Transcriptomic and Functional Pathway Analysis of Human Cervical Carcinoma Cancer Cells Response to Microtubule Inhibitor.

BACKGROUND: There clearly is a need for effective chemotherapy for early-stage, high-risk patients with human cervical carcinoma. Vinblastine (VBL) is a key microtubule inhibitor, but unproven in its mechanisms as an important antitumor agent in cervical carcinoma.
METHODS: We selected the concentration of vinblastine inducing 30% cell death for analyses assessing the DNA content, gene expression and transcriptional gene regulation of VBL-treated KB-3 cells.
RESULTS: Transcriptomic and hierarchical clustering analysis demonstrated that treatment of KB-3 cells with VBL altered the expression of a diverse group of genes with G2/M arrest, which regulated by four oncogenic or tumor suppresser transcription factors (AP1, NFKB1, RELA, and TP53). Functional pathway analysis revealed the disease response to the biological effects of vinblastine in cervical carcinoma chemotherapy including protein ubiquitination pathway, RhoGDI signaling, integrin signaling, agranulocyte adhesion and biapedesis, and actin nucleation pathways. Northern blots also confirmed that KRT-7, FN14, IER3, and ID1 were deregulated in VBL-treated KB-3 cells.
CONCLUSION: Transcriptional time series profiles and a functional pathway analysis of VBL-treated KB-3 cells will provide a new strategy for improving microtubule inhibitor chemotherapy for cervical carcinoma.

Background

Human cervical carcinoma KB-3 cells, representing a subclone of human HeLa cells, are highly sensitive to most chemotherapeutic drugs, clone with high efficiency, growing rapidly 1, 2. Vinblastine (VBL), as a microtubule inhibitor, is an important antitumor agent that induces G arrest and subsequent apoptosis in a wide variety of cell lines 3. It was well known that VBL treatment caused the down-regulation of p53 and its target p21 and up-regulation of tumor necrosis factor alpha and Bak in KB-3 cells, identifying these genes as putative targets of vinblastine-inducible AP-1, which demonstrated that VBL -inducible AP-1 played a destructive, proapoptotic role and regulated the expression of a specific subset of target genes 4. p21 was also confirmed to play a protective role in VBL-induced apoptosis in KB-3 cells through a c-Jun regulated pathway 5. Repression of proangiogenic and metastatic factors (VEGF, bFGF, MMP2, and MMP9) was also detected in VBL-treated cancer cells 6. However, the networks of affecting gene expression programs and the molecular mechanisms of antitumor of this microtubule inhibitor that link G2/M arrest and apoptosis in VBL-treated cervical cancer are poorly understood.

In our previous studies, 49 genes were down-regulated when VBL-resistant KB-v1 cells were subjected to lower dose or depletion of VBL and DNA microarray is a very useful tool to detect drug response targets in cancer treatment 7. Thus, in view of the complex array of genetic factors contributing to VBL treatment of KB-3 cells, cDNA microarray should be useful for examining the drug response in cancer. Identification of transcription factor (TF) target genes would help for elucidating the transcriptional control of the VBL-triggering gene expression programs. Thus, these analyses may ultimately enable the use of signature expression profiles of drug treatment to predict response to VBL, further to improve our understanding of mechanisms of microtubule inhibitor in human cervical carcinoma cancer KB-3 cells.

Methods

Cytotoxicity assay

The sulforhodamine B (SRB) assay standard procedures were used 8. KB-3 cells were purchased from the American Type Culture Collection and cultured in DMEM supplemented with 10% heat-inactivated fetal calf serum and the antibiotics penicillin and streptomycin. In this assay, KB-3 cells were plated in 96-well culture plates (104 cells/well) and were grown overnight at 37oC in a 5% CO2 incubator. VBL was then added to the wells to achieve a final concentration ranging from 10-6 to 10-4 M. Control wells were prepared by adding 100 μl culture medium. The plates were incubated at 37oC in a 5% CO2 incubator for 48 h. After adding 50 μl of 50% trichloroacetic acid for 10 min at room temperature, the plates were then put at 4oC for 4 h and the supernatant were removed, washed 5 times with H2O. Then, SRB was added to each well for 30 minutes. Unbound dye was removed by four washes with 1% acetic acid. The protein-bound dye was extracted with 10 mM Tris base at a wavelength of 515 nm. The LD and LD were determined from the plots of percent viability vs. dose of compound added.

Cell cycle analysis

To determine cell cycle distribution, 5 x 105 cell were plated in 60-mm dishes, treated with 2.5 x 10-4 µM of vinblastine for 0, 2, 4, 8, 12, 24, and 48 h. Cells were then collected by trypsinization, fix in 95% ethanol, wash in 1% BSA-PBS, resuspended in 1 μg/ml of RNase and 50 μg/ml of PI, incubated for 30 min in the dark at 37oC, and analyzed by flow cytometry using a FACSCalibur. The data were analyzed using the ModFit DNA analysis program as described previously 9.

Gene expression profiling

RNA was isolated with Trizol LS (Invitrogen, Carlsbad, CA, USA) and purified using the RNeasy Mini Kit (Qiagen, Valencia, CA, USA). RNA quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbronn, DE, USA). Total RNA from each sample was labelled using a Low Input Labeling Kit (Agilent Technologies), which involves reverse transcribing the mRNA to produce cDNA and then transcribing in the presence of Cy3-CTP or Cy5-CTP to produce labelled cRNA from pancreatic cancer cell lines was paired with differentially labelled cRNA from KB-3 cells or VBL-treated KB-3 cells and hybridized to a 44K Whole Human Genome Oligo Microarray (Agilent Technologies) at 60 °C for 17 h according to the manufacturer's protocol. To reduce systematic bias, dye-swap hybridizations were performed for each cell line, and the resulting data were combined with analysis, in which dye assignment was reversed in the second hybridization in Agilent arrays.

Microarray data and pathway analysis

The microarray data were represented as the antilog ratio of gene expression measures of VBL treated KB-3 cells to untreated KB-3 cells. The data were normalized and analyzed using Partek Genomics Solution software, as reported in our previous studies 10. We used the Ingenuity Pathway Analysis (IPA) tool to determine the enriched canonical pathways on the basis of differential gene expression.

Identification of transcription factor (TF) target genes

Previously, we developed a bioinformatics tool CMGRN to identify target gene of TFs and to construct gene regulatory networks, that has been successfully applied in cancer dataset 11. This method is based on Bayesian hierarchical model with Gibbs sampling implementation to calculate joint conditional probability that genes is regulated by TFs. It is able to integrate both gene expression profiles with binding data of TFs, and to predict target genes of TFs 11. Here, we applied this methodology to integrate expression data of the differentially expressed genes (fold change at least 1.5) from the VBL-treated microarray experiment and the binding data of four TFs, NFKB1, RELA, AP1 and TP53 derived from Yan et al 12. As a result, we will predict target genes of the four TFs and then infer the transcriptional gene regulatory networks.

Northern blotting

Total cellular RNA of untreated or VBL-treated KB-3 cells was isolated as described previously 7, run on a 1% agarose gel (15 g per gel lane) containing 2.2 M formaldehyde, and transferred to a nitrocellulose membrane. Membrane was dried at room temperature, and RNA was cross-linked by UV irradiation in a Stratalinker (Stratagene). Hybridization was performed in ExpressHyb Hybridization buffer (CLONTECH) containing 1 x 106 cpm/ml of 32P-labeled cDNA probe (ID1, IER3, KRT-7, FN14 and β-actin) at 65oC for 2 h. Blots were washed in 2 x SSC and 0.1% SDS for 20 min, followed by a wash at 50oC in 0.2 x SSC and 0.1% SDS for 20 min.

Results

Flow cytometry analysis of the effect of vinblastine on KB-3 cells

First, we tested the cytotoxicity of vinblastine in human cervical carcinoma KB-3 cells using Sulforhodamine B (SRB) assay as described previously 8. After KB-3 cells were treated with VBL for 48 h, the median lethal dose (LD) and the concentration of vinblastine at which 30% cell death was reached (LD) in KB-3 cells were 6.8 x 10-4 µM and 2.5 x 10-4 µM, respectively. The flow cytometry assay provides information regarding cell cycle phase sensitivity to apoptosis is based on bivariate analysis of their DNA content 9. Next, the concentration of LD was selected for analyses assessing the DNA content of vinblastine-treated KB-3 cells, which was examined via propidium iodide (PI) staining and flow cytometry. At 0, 2, 4, 8, 12, 24, and 48 h post-treatment with 2.5 x 10-4 µM of vinblastine, KB-3 cells arrested predominately at G2/M phase of the cell cycle and the number of cells in the G1 phase had diminished (Fig. 1). With a longer duration of treatment (24 h), a much greater percentage of KB-3 cells at G2/M phase. These results indicate that KB-3 arrested at G2/M in response to vinblastine treatment.

Figure 1

Effect of vinblastine (VBL) on cellular DNA content. KB-3 cells untreated and treated with 2.5 x 10-4 µM of vinblastine for 2, 4, 8, 12, 24, and 48 h. Cells were subjected to DNA content analysis by flow cytometry with PI staining. A-D: KB-3 cells treated with 2.5 x 10-4 µM of VBL for 0, 2, 12 and 24h. E: Data analysis of Effect of VBL on cellular DNA content in KB-3 cells treated with 2.5 x 10-4 µM of VBL for 0, 2, 4, 8, 12, 24, and 48 h.

Gene clustering analysis showing the differentially expressed genes in VBL treated KB-3 cells in a time-dependent manner

The VBL response gene candidates were selected based on the patterns from a heat-map figure (Fig. 2) from the 73 normalized gene lists via Partek Genomic Suite. The red is for the up-regulated genes, blue is for down-regulated genes, white is for the missing data in a time point, which were from differently expressed genes in KB-3 cells treated with 2.5 x 10-4 µM of vinblastine at 0, 2, 4, 8, 12, 16, 24, and 48 h, and identified to change in the transcriptome at a normal cutoff value ≥ ± 1.5-fold in 80% tested samples. Hierarchical clustering has the advantage that it is simple and the results can be easily visualized 13, and was used to partition data into two groups that have similar expression patterns (Fig. 2). These classes of genes could be distinguished into those whose mRNA levels remained induced for much longer (Fig. 2, cluster A) and those that responded early and whose induction was transient (Fig 2. cluster B), which included IER3, SCO2, SP140, ID1, DUSP6, ADRM1, U2AF1, NUDEL, SLC26A6, BCAA, PPID, SKD1, SPHK1, and SGK. Over-expressed genes in MDR KB-v1 cells can also be found in cluster A, which showed gradually increasing gene expression in VBL treatment of KB-3 cells as a function of time, including the genes that responded later (KRT 7, KRT 17, FN14, UGT2B7, ITGA5, and CD63) that appeared to be related to drug resistance involving cell wall metabolism, drug modification, and signal transduction 7. The transcriptional response to vinblastine suggests a multifaceted role for KB-3 cells in the physiology of drug metabolism.

Figure 2

Cluster image showing the different classes of gene expression profiles. Five hundred thirty-six genes whose RNA levels changes in response to 2.5 x 10-4 µM of vinblastine were selected. This subset of genes was clustered hierarchically into groups on the basis of the similarity of their expression profiles. The graphs show the average expression profiles for the genes in the corresponding cluster A and B.

Northern blot analysis was used to verify the array hybridization data

Northern blot analysis of ID1, IER3, KRT-7, FN14, and β-actin was performed on RNA prepared from KB-3 cells that were treated with 2.5 x 10-4 µM VBL for 0, 2, 4, 8, 12, 16, and 24 h and demonstrated that ID1, IER3, KRT-7, and FN14 were deregulated in KB-3 cells treated with VBL compared to β-actin as control (Fig. 3). Northern blot analysis of deregulated genes in KB-3 cells treated with VBL verified the array hybridization data.

Figure 3

Northern Blots analysis of ID1, IER3, KRT-7 and FN14, and ß-actin was used to normalize the RNA quantity in each sample (A). Analyzed the quantification of the mRNA expression levels of ID1, IER3, KRT-7 and FN14 those normalized to ß-actin in KB-3 cells treated with vinblatine (B).

Functional networks and pathways of VBL-induced stress response in human cervical carcinoma KB-3 cells were analyzed by the Ingenuity Pathway Analysis (IPA)

The genetic networks and cellular pathways were derived using the IPA program by analyzing 73 genes that were differentially expressed in VBL-treated KB-3 cells. A more comprehensive network and pathway analysis of all deregulated genes revealed their association with four important network functions and five critical canonical pathways, all of which are relevant to VBL-treated KB-3 cells. The differently expressed genes constituted about half the total molecules involved and the network-associated cellular functions and included those related to dermatological diseases and conditions, inflammatory disease, inflammatory response, energy production, cellular development, digestive system development and function, embryonic development, organ development, cancer, and cellular movement in VBL- treatment of cells (Table 1). They belong to five canonical signaling pathways that are commonly deregulated in VBL treatment (Table 2). Although only 14 (HSPA8, PSMD11, PSMC1, HLA-C, PSMD1, PSMD8, RHOC, ITGA5, RHOF, ACTG1, MSN, MYH9, CLDN1, and CAPN2) were deregulated in VBL-treated KB-3 cells with the protein ubiquitination, RhoGDI signaling, integrin signaling, agranulocyte adhesion and biapedesis, and actin nucleation signaling pathways, which play a role in in controlling cancer cell growth, microtubule inhibition, and the stress response of VBL- treated KB-3 cells.

Table 1

Genetic networks associated with VBL-treated KB-3 cells.

Top 4 VBL networks	Score	Focus molecules	Molecules in network
Dermatological diseases and conditions, inflammatory disease, inflammatory response	42	19	19S proteasome, 20s proteasome, 26s Proteasome, Actin, ADRM1, Alpha tubulin, ATPase, CD63, Cytokeratin, ENaC, ERK1/2, ID1, IER3, KRT7, KRT14, KRT15, KRT17, Mlc, MYH9, Myosin, POLK, PP2A, PSMC1, PSMD, PSMD1, PSMD8, PSMD11, RACGAP1, RHOC, Rock, Smad, SMAD2, Ubiquitin, VPS4B, WNK1
Energy production, cellular development, digestive system development and function	31	15	ABCF1, ANXA3, ATG4D, C1orf50, Calmodulin, DDX5, ERK, FAM216A, FBXO9, GDAP2, HLA-C, HNF1A, HNF4A, LOC102724594/U2AF1, MRPL19, MRPL33, MTRF1L, ORMDL2, PAM, Pka, PLP2, PRMT3, RNF44, SLC35D1, SLC7A6OS, SNX11, SP140, TAGLN2, TMUB2, TPM4, UBC, UGT2B7, ZNF155, ZNF442, ZNF586
Embryonic development, organ development,	28	14	ACTG1, AKR1B1, Alpha catenin, ANXA4, caspase, CD3, CDKN1A, Clathrin, cytochrome C, estrogen receptor, F Actin, Hdac, Histone h3, Histone h4, HSPA8, IL12 (complex), Insulin, Interferon alpha, Jnk, LGALS3BP, MAGED2, MBD1, MSN, NFkB (complex), Notch, P38 MAPK, PI3K (complex), PPID, Pro-inflammatory Cytokine, Ras homolog, RHOF, RNA polymerase II, RPS3, RPS27L, SGK1
Cancer, cellular movement	23	12	Akt, Alp, Ap1, AURKA, calpain, CAPN2, Cg, CLDN1, Creb, Cyclin A, DUSP5, Fibrinogen, Focal adhesion kinase, FSH, IL1, ITGA5, LDL, Lh, Mek, MT1E, MT1L, NMDA Receptor, NT5E, p85 (pik3r), Pdgf (complex), PDGF BB, Pkc(s), PLAT, PLC gamma, Ras, SLC26A6, SOD2, SPHK1, Tgf beta, VegfF

Table 2

Top 5 canonical pathways involving genes that are differently expressed in VBL- treated KB-3 cells, as determined by Ingenuity Pathway Analysis.

Top 5 Canonical Pathways	P value	Ratio	Molecules
Protein Ubiquitination Pathway	1.31E-04	2.35E-02	HSPA8, PSMD11, PSMC1, HLA-C, PSMD1, PSMD8
RhoGDI Signaling	1.91E-04	2.89E-02	RHOC, ITGA5, RHOF, ACTG1, MSN
Agranulocyte Adhesion and Diapedesis	2.88E-04	2.65E-02	MYH9, CLDN1, ITGA5, ACTG1, MSN
Integrin Signaling	3.90E-04	2.48E-02	RHOC, ITGA5, CAPN2, RHOF, ACTG1
Actin Nucleation	6.84E-04	5.36E-02	RHOC, ITGA5, RHOF

Transcriptional gene regulatory network in VBL-treated cells

It has been demonstrated that some oncogenic or tumor suppresser TFs are implicated in affecting gene expression programs in cervical cancer, such as NF-κB 14, 15, AP1 4, 14, and TP53 16. In order to elucidate how these cancer-related TFs affect the response of VBL-treated KB-3 cells, we employed a newly developed bioinformatics method to identify their target genes 11. As shown in Table 3, we display the predicted target genes of AP1, NFKB1, RELA, and TP53. Some target genes have been validated in previous studies, for example, CDKN1A (p21), DUSP5, IER3, ITGA5, RPS27L and SOD2 (Table 3). We constructed a transcriptional gene regulatory network controlled by the four TFs (Fig. 4). Noticed is that nine genes are co-targeted by oncogenic (AP1, NFKB1 and RELA) and tumor suppresser TFs (TP53), and involve biological processes apoptosis (CDKN1A and SPHK1), calcium-activated neutral proteinase (CAPN2), epidermis development (EMP1, ACTG1, KRT14, KRT15 and KRT17), and tumor-associated antigen (MAGED2). By contrast, the oncogenic TFs also regulate genes related with ubiquitin-protein modification (PSMD11 and PSMD1), apoptosis (IEG3), and cell adhesion (ITGA5 and MSN). This result indicates a possibility of transcriptional gene regulation that mediates the VBL-induced response in the cancer cells.

Table 3

Transcription factor target genes in VBL-treated tumor cells.

Gene symbol	AP1	NFKB1	RELA	TP53
ACTG1		predicted target		predicted target
AKR1B1		predicted target	predicted target
CAPN2	predicted target	predicted target		predicted target
CD63	predicted target	predicted target
CDKN1A	validated target	validated target	validated target	validated target
CLDN1		predicted target
DUSP5				validated target
EMP1	predicted target			predicted target
HLA-C		predicted target
HSPA8		predicted target
IER3		validated target	validated target
ITGA5	predicted target	validated target	validated target
KCNK1		predicted target
KRT14	predicted target			predicted target
KRT15			predicted target	predicted target
KRT17		predicted target		predicted target
LASP1		predicted target
LGALS3BP				predicted target
MAGED2	predicted target	predicted target		predicted target
MORF4L2	predicted target
MSN		predicted target	predicted target
MT1E		predicted target
MYH9		predicted target
MYO1C	predicted target
PAM	predicted target	predicted target
PLP2	predicted target	predicted target	predicted target
PSMC1	predicted target
PSMD1	predicted target	predicted target
PSMD11	predicted target	predicted target
PSMD8				predicted target
RAB31	predicted target	predicted target	predicted target
RPS27L				validated target
SDC4		predicted target	predicted target
SOD2				validated target
SPHK1	predicted target	predicted target		predicted target
STC2			predicted target
TAGLN2		predicted target
TPM4		predicted target

Figure 4

Transcriptional gene regulatory network in VBL-treated KB-3 cell. Triangle nodes represent four TFs AP1, NFKB1, RELA and TP53. Circle nodes refer to target genes of at least two TFs. Orange nodes refer to genes jointly regulated by TP53 and other TFs.

Discussion

In this study, first, cytotoxicity studies and the flow cytometry assay provided information regarding the cytotoxicity of microtubule inhibitor in KB-3 cells and cell cycle phase sensitivity to apoptosis is based on bivariate analysis of their DNA content. The DNA content of untreated and VBL-treated KB-3 cells was examined via PI staining and flow cytometry. At 12 h post-treatment with 2.5 x 10-4 µM of VBL (LD), KB-3 cells were arrested predominately at G2/M phase of the cell cycle and G1 phase was apparently decreased (Fig. 1). Under a longer duration of treatment, a much greater percentage of KB-3 was arrested at G2/M phase. The result indicated that KB-3 arrested at G2/M in response to VBL. At the same time, the hierarchical clustering analysis revealed that RPS27L, KRT17, FN14, UGT2B7, MYH9, CD63, ARHC, MT1E, KRT7, CDKN1A, PLAT, and ITGA5 were responsible for KB-3 cells arrested predominately at G2/M phase of the cell cycle with 2.5 x 10-4 µM vinblastine, which were highly overexpressed in VBL-treated KB-3 cells that responded later including those that control the cell cycle, DNA repair, and drug resistance. In these later response genes, RPS27L-expressing LoVo cells ceased DNA synthesis and apoptotic activity, and elevated RPS27L may improve the prognoses of certain CRC patients by enhancing the DNA repair capacity of their colonic cells 17. CDKN1A, as a negative regulator of cell proliferation and DNA replication, plays additional and fundamental roles in other important pathways, including regulation of transcription, apoptosis and DNA repair 18. UGT2B7 is an enzyme responsible for detoxification of xenobiotics, as one of the most active UDP-glucuronosyltransferases, involved in drug metabolism and in maintaining homeostasis of endogenous compounds 19. The expression of keratins is highly dependent on the state of cell development and differentiation and varies within different types of epithelia 20-22. For example, the incidence of a positive expression for KRT7 and KRT17 was significantly higher in desmoplastic malignant mesothelioma than in fibrous pleuritis 23. Fibroblast growth factor-inducible 14 (FN14) protein expression was associated with the invasive and metastatic potential of NSCLC and promotes hepatocyte growth factor receptor (HGFR/MET)-driven cell invasion 24. Our Northern blot validation of gene expression profiles confirmed that KRT-7 and FN14 were upregulated genes that responded later in KB-3 cells.

On the other hand, Figure 2 also shows that Cluster B of genes in the transcriptional program of KB-3 cells in early response to vinblastine treatment share similar expression patterns in transcript levels, which mRNA levels increase at 2, 4, and 8 h with vinblastine treatment, includes IER3, ID1, SCO2, SP140, DUSP6, ADRM1, U2AF1, NUDEL, SLC26A6, BCAA, PPID, SKD1, SPHK1, and SGK, which are known or likely to be involved in stress response, apoptosis, and PI3K/Akt signal transduction pathways. IER3 (early response gene immediate early response 3), which is induced by various stimuli, such as growth factors, cytokines, ionizing radiation, viral infection, and other types of cellular stress, plays a complex and to some extent contradictory role in cell cycle control and apoptosis 25. The lack of IER3 expression is associated with a deregulation of the NF-κB and PI3K/Akt pathways 26, 27, which represents a novel mechanism of Nrf2 regulation that may be lost in tumors and by which IER3 exerts its stress-adaptive and tumor-suppressive activity 28. Additionally, inhibitor of DNA binding (ID) family members are key regulatory proteins in a wide range of developmental and cellular processes and function by inhibiting target proteins that include the basic helix-loop-helix transcription factors 29. ID1 and ID3 function together to govern colon cancer-initiating cell self-renewal through p21 driven cell-cycle restriction 30. Based on the bioinformatics model analysis, we demonstrated that four major TFs (AP1, NFKB1, RELA, and TP53) and the downstream genes likely form regulatory networks to modulate genes expression programs related with the VBL-induced response in cervical cancer cells. Two members of NF-κB, RELA and NFKB1, can regulate IER3 and p21, providing evidence for involvement of this oncogenic TF in response of KB-3 cells to VBL through targeting apoptosis signaling. Our Northern blot analyses further confirmed that IER3 and ID1 were upregulated genes those responded early in KB-3 cells.

Last, we demonstrated that 14 genes, deregulated in VBL-treated KB-3 cells, were involved in five signaling pathways including protein ubiquitination pathway (HSPA8, PSMD11, PSMC1, HLA-C, PSMD1, PSMD8), RhoGDI signaling (RHOC, ITGA5, RHOF, ACTG1 and MSN), agranulocyte adhesion and biapedesis (MYH9, CLDN1, ITGA5, ACTG1 and MSN), integrin signaling (RHOC, ITGA5, CAPN2, RHOF and ACTG10), and actin nucleation by ARP-WASP complex (RHOC, ITGA5, RHOF) (Table 2). Gene network analysis reveals that cancer-related TFs are able to regulate these pathway genes, such as PSMD1, PSMD11, ITGA5 and MSN (Fig. 4). These functional pathways will help us understand the response of human cervical carcinoma to the biological effects of microtubule inhibitor as chemotherapy.

In summary, the present results represent an important advancement and provide a sound basis for further exploration, but we also recognize that in vitro results almost certainly represent a distorted and incomplete rendering of the normal physiological response of KB-3 cells to microtubule inhibitor. Transcriptional profiles and pathway analysis offer an opportunity to generate functional data on a genome-wide scale. Thus, mediating protein ubiquitination, RhoGDI signaling, and integrin signaling pathways may have a direct and effective impact on human cervical carcinoma treatment with microtubule inhibitor.