analysis combined using molecular
signature and clinical characteristics, the risk score for prognosis
predicting of KIRP patients is robustly and accuracy. The genes
identified in autophagy-related prognostic model had been verified, and
they were all correlated with KIRP patients’ prognosis, and they were
all up-regulated in KIRP tissues. What’s more, this research is benefit
for illustrating the molecular mechanisms behind KIRP from a new
perspective.
Supplementary Information
[109]12885_2021_8139_MOESM1_ESM.tif^ (286.4KB, tif)
Additional file 1: Figure S1. The flow chart of the overall process in
our manuscript.
[110]Additional file 2. ^(38.9KB, xlsx)
[111]Additional file 3. ^(222.5KB, xlsx)
Acknowledgements