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5970 0004-8725
Biotechnology and Genetic Engineering Reviews
Biotechnology and Genetic Engineering Reviews
Taylor & Francis Taylor & Francis Group
ISSN: 0264-8725 (Print) 2046-5556 (Online) Journal homepage: www.tandfonline.com/journals/tbgr20
Bioinformatics analysis: relationship between adrenocortical carcinoma and KIFs
Xiao Li, Yanghao Tai, Shuying Liu, Yating Gao, Kaining Zhang, Jierong Yin, Huijuan Zhang, Xia Wang, Xiaofei Li & Dongfeng Zhang
To cite this article: Xiao Li, Yanghao Tai, Shuying Liu, Yating Gao, Kaining Zhang, Jierong Yin, Huijuan Zhang, Xia Wang, Xiaofei Li & Dongfeng Zhang (2023) Bioinformatics analysis: relationship between adrenocortical carcinoma and KIFs, Biotechnology and Genetic Engineering Reviews, 39:2, 575-585, DOI: 10.1080/02648725.2022.2160560
To link to this article: https://doi.org/10.1080/02648725.2022.2160560
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ARTICLE
Bioinformatics analysis: relationship between adrenocortical carcinoma and KIFs
Xiao Lia#, Yanghao Taib#, Shuying Liuª, Yating Gaoª, Kaining Zhangª, Jierong Yina, Huijuan Zhangª, Xia Wangª, Xiaofei Lia and Dongfeng Zhanga
ªDepartment of Thoracic Oncology, Linfen Central Hospital, Linfen, China; bDepartment of Clinical Medicine, Shanxi Medical University, Taiyuan, China
ABSTRACT
Adrenal cortical cancer has a relatively low incidence, but a dismal 5-year survival rate. Surgical intervention is the gold standard of care today. In spite of this progress, patients continue to have a dismal outlook. The results of this study demonstrate that kinin superfamily (KIF) has strong ties to many different types of cancers. However, their prognostic and immune cell infiltration of adrenocortical carcinoma (ACC) remain unclear. Multiple databases were searched for information on the transcription level of KIFs, its correlation with clinical data of ACC patients, patients’ overall survival (OS), first progression survival (FPS), and progression free interval (PFI). Its role and association with immune cells were also investigated. We observed an increase in the expression of KIF4A, KIF11, KIF20A, and KIF22. There was a strong correlation between them and the advancedness of ACC tumors. Parallel to this, KIFs are connected to the con- cepts of operating systems, distributed file systems, and partitioned file systems. Similarly, we found five key genes, PRC1, PLK1, KIF23, KIFC1, and KIF5A, through data analysis, all of which participate in multiple cellular pathways. Both KIF4A and KIF11 expression levels were marginally positively corre- lated with immune infiltration. Because KIF4A, KIF11, KIF20A, and KIF22 are involved in multiple ACC processes and can influence the onset and progression of ACC, they provide a mechanistically grounded framework for diagnosing and managing the disease.
ARTICLE HISTORY Received 9 November 2022 Accepted 15 December 2022
KEYWORDS Kifs; clinical data; prognosis; immune infiltration; ACC
1. Introduction
There are over a million new cases of adrenal cortical carcinoma (ACC) per year (Fassnacht et al., 2018), making it a relatively infrequent malignant tumor. Recurrence, poor prognosis, and high mortality are among character- istics that are often associated with ACC. Data show that the 5-year survival
Department of Thoracic Oncology, Linfen Central Hospital, Linfen 041000, China
Xiao Li and Yanghao Tai contributed equally to this work.
+ Supplemental data for this article can be accessed online at https://doi.org/10.1080/02648725.2022.2160560.
rate is much lower than 20%. A recurrence of ACC often occurs during the first two years after major surgery (Glenn et al., 2019). If you have ACC, you should know that surgical resection is the gold standard therapy. Surgery to remove adrenal tumors suspected of ACC should be comprehensive, includ- ing removal of peritumorous/periadrenal retroperitoneal fat, according to recent international recommendations (Fassnacht et al., 2018). Further, lymph node dissection should be conducted locally to decrease the recurrence rate and increase the survival time of patients (Mirallie et al., 2019). Recent research has shown PD-L1 expression in ACC malignancies. The high expression of the FATE1 gene, which is found in both male and female germ cells, in adult ACC tissue has also been linked to a bad prognosis Doghman et al. (2020). Patients diagnosed at stage I have a 5-year survival rate of over 80%, whereas those diagnosed at stage IV have a survival rate of approximately 13% (Fassnacht et al., 2009). Recent studies have shown that genetic and molecular features of ACC have a regulatory role in the invasion and proliferation of the pathogen (Wasserman et al., 2015). As a result, it is crucial to look for genes associated with acc prognosis and therapy.
Recent research has shown that members of the kinin superfamily (KIFs) are kinesins that operate on microtubules. Mainly, they use intracellular transporters to move things like organelles and messenger RNAs along microtubules in an ATP-dependent fashion (Hirokawa & Takemura, 2008; Hirokawa et al., 2009; Miki et al., 2005). They are present in both mitotic (engaged in cell division) and amitotic (involved in intracellular transport) eukaryotes (Rath & Kozielski, 2012; Vale et al., 1985). They’re crucial for cell morphogenesis and fundamental biological processes (like mitosis and meiosis), but they also take part in more complex living activities like memory and learning in the brain (Hirokawa & Takemura, 2008; Hirokawa et al., 2009; Miki et al., 2005). At present, there is no relevant research on the mechanism of KIFs in ACC.Multiple public databases were searched for information on KIFs belonging to ACC patients in order to determine the association between the two conditions, with the hopes of better informing treatment decisions and providing more insight into prognosis. This research shows that KIFs significantly influenced the onset and progression of adrenocortical carcinoma, which opens up new avenues for research into adrenocortical carcinoma, sheds light on its complex pathophysiology, and points toward potential therapeutic interventions.
2. Methodology
2.1. TCGA and GTEx
After obtaining gene expression profiles of adrenocortical carcinoma tissues and healthy adrenal cortex from TCGA (The Cancer Genome Atlas) and
GTEx (Genotype-Tissue Expression), we used R software (version 3.6.3) to determine whether or not there was a significant difference in the expres- sion of members of the kinesin family in these two groups. Specifically, version 3.3.3 of the ggplot2 software was used to generate the images. In addition, the TCGA database was queried for patient clinical data such age, gender, pathological stage, survival, and outcome.
2.2. Gene expression profiling interactive analysis (GEPIA) analysis
Differential gene expression analysis, gene-related survival analysis, correlation analysis, clinical tumor staging, and pathological tumor staging are all possible with the help of the GEPIA database’s adaptable tools. Information from the GTEx project and TCGA RNA-seq studies are included in GEPIA. An array of kinesin-related genes were analyzed for differential expression using GEPIA in this study. We utilized GEPIA to investigate the expression of kinesin family members throughout the spectrum of ACC disease progression, and we also used it to do a survival study connected to individual kinesin family members.
2.3. GeneMANIA analysis
GeneMANIA is a database that may be used to learn about the intercon- nectedness of genes of interest and to make educated guesses about their potential functions. GeneMANIA was consulted in order to learn more about the gene-gene interaction network of the kinesin family.
2.4. Search tool for the retrieval of interacting genes (STRING) analysis
Protein-protein interaction networks across species may be analyzed with the help of STRING, an online tool. We investigated the kinesin family’s protein-protein interaction network using the STRING database.
2.5. Functional enrichment and KEGG pathway analysis
Gene Ontology (GO) functional annotation was achieved by using biologi- cal processes (BP), cellular components (CC), and molecular functionalities (MF). All of the following R packages were utilized: We used the clusterProfiler (version 3.14.3) and ggplot2 (version 3.3.3) packages for our respective GO enrichment analyses and plotting, respectively.
2.6. Tumor immune estimation resource (TIMER) database
Direct immunological investigation of individual genes in tumors may be performed and seen in high detail using TIMER, which serves as a genetic
immunoassay platform. We analyzed the relationship between the expres- sion of members of the kinesin family and immunological infiltrates in tumors, including neutrophils, CD8+ T cells, CD4+ T cells, dendritic cells, B cells, and macrophages.
2.7. The relationship between kinesin family members and clinical characteristics
To prove the therapeutic value of kinesin family member prognostic evalua- tion, we compared expression patterns between malignancies and adjacent normal tissues. Prognostic kinesin family member expression was studied in relation to patient characteristics including Gleason score and TNM stage. This study used the t-test and one-way analysis of variance (ANOVA) to compare many groups. The cutoff for statistical significance was set at 0.05, or less.
3. Results and discussion
3.1. Differential expressions of kinesin family members in ACC
To determine the probable function of kinesin family members of adreno- cortical carcinoma, we sought GEPIA and observed that KIF4A, KIF11, KIF20A, and KIF22 were obviously up-regulated in ACC samples con- trasted with nontumor samples (Figure 1a). KIF1C, KIF5C, KIF7, KIF13A, KIF17, and KIFC2 indicate high levels of expression in normal adrenal cortex tissues.
3.2. The relation between the transcription level of the kinesin family member and tumor stage of adrenocortical carcinoma
After investigating the KIF gene family’s expression pattern in ACC, we speculated as to whether or not its transcriptional level was related to the stage of ACC. For this study, we used the GEPIA2 databases, which are built off of the TCGA database, to evaluate the data. As shown in Figure 2, KIF4A (P=4.1e-05), KIF11 (P=1.71e-05), KIF20A (P=9.37e-05) and KIF22 (P= 0.00567) were significantly associated with the tumor stage of adrenocortical carcinoma, whereas KIF1C, KIF5C, KIF7, KIF13A, KIF17 and KIFC2 groups did not significantly differ. Furthermore, univariate analysis using the Cox proportional hazards model indicated tumor stage and high mRNA expression of all four KIF family members were associated with poor survival prognosis (Table 1). Therefore, we regard these four genes as the key genes of ACC for further study.
5
KIF1C
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KIF4A
KIF5C
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KIF7
KIF11
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2
3
2
N
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-
-
-
-
-
0
0
0
0
0
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
(num(T)=77; num(N)=128)
ACC
KIF13A
KIF17
1
0
KIF20A
5
KIF22
KIFC2
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ACC
(num(T)=77; num(N)=128)
ACC
ACC
ACC
(num(T)=77; num(N)=128)
ACC
(num(T)=77; num(N)=128)
(num(T)=77; num(N)=128)
1
K
F value = 0.994
Pr[>F) = 0.401
+
K
F value = 8.97
Pr(>F) = 4.10-05
K
F value = 0.729 Pri>F]= 0.538
F value = 0.685
Pr(F) = 0.576
1)
K
F value = 3.81
0
0
PI[>F] = 1.716-05
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Stage I
Stage Ii
Stage IN
Stage I
Stage Ii
Stage III
Stage NY
Stage 1
Stage II
Stage III
Stage M
Stage 1
Stage Ii
Stage III
Stage IV
Stage 1
Stage II
Stage III
Stage IV
KI
F value = 0.14 Pr(>F) = 0.935
®
K
F value = 0.225 Pr(F) = 0.879
KI
F value = 8.19
Pr[>F)=9.37€-05
K
F value = 4.55
0
K
F value = 1.82 Pr(F) = 0.152
-
5
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Pr(F) - 0.00567
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Stage II
Stage III
Stage IV
Stage I
Stage Il
Stage Ill
Stage IV
Stage I
Stage II
Stage III
Stage IV
Sugel
Stage l
Stage III
Stage IV
Stogo |
Stage II
Stage III
Stage IV
3.3. Role of the key genes in the prognosis of adrenocortical carcinoma patients
The survminer package and survival package in R software were used to analyze and visualize the expression profile and clinical data of ACC patients in the database in order to gain a deeper understanding of the relationship between the expression level of the key genes and OS, disease specific survival (DFS), and
| Characteristics KIFs | p-value | |||
|---|---|---|---|---|
| KIF4A | KIF11 | KIF20A | KIF22 | |
| Age | 0.907 | 0.571 | 0.571 | 0.739 |
| Gender | 0.581 | 0.928 | 0.08 | 1 |
| T stage | 0.003 | 0.002 | 0.008 | 0.002 |
| N stage | 0.31 | 0.014 | 0.31 | 0.31 |
| M stage | 0.018 | < 0.001 | 0.018 | 0.075 |
| Pathologic stage | 0.013 | < 0.001 | 0.028 | 0.006 |
| Tumor status | < 0.001 | < 0.001 | < 0.001 | < 0.001 |
| New event | 0.003 | < 0.001 | 0.012 | 0.003 |
| Radiation therapy | 0.221 | 0.076 | 0.221 | 0.221 |
| Primary therapy outcome | < 0.001 | < 0.001 | 0.002 | < 0.001 |
| Residual tumor | < 0.001 | < 0.001 | 0.002 | 0.009 |
| Laterality | 0.897 | 0.897 | 1 | 0.559 |
| Mitotane therapy | 0.259 | 0.006 | 0.195 | 0.106 |
Differentials with a significance level of p < 0.05 are highlighted in bold.
progress free interval (PFI) in ACC patients. As shown in Figure 3, high expression of KIF4A (HR =7.54, P <0.001), KIF11 (HR =12.61, P<0.001), KIF20A (HR = 5.97, P < 0.001) and KIF22 (HR =4.66, P <0.001) were related to the poor OS of the patients with adrenocortical carcinoma. High expression of KIF4A (HR =7.25, P <0.001), KIF11 (HR=12.08, P<0.001), KIF20A (HR = 5.73, P < 0.001) and KIF22 (HR=4.40, P=0.001) were related with the poor DSS of the patients with adrenocortical carcinoma. Besides, high expression of KIF4A (HR =3.65, P<0.001), KIF11 (HR=5.96, P<0.001), KIF20A (HR= 3.02, P = 0.001) and KIF22 (HR = 3.46, P < 0.001) were connected with the poor FPI of the patients with adrenocortical carcinoma.
To confirm our observations, we used the online database GEPIA to assess the prognostic ability of significant genes in ACC patients. The GEPIA2 database yielded findings that were comparable to those found in the TCGA database (Figure 4). High expression of KIF4A (Log-rank p = 1.5e-06), KIF11 (Log-rank p = 3.3e-08), KIF20A (Log-rank p = 1.1e-05) and KIF22 (Log-rank p = 0.00014) was connected with the poor OS of the patients with adrenocortical carcinoma. Besides, high expression of KIF4A (Log-rank p = 0.0016), KIF11 (Log-rank p =2.8e-06), and KIF20A (Log- rank p = 0.004) were related to the poor DFS of the patients with adreno- cortical carcinoma. These results indicate that the mRNA levels of KIF4A, KIF11, KIF20A, and KIF22 are related to clinical prognosis and might be prognostic markers in patients with ACC.
3.4. Examining the roles of kinesin family members in ACC: a functional and pathway enrichment analysis
GeneMANIA was used to build the gene-gene interaction network of the essential kinesin family (Figure 5a). Physical interaction, co-expression,
1.0
KIF4A
1.0
KIF4A
1.0
KIF4A
Low
Low
Low
High
High
High
0.8
Survival probability
0.8
0.8
Survival probability
Survival probability
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival
0.2
Disease Specific Survival
0.2
Progress Free Interval
HR = 7.54 (2.85-19.95)
HR = 7.25 (2.72-19.38)
HR = 3.65 (1.86-7.16)
0.0
P < 0.001
0.0
P < 0.001
0.0
P < 0.001
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
1.0
KIF11
1.0
KIF11
1.0
KIF11
Low
Low
Low
High
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival
0.2
HR = 12.61 (4.16-38.18)
Disease Specific Survival HR = 12.08 (3.96-36.90)
0.2
Progress Free Interval
HR = 5.96 (2.84-12.51)
0.0
P < 0.001
0.0
P < 0.001
0.0
P < 0.001
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
1.0
KIF20A
1.0
KIF20A
1.0
KIF20A
Low
Low
Low
High
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival
0.2
HR = 5.97 (2.40-14.90)
Disease Specific Survival
0.2
HR = 5.73 (2.27-14.44)
Progress Free Interval
HR = 3.02 (1.55-5.88)
0.0
P < 0.001
0.0
P < 0.001
0.0
P = 0.001
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
1.0
KIF22
1.0
KIF22
1.0
KIF22
Low
Low
Low
High
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival HR = 4.66 (2.00-10.85)
0.2
Disease Specific Survival HR = 4.40 (1.86-10.39)
0.2
Progress Free Interval
HR = 3.46 (1.75-6.83)
0.0
P < 0.001
0.0
P = 0.001
0.0
P < 0.001
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
Overall Survival
Disease Free Survival
Overall Survival
Disease Free Survival
9
LOW KIF4A TPM
8
Low KIF4A TPM
9
High KIF4A TPM
High KIF4A TPM
LOW KIF11 TPM
9
Low KIF11 TPM
Logrank p=1.5e-06
High KIF11 TPM
High KIF11 TPM
0.8
HR(high)=7.8
Logrank p=0.0016
HR(high) 2,9
Logrank p=3.3e-08
Logrank p=2.8e-06
p(HR)=3.80-05
US
p(HR)=0.0024
8
HR(high) 13
p(HR)=5.8c-06
8
HR(high)=5.3
p(HR)=1.8€-05
Percent survival
n(high)=38
n(low)=38
Percent survival
n(high)=38
n(low)=38
Percent survival
n(high)=38
Percent survival
n(high)=38
0.6
0.6
0.6
n(low)=38
0.6
low)=38
8
5
3
3
2
0.2
0.2
2
0.0
0.0
8
8
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
Months
Overall Survival
Disease Free Survival
Overall Survival
Disease Free Survival
2
Low KIF20A
1
High KIF20A TPM
Low KIF20A TPM
:
Low KIF22 TPM
2
High KIF20A
Low KIF22 TPM
Logrank p=1.1e-05
Logrank p=0.004
High KIF22
Logrank p=0.00014
High KIF22 TPM
Logrank p=0.079
0.8
HR(high)=6.4
P(HR)=9.4e-05
OB
HR(high)=2.7
p(HR)=0.0055
0
HR(high)=4.7
p(HR)=0.00043
30
HR(high)=1.8
p(HR)=0.083
Percent survival
nthigh)=38
n(low)=38
Percent survival
n[high)=38
0.6
0.6
n(low)=38
Percent survival
n(high)=38 [(Tow)=38
Percent survival
n(high)=38
0.6
0.6
nílow)=38
0.4
los
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50
100
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50
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50
100
150
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50
100
150
Months
Months
Months
Months
anticipated, co-localization, genetic exchange, route, and common protein domains were only a few of the GeneMANIA network categories that encircled the essential kinesin family members. Protein regulator of cyto- kinesis 1 (PRC1), polo-like kinase 1 (PLK1), kinesin family member 23 (KIF23), kinesin family member C1 (KIFC1), and kinesin family member 5A (KIFC1) were the top 5 genes linked to the essential kinesin family members (KIF5A). PRC1 showed the strongest association with the genes involved in physical contact. It is possible that the kinesin family member interacts with the other four genes in the context of antigen processing and presentation of peptide antigen via MHC class II, antigen processing and presentation of peptide or polysaccharide antigen via MHC class II, the microtubule-associated complex, motor activity, tubulin binding, the spin- dle apparatus, and mitotic nuclear division. The STRING database was also used to build the protein-protein interaction network (version 11.5). Figure 5b displays the 10 proteins that were linked to the crucial genes. The aforementioned 20 genes and their linked essential genes were analyzed for GO functional enrichment and visualized using the R program. Figure 5c and Supplementary Table S1 both show the outcomes.
3.5. Immune infiltration analysis of kinesin family member in ACC
Tumor microenvironment features such as immune cell infiltration are indepen- dent indicators of prognosis and lymph node metastasis. Figure 6 displays the results of an examination of the TIMER2.0 database that compares KIF to other immune cells seen in AC. KIF4A and KIF11 expression in ACC was weakly
a
Networks
Functions
Physical Interaction
Microtubule associated complex
Co-expression
KF11
Motor activity
Predicted
Tubulin binding
Co-localization
Antigen processing and presentation of peptide antigen via MHC class II
Pathway
Antigen processing and presentation of peptide or polysaccharide antigen via MHC class II
Genetic Interactions
Spindle
Shares protein domains
Mitotic nuclear division
b
PLK1
CDK1
KIF20A
c
tubulin binding
microtubule binding
motor activity
KIF11
p.adjust
DLGAP5
202856e-16
microtubule motor activity
3.152142e-16
microtubule
2.101428e-16
1.050714e-16
CDCAB
KIF4A
microtubule associated complex
1.8225480-28
kinesin complex
Counts
10
AURKB
KIF22
spindle
☐
12
☐
14
microtubule-based movement
☐
16
Known Interactions
Others
antigen processing and presentation of peptide
From curated databases
Textmining
antigen via MHC class II
Experimentally determined
Co-expression
antigen processing and presentation of exogenous
Protein homology
peptide antigen via MHC class II
Predicted Interactions
retrograde vesicle-mediated transport, Golgi to ER
Gene neighborhood
Gene fusions
0.5
0.6
0.7
0.8
Gene co-occurrence
GeneRatio
KIF4A Expression Level (log2 TPM)
6
Purity
B Cel
CD8+ T Cell
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cell
cor = 0.262
p - 2.42e-02
partial.cor = 0.322
p = 5.44e-03
partial.cor = - 0.051
p - 6.66e-01
partial.cor = 0.022
p - 8.50e-01
partial.cor = - 0.02
P - 8.67e-01
partial.cor = 0.155
p = 1.91e-01
partial.cor = 0.326
p = 4.91e-03
F
KIF
4A
2
02
0.4
0.6
0.8
1.0
0.11
0.12
0.13
0.20
0.25
0.30
0.35 0.07
0.09
0.11
0.13
0.15
0.08
0.12
0.18
0.12
0.14
0.16
0.18
0.49
0.50
0.51
0.52
0.53
Infiltration Level
KIF11 Expression Level (log2 TPM)
Purity
B Cell
CD8+ T Cell
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cell
7.5
cor = 0.368
p = 1.24e-03
partial.cor = 0.37
p = 1.26e-03
partial.cor = - 0.077
p = 5.17e-01
partial.cor = 0.013
p = 9.15e-01
partial.cor = - 0.038
p = 7.50e-01
partial.cor = 0.125
P = 2.93e-01
partial.cor = 0.253
p = 3.06e-02
.0
KIF
AC
11
0.0
0.2
0.4
0.6
0.8
1.0
0.11
0.12
0.13
0.20
0.25
0.30
0.35 0.07
0.09
0.11
0.13
0.15
0.08
0.12
0.14
Infiltration Level
0.12
0.18
0.16
0.18
0.49
0.50
0.51
0.52
0.53
KIF20A Expression Level (log2 TPM)
Purity
B Cell
CD8+ T Cel
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cell
7.5
cor = 0.268
p = 2.11€-02
partial.cor = 0,192
p = 1.03e-01
partial.cor = - 0.152
p = 1.980-01
partial.cor = - 0.053
p = 6.54e-01
partial.cor = - 0.082
p = 4.91e-01
partial.cor = 0.066
p = 5.80e-01
partial.cor = 0.155
p = 1.89e-01
5.0
KIF2
2.5
ADO
0A
0.0
2.5
0.2
0.4
0.6
0.8
1.0
0.11
0.12
0.13
0.20
0.25
0.30
0.35 0.07
0.09
0.11
0.13
0.15
0.08
0.12
0.16
0.12
0.14
0.15
0.18
0.49
0.50
0.51
0.52
0.53
Infiltration Level
KIF22 Expression Level (log2 TPM)
Purity
B Coll
CDB- T Col
CD4+ T Col
Macrophago
Neutrophil
Dendritic Coll
cor - 0.219
partial.cor = 0.011
p = 6.07e-02
p - 9.26e-01
partial.cor = 0.11
p - 3.540-01
partial.cor = 0.101
P- 3.93e-01
partial.cor - - 0.074
p = 5.34e-01
partial.cor - 0.039
partial.cor - - 0.01
p = 7.43e-01
p = 9.36e-01
5
KIF2
ACC
2
9
0.2
0.4
0.6
0.8
1.0
0.11
0.12
0.13
0.20
0.25
0.30
0.35 0.07
0.09
0,11
0.13
0.15
0.08
0.12
0.16
0.12
0.14
0.16
0.18
0.49
0.50
0.51
0.52
0.53
Infiltration Level
correlated with immune infiltration characteristics such as purity of infiltration, B cell count, and dendritic cell count (Figure 4a). However, there was no relationship between KIF20A and KIF22 expression and immune cell count.
4. Conclusion
Using a variety of sources, we uncovered the following about KIF: 1. KIF1C, KIF5C, KIF7, KIF13A, Kif17, and KIFC2 were considerably expressed in normal adrenal cortex, whereas KIF4A, KIF11, KIF20A, and KIF22 were highly up- regulated in ACC samples. Among the KIFs, KIF4A (P=4.1e-05), KIF11, KIF20A, and KIF22 were substantially linked to ACC tumor stage, but KIF1C, KIF5C, KIF7, KIF13A, KIF17, and KIFC2 were not. The genes KIF4A, KIF11, KIF20A, and KIF22 are linked to ocular surface disease, fibrosis, and ischemia. There is a negative association between OS, DFS, and PFI and the expression of KIF4A, KIF11, KIF20A, and KIF22. There is some speculation that they can be used as prognostic indicators in ACC patients. By querying the database, we were able to identify five key genes - PRC1, PLK1, KIF23, KIFC1, and KIF5A - in the process of building a gene interaction network. Microtubule-related complex, motility, microtubule-binding spindle, and mitosis play major roles, as do MHC II antigen processing and presentation of peptide antigen and MHC II antigen processing and presentation of polysaccharide antigen 5. Weak immune infiltration, including purity, B cell, and dendritic cell infiltration, was favorably linked with KIF4A and KIF11 expression levels. There was no correlation between the total number of immune cells and the KIF20A and KIF22 expression levels. Last but not least, we think that KIF4A, KIF11, KIF20A, and KIF22 all play a role in various ACC processes, have an impact on the onset and progression of ACC, and provide a unique foundation upon which to build a prognosis and treatment plan for the disease.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Data sharing agreement
The datasets used and/or analyzed during the current study are available from the corre- sponding author on reasonable request.
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