Mammalian karyopherins (KPNAs) as potential biomarkers of prognosis and immune invasion in gastric cancer

doi:10.21203/rs.3.rs-1498074/v1

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Research Article

Mammalian karyopherins (KPNAs) as potential biomarkers of prognosis and immune invasion in gastric cancer

https://doi.org/10.21203/rs.3.rs-1498074/v1

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Background: Gastric cancer (GC) is among the common malignancies in the world. Studies of the KPNAs proteins’ participation in gastric cancer are still lacking.

Materials and methods: The transcript levels and differential expression relating to tumor stage of KPNAs were analyzed through oncomine dataset and the Gene Expression Profile Interaction Analysis dataset. Experimental validation of mRNA and protein of KPNAs in non-malignant gastric mucosal tissues versus gastric cancer tissues was carried out using RT-qPCR, WB, and IHC. The prognostic value of KPNAs was analyzed using Kaplan-Meier Plotter. The association of KPNAs with immune invasion was assessed via the TIMER dataset. Mutations in the KPNAs family of genes were analyzed using the cBioportal dataset. In addition, inter-protein interactions expressed by the KPNAs family factors were analyzed using the String dataset, GeneMANIA. Finally, R software was adopted to analyze GO/KEGG functional enrichment of the KPNAs family factors.

Results: The study illustrated that the transcript levels of KPNAs factors were elevated in GC tissues, both in mRNA and protein expression. The increased expression level of KPNA1/4/5/7 was linked to poor OS, FP and PPS. Further, it was noted that all KPNAs were linked to invasion of B-cell and the invasion of CD8+ T-cell was linked to KPNA2/3/4 expression. GO/KEGG enrichment assessment data of the KPNAs family estimated the functional role of the target host genes and biological processes showed that the observed changes were mainly cellular components, and molecular functions. Further, the cBioPortal online tool indicated that overexpression of mRNA was the most frequent alteration. Lastly, the String dataset, GeneMANIA dataset revealed that the KPNAs family factors have interactions in nuclear protein import.

Conclusion: The results of the present research work showed that KPNAs might provide novel insights into potential biomarkers of prognosis along with immune invasion in GC.

Bioinformatics analysis

Karyopherins family

Gastric cancer

Immune invasion

Survival

Prognosis

Globally, gastric carcinoma (GC) is the fifth most commonly diagnosed cancer and the fourth leading cause of mortality[1]. The primary method of treating GC is surgical resection combined with chemotherapy, radiotherapy, immunotherapies, multimodality treatment, and other adjuvant treatment which enhance prognosis of the individuals with GC [2]. However, despite the various achievements toward treatment of GC, the survival rate of individuals with the cancer is still low [3–4]. Although diagnosis of GC is particularly important and there are many molecular targets for the cancer, most of the targets lack specificity. Hence, it is still imperative to establish novel biomarkers to enhance GC progression.

The mammalian karyopherins family (KPNAs) contains seven KPNA factors (KPNA1, KPNA2, KPNA3, KPNA4, KPNA5, KPNA6, and KPNA7) involved in nuclear protein import as adapter proteins between the cytoplasm and the nucleus. Further, the KPNAs are suggested to have complex roles in human malignant tumors. Mammalian karyopherin-1 (KPNA1) has been reported to be involved in modulation of nuclear transport of tumor suppressors in breast cancer cells [5]. However, upregulating KPNA2 has been reported to increase the prognosis of GC, colorectal carcinoma, hepatocellular carcinoma, and development of bladder carcinoma [6–9]. In addition, KPNA3 participates in progression of colorectal [10]. Mammalian karyopherin-4 (KPNA4) has been reported to promote tumor progression in papillary thyroid cancer, prostate cancer, and lung adenocarcinoma [11–13]. The KPNA5 and KPNA6 have also been documented to participate in progression of breast cancer [5]. Further, KPNA7 has been documented to promote the malignant properties of pancreatic cancer cells [14]. However, the potential mechanisms by which the mammalian karyopherins family factors (KPNAs) are inhibited or promoted and the role of the factors in GC have not yet to been clarified.

Currently, there is no study to clarify the biological role of KPNAs in GC except for KPNA2. Further, there is no bioinformatics analysis to explore the expression of the genes in gastric cancer, their relationship with clinical progression of GC, and their correlation with immune invasion. Therefore, the present research work aimed to analyze the available gene along with protein expression datasets to study the differential expression of KPNAs in patients with GC as well as their relationship with clinical parameters. The study provided insights into the molecular mechanism of GC and revealed new possible therapeutic targets as well as immune invasion for the disease. The flow chart for this study was as shown in Fig. 1:

Oncomine analysis

The Oncomine online data resource (http://www.oncomine.com) was adopted to analyze expression of KPNAs genes linked to clinical data in 20 common carcinomas [15]. The expression of mRNA for KPNAs factors in different carcinomas specimens was compared with that in normal tissues, using the cut-off standard: Fold Change>1.0 with p=<0.01(Student's t-test), gene rank≤10% to ensure the statistical significance of differnces.

Gene expression profiling interactive analysis (GEPIA) dataset

The GEPIA web resource (http://gepia.cancer-pku.cn/index.html) is an interactive tool with a standard processing pipeline and includes the data of thousands of tumor and non-malignant samples [16]. Herein, the comparison of tumor and non-malignant tissue of the KPNAs, pathological stage correlative analysis were performed using GEPIA.

Kaplan-Meier Plotter

The prognostic analysis of the mRNA expression of the KPNAs factors in individuals with GC was analyzed on the Kaplan-Meier plotter website (http://kmplot.com/analysis/index.php?p=service&cancer=gastric) [17]. The database divided the patients into two groups according to different quantiles of the gene expression. The two cohorts were compared via Kaplan-Meier survival Plotter and the HR, 95% CI as well as log-rank P value (p< 0.05) were calculated.

cBioPortal dataset

The cBioPortal (www.cbioportal.org) offers a visual tool for studying and analyzing cancer gene data and help understand the KPNAs molecular data obtained from cancer tissue as well as cytology research [18]. The database can quickly abtain the molecular spectrum and clinical prognosis correlation of large-scale cancer genomics projects based on the TCGA database as well as transform the rich data sets into visual data for clinical application.

GeneMANIA

GeneMANIA web resource (http://www.genemania.org) constitutes a resource-rich tool and can be used to find related genes including protein-protein and protein-DNA, as well as genetic interaction, protein domain, pathway, physiological along with biochemical reaction, gene and protein expression, and phenotypic screening [19]. Therefore, GeneMANIA website was adopted to explore the predictive value of the KPNAs factors.

String dataset

The STRING dataset (https://string-db.org/, v11.5) is an online data resource, which can search the database of genes, known proteins, and predict interactions between proteins [20]. It was used the database to construct the PPI networks of the KPNAs in the research work to find the difference of expression and potential interactions.

Timer dataset

The TIMER data resource (https://cistrome.shinyapps.io/timer/) is a user-friendly website on which the RNA sequencing expression pattern data were employed to explore the invasion of immune cells in tumor tissues [21]. It provides the invasion of 6 different kinds of immune cells consisting of B cells, dendritic cells, CD4 + T cells, macrophages, CD8 + T cells as well as neutrophils. Expression of KPNAs and the level of immune invasion in GC was visualized through the Timer website dataset.

Statistical analyses

The R (www.r-project.org, v3.6.3, for analysis), ggplot2 package [22] [v3.3.3] (for visualization) were used to analyze the KPNAs family ROC curves in GC. Gene ontology (GO) enrichment along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway assessment of co-expression genes were performed using the “ClusterProfiler” [23] package and then visualization done with the “ggplot2” package.

RNA isolation and quantitative real-time PCR

Isolation of total RNA was done with the Trizol reagent (TIANGEN BIOTECH), reverse transcription process strictly followed the instructions of FastKing RT Kit (TIANGEN BIOTECH, KR116), and SuperReal PreMix Plus (SYBR Green, TIANGEN BIOTECH, FP 205) was employed for qRT-PCR. The KPNAs sequences were as presented in Table 1.

Table 1. KPNAs primer sequence

Gene symbol	Primer	Sequence
KPNA1	F Primer	5’-3’ GAGTGTGACGCATTCTTCGC
KPNA1	R Primer	5’-3’ CAGACATCAGACGGTATCCATCC
KPNA2	F Primer	5’-3’ GTGGGCCGTGACCAACTATAC
KPNA2	R Primer	5’-3’ CATCAACGGTTCTATTATGCCAC
KPNA3	F Primer	5’-3’ GGGTTGGAGGAAGAACTTTAGAT
KPNA3	R Primer	5’-3’ GGAACACACAGGCTGAAGAGAG
KPNA4	F Primer	5’-3’ ATGTCGCCACAAAGACCCAC
KPNA4	R Primer	5’-3’ TCATTGCCAGCATCAGTAAGG
KPNA5	F Primer	5’-3’ ACAACAACAAGAAATGCCGTG
KPNA5	R Primer	5’-3’ AAACCTTACTAAAGTTTGGAGGAGG
KPNA6	F Primer	5’-3’ GATGGGAGTAGAGGAAGTTGGTG
KPNA6	R Primer	5’-3’ TTCAGAGGAAGGAAAAGCAGAG
KPNA7	F Primer	5’-3’ ACATCGCTTCAGGGACTTCG
KPNA7	R Primer	5’-3’ AGCCCACACTGCCTGTTCAC
GAPDH	F Primer	5’-3’ AGAAGGCTGGGGCTCATTTG
GAPDH	R Primer	5’-3’ AGGGGCCATCCACAGTCTTC

Western blot

The cells in a logarithmic growth period were collected, and the protein was isolated with Ripa lysate and quantified by BCA protein. First, it was electrophoresed with SDS-PAGE and then transferred to the PVDF membrane. It was blocked with 3% bovine serum albumin (BSA) at 4^oCovernight, and the primary antibody against KPNAs (1:1000) was added (KPNA1: Rabbit. No:DF6587; Affifinity; KPNA2: Rabbit. No:DF6510; Affifinity; KPNA3: Rabbit. No: bs-16803R; Bioss; KPNA4: Rabbit. No: ab176585; Abcam; KPNA5: Rabbit. No: ab1899987; Abcam; KPNA6: Rabbit. No: 12366-2-AP; Proteintech; KPNA7: Rabbit. No: 12366-2-AP; Proteintech) and GAPDH (1:1000; Cat. No: 2118s; CST), then add the corresponding peroxidase-labeled secondary antibody (1:5000; HRP-labeled Goat Anti-Rabbit IgG (H+L); Beyotime) after 2 hours. The KPNAs protein levels were evaluated using a chemiluminescence detection system according to an electrochemiluminescence (ECL) chromogenic kit.

Immunohistochemistry

The tumor and adjacent tissues were embedded into 5 mm sections, dewaxed, antigen repaired, and sealed. They were added to the primary antibody and stored in the refrigerator at 4^oC overnight. The secondary antibody was then added, DAB was added for color development, hematoxylin restraining, and dehydration sealing. Finally, the panorama was also observed under an optical microscope.

Transcriptional levels of KPNAs in GC patients

Oncomine data resource was used to compare mRNA expression and hence compare the differential expression of different KPNAs in patients with GC (Fig. 2). According to the data obtained from Oncomine, it was found that the transcript levels of KPNA1/2/3/4/6 were remarkably elevated in GC tissues. On the contrary, the transcript levels of KPNA5 and KPNA7 were not remarkably different in GC in contrast with non-malignant tissues (Fig. 2, Table 2).

Table 2

Remarkable changes of KPNAs expression at transcript level between GC and non-malignant gastric tissues (Oncomine)
	Types of GC VS. Normal	Fold change	P Value	t-Test
KPNA1
	Gastric Mixed Adenocarcinoma vs.Normal	1.607	1.63E-4	5.590	Derrico Gastric
KPNA2
	Gastric Intestinal Type Adenocarcinoma vs.Normal	2.573	4.56E-18	11.871	Chen Gastric
	Gastric Mixed Adenocarcinoma vs.Normal	1.846	4.81E-5	5.466	Chen Gastric
	Diffuse Gastric Adenocarcinoma vs.Normal	1.783	1.80E-4	4.305	Chen Gastric
	Gastric Intestinal Type Adenocarcinoma vs.Normal	1.922	7.92E-6	5.185	Cho Gastric
	Gastric Mixed Adenocarcinoma vs.Normal	2.638	3.51E-5	5.356	Cho Gastric
	Diffuse Gastric Adenocarcinoma vs.Normal	1.957	4.05E-6	5.531	Cho Gastric
	Gastric Intestinal Type Adenocarcinoma vs.Normal	2.595	5.34E-12	8.742	Derrico Gastric
	Diffuse Gastric Adenocarcinoma vs.Normal	2.222	9.67E-4	4.161	Derrico Gastric
	Gastric Mixed Adenocarcinoma vs.Normal	3.088	2.02E-4	6.321	Derrico Gastric
	Gastric Cancer vs.Normal	2.014	0.003	3.150	Wang Gastric
KPNA3
	Gastric Mixed Adenocarcinoma vs.Normal	1.849	3.01E-6	1.849	Cho Gastric
KPNA4
	Gastric Intestinal Type Adenocarcinoma vs.Normal	2.057	1.22E-10	8.021	Derrico Gastric
	Diffuse Gastric Adenocarcinoma vs.Normal	1.733	4.99E-4	4.148	Derrico Gastric
	Gastric Mixed Adenocarcinoma vs.Normal	1.573	3.62E-4	1.573	Cho Gastric
KPNA6
	Gastric Intestinal Type Adenocarcinoma vs.Normal	1.736	3.28E-10	1.736	Derrico Gastric
	Gastric Mixed Adenocarcinoma vs.Normal	1.830	7.65E-4	6.202	Derrico Gastric
	Gastric Cancer vs.Normal	1.530	0.002	3.281	Wang Gastric
GC: gastric cancer KPNA:karyopherin subunit alpha

The differences in the transcript expression of KPNAs between GC and non-malignant gastric tissues were also identified by reviewing the GEPIA dataset. Data of this research work illustrated that the expression of KPNA1/2/3/4 was higher in GC tissues in contrast with non-malignant tissues. The expression of KPNA5/6/7 was not remarkably different between the GC and normal gastric tissues (Fig. 3). Furthermore, the results of the current study were similar to those of oncomine.

The present study used real-time qPCR (RT-qPCR), western blot, and immunohistochemistry assays of cancer and paraneoplastic tissues to validate the expression differences of KPNAs in GC. Results of RT-qPCR showed significant differences in KPNA6 in tumor and normal tissues, whilst the rest of the cell lines were of low expression (Fig. 4(A)). Data of the western blotting illustrated that there were remarkable differences in KPNA2, KPNA3, and KPNA7, whereas the rest of the protein amounts were not remarkably different (Fig. 4(B)). Immunohistochemical histochemistry assay revealed that there were remarkable differences in the expression of KPNA4, KPNA6, and KPNA7 in cancer and paraneoplastic tissues. Generally, results of the described experiments were similar (Fig. 4(C)). Therefore, it was evident that the expression of transcript along with protein of the KPNAs family is up-regulated in GC.

Prognostic value of the KPNAs family factors in individuals with GC

Kaplan Meier plotter database was adopted to explore the prognostic significance of KPNAs gene expression in GC. The increased expression level of KPNA1/4/5/7 was linked to poor OS (overall survival), PFS (progression-free survival), as well as PPS (post-progression survival) (p < 0.05) in individuals with GC, whereas the increased expression level of KPNA2/3/6 was linked to better OS, FP and PPS (Fig. 5).

The correlation between the expression of KPNAs and the pathological stage of individuals with GC was also evaluated in the current study. The KPNA5 group remarkably differed, whereas the KPNA1/2/3/4/6/7 groups were comparable to each other (Fig. 6). Therefore, results of this research work suggest that KPNA5 may play an essential role in tumor growth metabolism of GC.

Immune invasion of the KPNAs in individuals with GC

Level of immune cells in the body is linked to the proliferation and progression of cancer cells. The TIMER database was used to investigate the correlation of the KPNAs family protein factors with immune cell invasion in patient with GC (Fig. 7).

It was observed that all KPNAs in patients with GC were positively linked to B-cell invasion. Further, the CD8 + T-cell invasion was positively linked to expression of KPNA2/3/4. With exception of KPNA7 the expression of KPNAs protein factors was positively linked to CD4 + T cell invasion. Besides, it was found that the expression of KPNA1/2/4/5/6 was positively linked to macrophage invasion. Moreover, it was found that neutrophil invasion was also positively linked to the expression of KPNA2/3, whereas the dendritic cell invasion was only positively linked to the expression of KPNA2/3/5.

Genetic alteration, co-expression, and GO&KEGG analyses of KPNAs in individuals with GC

The TCGA database of 1213 individuals with GC and 255 (21%) genetic mutations in KPNAs using the cBioPortal online tool. In the current study, it was found that KPNAs have different genetic alterations, consisting of inframe mutation, missense mutation, truncating mutation, amplification along with deep deletion, and mRNA overexpression was the most frequent alteration (Fig. 8(A)). It was found that KPNA5 was the most (3%) mutated gene in the KPNAs family whereas KPNA7 was the most (7%) frequently mutated gene. Further, it was found that amplification was the most frequent kind of genetic alterations among all KPNAs.

Furthermore, PPI network evaluation of differentially expressed KPNAs was performed in the current study using STRING to determine the possible cross talks between them. In the result analysis, 7 nodes, 15 edges were found (Fig. 8(B)). Gene ontology enrichment analysis of the KPNAs family by R software predicted the functional role of the target host genes (Fig. 8(C)), and the biological processes. It was found that the changes were mainly, cellular components along with molecular functions.

The present research work found biological processes of KPNAs in regulation by virus of the host physiology, or morphology, modification via symbiont of host morphology or physiology, NLS-harboring protein import into nucleus, regulation via virus of host process, and modulation via symbiont of host cellular process. Further, it was noted that the changes in cellular components were mainly focused on host cell. Regarding molecular function, the changes were primarily in nuclear import signal receptor activity, nucleocytoplasmic carrier activity along with molecular carrier activity, nuclear localization sequence docking and signal sequence docking (Table 3).

Table 3

GO and KEGG analysis of KPNAs in patients with GC.
ONTOLOGY	ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue
BP	GO:0019048	modulation by virus of host morphology or physiology	7/7	39/18670	9.82e-20	7.75e-18	2.48e-18
BP	GO:0044003	modification by symbiont of host morphology or physiology	7/7	46/18670	3.42e-19	1.35e-17	4.31e-18
BP	GO:0006607	NLS-bearing protein import into nucleus	6/7	18/18670	2.21e-18	5.82e-17	1.86e-17
BP	GO:0019054	modulation by virus of host process	6/7	25/18670	2.11e-17	4.16e-16	1.33e-16
BP	GO:0044068	modulation by symbiont of host cellular process	6/7	29/18670	5.65e-17	8.93e-16	2.86e-16
CC	GO:0018995	host	4/7	73/19717	6.00e-09	2.98e-08	1.41e-08
CC	GO:0043657	host cell	4/7	73/19717	6.00e-09	2.98e-08	1.41e-08
CC	GO:0044215	other organism	4/7	77/19717	7.46e-09	2.98e-08	1.41e-08
CC	GO:0044216	other organism cell	4/7	77/19717	7.46e-09	2.98e-08	1.41e-08
CC	GO:0044217	other organism part	4/7	77/19717	7.46e-09	2.98e-08	1.41e-08
MF	GO:0061608	nuclear import signal receptor activity	7/7	20/17697	7.20e-22	6.48e-21	7.57e-22
MF	GO:0140142	nucleocytoplasmic carrier activity	7/7	31/17697	2.44e-20	1.10e-19	1.28e-20
MF	GO:0140104	molecular carrier activity	7/7	54/17697	1.64e-18	4.93e-18	5.77e-19
MF	GO:0008139	nuclear localization sequence binding	5/7	25/17697	7.70e-14	1.73e-13	2.03e-14
MF	GO:0005048	signal sequence binding	5/7	46/17697	1.98e-12	3.57e-12	4.18e-13
KEGG	hsa05164	Influenza A	5/7	171/8076	8.15e-08	1.63e-07
KEGG	hsa05132	Salmonella infection	3/7	249/8076	9.25e-04	9.25e-04

Lastly, the results of GeneMANIA also illustrated that the funcitons of the differentially expressed KPNAs and their associated molecules (for instance ARMC1/3/4/6/7/8, RSPH14, CTNND2, SNUPN, CTNND1, ARVCF, PKP1/2/3/4, ANKAR, SPAG6, CTNNB1, RAP1GDS1, and JUP) were primarily related to nucleocytoplasmic carrier activity, protein import into nucleus, molecular carrier activity, cell-cell junction organization, import into nucleus, protein import, and nucleocytoplasmic transport (Fig. 9).

Studies on the KPNA family have recently started to increase. However, reports of the KPNAs proteins in gastric cancer are relatively rare. The KPNA1 was the first to be reported on its involvement in drug transport into cell membranes [24]. It was reported to block some nuclear translocation functions in breast cancer cells in concert with other family members KPNA5/6 [5]. Here in the article, it was found that both transcript and protein contents of KPNA1 were higher in gastric cancer tissues than in paraneoplastic tissues and remarkably linked to OS, FP, and PPS as well as involved in immune invasion of tumor cells. Therefore, it was evident that KPNA1 has potential to become a new oncotarget in gastric cancer.

The existing reports showed that elevated expression of KPNA2 is linked to poor prognosis in gastric cancer [8, 25], and the results obtained in these studies were also consistent. The KPNA2 was highly expressed for both mRNA and protein expression, However, the correlation with the clinical stage was not very high, whereas the AUC curve showed high confidence, and the KM curve exhibited that the prognosis of the gene was linked to OS, FP, and PPS. Further, it was involved in tumor invasion of B cell, CD4 + T cell, and many other immune cells. Therefore, it was evident that KPNA2 can be utilized as a molecular marker for diagnosis of gastric cancer.

Previously, KPNA3 was reported to be highly expressed in colorectal cancer [10], whereas the current study exhibited that KPNA3 was also highly expressed in both colorectal and gastric cancers, and the difference in the level of mRNA expression was relatively significant. Results of PCR experiments showed that the difference in mRNA expression between cancer and paracancerous tissues was statistically significant, whereas the result of WB also supported the high expression of KPNA3 protein in gastric cancer. From the plotted survival curve, it was found that harboring higher KPNA3 expression had a dismal prognosis in contrast with normal individuals. The invasion of B cell, dendritic cell, CD8 + T cell, neutrophils, and CD4 + T cells in gastric cancer tissues was also linked to KPNA3 expression. Therefore, it was evident that KPNA3 is important in dragonizing gastric cancer.

It has been suggested that the knockdown of KPNA4 attenuated the metastasis of prostate cancer and its overexpression also promoted proliferation and invasion of papillary thyroid cancer [11, 13]. This research work demonstrated that the difference in KPNA4 expression between cancer and paraneoplastic tissues was statistically significant, and this result was consistent with those of PCR, WB, and IHC. Furthermore, the invasion of numerous immune cells B cell, macrophages, CD8 + T cell, along with CD4 + T cells was also linked to the expression of KPNA4. Therefore, it can be concluded that the present study can be a useful marker for the diagnosing gastric cancer and offer a reference for the diagnosis along with treatment of gastric cancer.

It has been suggested that elevated expression of KPNA5 is inversely linked to overall survival rate of individuals with breast cancer [26]. The current study demonstrated that the difference in KPNA5 expression in gastric cancer and non-malignant gastric tissues was not statistically significant, but there was a significant correlation with the clinical stage of gastric cancer. Further, it was found that KPNA5 expression at the transcript and protein levels in human gastric cancer tissues was remarkably different from that in non-malignant gastric mucosal tissues.

From the KM curves, it was evident that elevated expression of KPNA5 was negatively linked to the survival rate. Further, there was a remarkable immune invasion of CD8 + T cells in tissues with high expression of KPNA5 gene. It was hence suggested that KPNA5 expression in gastric malignant tissues has an indispensable role and participates in the immune invasion of tumors, thus can offer a basis for the diagnosis of gastric cancer from a certain perspective.

The KPNA6 has been previously reported to be involved in E2 signaling in breast cancer [5], and the present research work also established that KPNA6 was expressed differently in gastric mucosa and gastric cancer tissues at the RNA level as well as at the protein level with statistical significant difference in expression. Results of the survival curve, exhibited that high expression of KPNA6 was inversely linked to OS, FP, and PPS in patients with gastric cancer and was statistically significant. The CD8 + T and neutrophil cell invasion in cancer tissues were remarkably linked to the expression of the gene. Therefore, it is evident that KPNA6 could be an important marker in the detection and treatment of gastric cancer.

It has been reported that silencing KPNA7 can inhibit malignant properties of pancreatic cancer in vitro [14]. In this premise, it was found that the differences in the expression of KPNA7 at RNA along with protein levels in non-malignant gastric tissues and gastric cancer tissues were remarkably different. From the KM curves, it was shown that high expression of KPNA7 was negatively and statistically linked to OS, FS, and PPS in the patients, as well as positively and statistically linked to CD8 + T cell, neutrophils, CD4 + T cell, and macrophage invasion in the cancer tissues.

Herein, the KPNAs family has a mutation rate of between 0.6 and 7%, and the string database showed that the KPNAs family molecules interacted very closely with each other. The GO and KEGG analyses show that the biological processes of KPNAs are mainly in the regulation of host morphology or physiology by viruses and symbionts, NLS- carrying proteins into the nucleus, regulation of host processes by viruses, and enrichment of host processes by symbionts. The main functions of the KPNAs family are focused on the activity of nucleocytic transport carriers, protein import into the nucleus, nuclear transport and signal sequence binding, cell-cell junction organization, and viral regulation of the host processes.

In conclusion, the present study showed that KPNA1/2/3/4/5/6/7 is over-expressed in gastric cancer tissues and that the high levels of KPNA1/2/3/4/5/6 mRNA implied a poor prognosis of GC. Therefore, the new findings not only demonstrate the development mechanism of gastric cancer but also provide a foundation for further research on gastric cancer.

Ethics approval and consent to participate

This study was approved by the Ethics committee of the first affiliated Hospital of Wannan medical college, and informed consent of the patients was also acquired before commencement of the study. The participants provided their written informed consent to participate in this study.

Consent for publication

All the authors listed have approved the manuscript that is enclosed.

Availability of data and materials

The raw data supporting the conclusions of the article is uploaded to the related files, and will be made available by the authors, without undue reservation.

Competing interests

The authors declare no competing interests.

Fundings

This research work was supported by Special Fund for Wannan Medical College Scholar Project (grant number: WK2021F07, WK202002, WK202009).

The Scientific research project of Anhui Provincial Health Commission in 2021, AHWJ2021b109 to LS

Author contributions

Lh S designed the study, analyzed the data, and revised the manuscript. G L carried out statistical analysis and wrote the manuscript draft. Cf Z and B W performed the PCR, WB, IHC assays. Zh W supervised the whole process and reviewed the manuscript. Hy Z, Cx Z, Bs X, B L, Y Z, Jj Land Lh Z helped in inspecting the data and giving suggestions. All authors approved the submitted version.We thank members of Laboratory of Anesthesia Laboratory and Training Center of Wannan Medical College (22 Wenchang west Rd, Wuhu, Anhui, 241002), and Wannan Medical College central Laboratory (22 Wenchang west Rd, Wuhu, Anhui, 241002) as well as our team support and a favorable environment they provided for our study.

Acknowledgments

This research work was supported by Special Fund for Wannan Medical College Scholar Project (grant number: WK2021F07, WK202002, WK202009). We thank members of Laboratory of Anesthesia Laboratory and Training Center of Wannan Medical College (22 Wenchang west Rd, Wuhu, Anhui, 241002), and Wannan Medical College central Laboratory (22 Wenchang west Rd, Wuhu, Anhui, 241002) as well as our team support and a favorable environment they provided for our study. This study was approved by the Ethics committee of the first affiliated Hospital of Wannan medical college, and informed consent of the patients was also acquired before commencement of the study. The participants provided their written informed consent to participate in this study. Further, all study procedures were consistent with the guidelines of the Helsinki declaration.

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No competing interests reported.

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Mammalian karyopherins (KPNAs) as potential biomarkers of prognosis and immune invasion in gastric cancer

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Abstract

Figures

Introduction

Materials And Methods

Results

Transcriptional levels of KPNAs in GC patients

Prognostic value of the KPNAs family factors in individuals with GC

Immune invasion of the KPNAs in individuals with GC

Genetic alteration, co-expression, and GO&KEGG analyses of KPNAs in individuals with GC

Discussion

Conclusion

Declarations

References

Additional Declarations

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