This work was supported by the Jingzhou Science and Technology Bureau Foundation (no. 2020HC06).

The study was completely in conformity with the publication guidelines provided by TCGA (https://cancergenome.nih.gov/publications/publicationguidelines). See the&#160;Table of Materials for details related to all materials, reagents, and instruments used in this protocol.
1. Data source and bioinformatics analysis
<ol>
	<li>Obtain RNA sequencing data from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov)&#160;for cluster analysis. Use the GEPIA (http://gepia.cancer-pku.cn/), a web-based tool to re-compute raw RNA-Seq data of samples from TCGA projects, to screen for candidate cancer drug targets with a standard processing pipeline. 
	NOTE: The GEPIA website is freely available to all users.</li>
	<li>Access the GEPIA website homepage and click on Cancer Type Analysis, select the Differential genes analysis option, and set the following parameters: Cancer name = CESE (defined as Cervical squamous cell carcinoma and endocervical adenocarcinoma on this webpage), |Log2FC| Cutoff = 1, q-value Cutoff = 0.01, Differential Methods = ANOVA, and Chromosomal Distribution = both. Then, click on List to obtain a gene list that shows differential expression between tumor and normal groups. 
	NOTE: It requires an extensive literature review to identify differentially expressed candidate genes. In this study, considering the background from the available literature, we focus on the tumor gene of interest, FAM83A.</li>
	<li>Then, examine the expression of FAM83A in cervical cancer and normal tissues using GEPIA by proceeding to the Expression DIY option on the website and select Boxplot as the chart type. Input FAM83A in the gene parameter field and set the following parameters: |Log2FC| Cutoff value = 1; q-value Cutoff = 0.01. Select CESE as the cancer name and Match TCGA normal data for the Matched Normal data field, leaving all other settings at their default. Click on Plot and allow the website to generate a boxplot representing the differential expression of the FAM83A gene; save the boxplot for future reference and analysis.</li>
	<li>Finally, analyze the overall survival of patients bearing tumors with different levels of FAM83A expression&#160;in cervical cancer. Navigate to the Survival Plots option on the website, set the Gene to FAM83A, and select Overall Survival as the analysis type. Add the tumor name CESE, choose Months for the Axis Units, and leave all other parameters at their default settings. Click Plot and let the website generate a survival curve chart; save this chart for future reference and analysis.</li>
	<li>Take into account both the differential expression of FAM83A in tumors and its correlation with patient survival analysis to identify FAM83A as a potential therapeutic target gene in cervical cancer.</li>
</ol>
2. Cell-based experiments
<ol>
	<li>Cell culture
	<ol>
		<li>Culture human tumor cell lines in the modified medium at 37 &#176;C in a humidified 5% CO2 atmosphere. 
		NOTE: Refer to the Table of Materials for cell line information and culture medium components.</li>
	</ol>
	</li>
	<li>Cell transfection
	<ol>
		<li>Use siRNA to knock down FAM83A expression in the cells. 
		NOTE: See Table 1 for the specific siRNA sequence.</li>
		<li>Seed cells in 6-well plates and incubate with the mixture of 50 nM si-FAM83A or siRNA-NC and 8 &#181;L of transfection agent or only 8 &#181;L of transfection agent for 4-6 h after attaining 50%-70% confluency. Add only serum-free DMEM to the negative control.</li>
		<li>After the incubation, replace the medium containing the transfection agent with DMEM + 10% fetal calf serum. Further, 48 h after transfection, treat with 5 &#181;M cisplatin (DDP) for 24 h as intended and harvest all the cells for total RNA extraction and qRT-PCR analysis.</li>
	</ol>
	</li>
</ol>
3. EdU detection for cell proliferation assay
NOTE: Use the EdU Cell Proliferation Kit to assess cell proliferation in vitro according to the manufacturer&#39;s instructions.
<ol>
	<li>Seed the cells in 6-well plates and incubate them with 10 &#181;M EdU solution for 2 h. Fix the cells with 4% paraformaldehyde for 15 min at room temperature (RT) and permeabilize with 0.3% Triton X-100 in PBS for 10 min at RT.</li>
	<li>Remove the permeabilization buffer and add 500 &#181;L of 1x reaction solution. Then, incubate for 30 min at RT in the dark for nuclear staining.</li>
	<li>Stain cells with 4&#39;,6-diamidino-2-phenylindole (DAPI) and visualize them under a fluorescence microscope at 200x magnification. Stain the nuclei of the proliferating cells with EdU and examine them for red fluorescence.</li>
	<li>Finally, use software to quantify the results by counting at least 10 random fields and calculate the proliferation rates using the ratio of the fluorescent-positive cells to total cells.</li>
</ol>
4. Wound healing assay
<ol>
	<li>Seed cells in 6-well plates at a density of 2 &#215; 105 cells per well.</li>
	<li>When the cells attain 90% confluency, use a sterile 10 &#181;L micropipette tip to gently create a linear scratch in the cell monolayer. Gently rinse the wells with sterile PBS to remove any detached cells and debris caused by scratching.</li>
	<li>Further, incubate the cells in medium containing 1% FBS. Then, observe and take images with the inverted microscope for the healing of the wounded region at 12, 24, and 48 h.</li>
</ol>
5. Porous membrane insert assay
<ol>
	<li>Prepare a cell suspension in cell culture medium at the desired cell concentration (containing 4 &#215; 104 cells), including transfected or control C33a cells and HeLa. Add the cell suspension to the top chamber of the membrane inserts, ensuring even distribution. Add complete medium to the lower chamber.</li>
	<li>After incubation for 24 h, use methyl alcohol and 0.1% crystal violet for the fixation and staining of the cells migrating to the lower chambers, respectively.</li>
	<li>Use an inverted microscope to take images and then analyze the number of migrating cells by counting at least 10 random fields with ImageJ.
	<ol>
		<li>For the number analysis with ImageJ, open the image in ImageJ software, go to the Image tab in the tool menu, select Adjust, and then click on Threshold. Adjust the threshold settings, if necessary, until only the cells are visible against a white background.</li>
		<li>Click on Apply in the Threshold window to apply these settings to the image. Now, select the Wand (tracing) tool from the toolbar (located at the top of the window).</li>
		<li>Click on the area of the image where the cells are located to select all the cells in the thresholded image. After the cells are highlighted, go to Analyze in the Tool menu and then, click on Analyze Particles. In the Analyze Particles window, enter the desired size (e.g., 0 - infinity) and circularity values (e.g., 0.00 - 1.00) according to the experimental requirements to include all cells in the count.</li>
		<li>Click OK and wait for the software to count the cells and for a new window to appear with the analysis results.</li>
	</ol>
	</li>
</ol>
6. Colony formation assay
<ol>
	<li>Seed 2 &#215; 105 cells per well of Si-NC and Si-FAM83A groups in a 6-well plate with or without 5 &#181;M cisplatin (DDP) treatment for 24 h.</li>
	<li>Following a 24 h period of drug treatment, detach the cells using 0.25% trypsin and resuspend them in Dulbecco&#39;s modified Eagle medium (DMEM) containing 10% FBS. Seed the resuspended cells in a 6-well plate at a density of 1 &#215; 103 cells per well, and then incubate in a 5% CO2 incubator at a temperature of 37 &#176;C.</li>
	<li>Following the incubation for ~7-10 days when colonies are visible, fix the cells with 4% Polyoxymethylene and stain them with Giemsa staining solution for 20 min.</li>
	<li>Wash the cells slightly and let the plates air dry. Take images of the colony clusters by microscope and count the number of colonies that contain more than 50 cells.</li>
</ol>
7. Mitochondrial membrane depolarization (MMP) analysis using JC-1 dye
<ol>
	<li>Seed 1.2 &#215; 106 tumor cells from Si-NC and Si-FAM83A groups into a six-well plate and culture for 24 h with or without 5 &#181;M cisplatin (DDP) treatment.</li>
	<li>Incubate with 1x JC-1 staining solution for 15-20 min under 5% CO2 at 37 &#176;C using a Mitochondrial membrane potential assay kit according to the manufacturer&#39;s instructions.</li>
	<li>Wash the cells and examine them under a fluorescent microscope at 200x magnification using excitation and emission wavelengths for JC-1 at ~490 nm and 590 nm, respectively. To visualize the JC-1 fluorescence, use appropriate filters, such as a blue excitation filter (450-490 nm) and a red emission filter (long-pass &#62; 590 nm) to selectively capture the emitted fluorescence signal.</li>
</ol>
8. Flow cytometric analysis of mPTP
<ol>
	<li>Seed 1.2 &#215; 106 tumor cells from Si-NC and Si-FAM83A groups into a six-well plate and culture for 24 h with or without 5 &#181;M cisplatin (DDP) treatment.</li>
	<li>Remove the culture medium from the cells, resuspend the cells in PBS, and add 300 &#181;L each (1x volume) of Calcein AM staining solution and fluorescence quenching working solution to cover the cells evenly. Incubate the cells at 37 &#176;C in a light-protected environment for 30-45 min, and replace the medium with fresh preheated culture medium at 37 &#176;C. Incubate the cells at 37 &#176;C in a light-protected environment for an additional 30 min to ensure complete hydrolysis of Calcein AM by intracellular esterases, resulting in the generation of intracellular green-fluorescent Calcein.</li>
	<li>Remove the culture medium, wash the cells 2-3x with PBS, and add detection buffer. Detect cell mPTP using flow cytometry and an excitation wavelength of 488 nm.</li>
</ol>
9. Flow cytometry
<ol>
	<li>Seed 1.2 &#215; 106 tumor cells from Si-NC and Si-FAM83A groups into a six-well plate and culture for 24 h with or without 5 &#181;M cisplatin (DDP) treatment.</li>
	<li>Following 24 h of drug treatment, re-suspend the cells in 200 &#181;L of binding buffer solution and gently mix 10 &#181;L of Annexin V-FITC and 5 &#181;L of propidium iodide (PI) into the cell suspension. Incubate the mixture for 15 min while avoiding light and add 300 &#181;L of binding buffer solution to the cells. Detect cell apoptosis using flow cytometry at an excitation wavelength of 488 nm.</li>
</ol>
10. RT-PCR analysis
<ol>
	<li>Seed 1.2 &#215; 106 tumor cells from Si-NC and Si-FAM83A groups into a six-well plate and culture for 24 h with or without 5 &#181;M cisplatin (DDP) treatment.</li>
	<li>Extract total RNA from the tumor cells using the RNA extraction reagent and convert the extracted RNA into complementary DNA (cDNA) with the cDNA Synthesis Mix by incubating at 42 &#176;C for 45 min and then at 95 &#176;C for 5 min.</li>
	<li>Prepare the PCR reaction mixture containing DNA polymerase, nucleotides, buffer, and the designed gene-specific primers. Add the cDNA template to the reaction mixture for the real-time PCR reaction and perform PCR on the real-time quantitative PCR instrument with the following thermocycling conditions: denaturation at 95 &#176;C for 30 s, followed by 40 cycles of 95 &#176;C for 5 s and 60 &#176;C for 30 s, and the melting curve stage at 95 &#176;C for 15 s, 60 &#176;C for 1 min, and 95 &#176;C for 15 s. Quantify the mRNA levels using the 2&#8722;&#916;&#916;Cq method using GAPDH as an internal control. 
	NOTE: The RT-PCR reaction system is shown in Table 1, and the primer sequences are shown in Table 1.
	<ol>
		<li>To calculate the 2&#8722;&#916;&#916;Cq value, measure the Cq values for the target gene and the reference gene (GAPDH) in the samples. Calculate the &#916;Cq by subtracting the reference gene&#39;s Cq value from the target gene&#39;s Cq value for each sample. Calculate the &#916;&#916;Cq by subtracting the &#916;Cq of a control sample from the &#916;Cq of each experimental sample. Finally, calculate the 2&#8722;&#916;&#916;Cq value by raising 2 to the power of the &#916;&#916;Cq value. 
		NOTE: The Cq value represents the cycle number at which the fluorescence signal reaches the set threshold.</li>
	</ol>
	</li>
</ol>
11. Western blot analysis
<ol>
	<li>Seed 1.2 &#215; 106 tumor cells from Si-NC and Si-FAM83A groups into a six-well plate and culture for 24 h with or without 5 &#181;M cisplatin (DDP) treatment.</li>
	<li>Collect the cultured cells and wash them with ice-cold phosphate-buffered saline (PBS) to remove any residual growth media or serum. Lyse the cells using RIPA Lysis Buffer containing phenylmethyl sulfonyl fluoride (PMSF) to release the cellular proteins. Incubate the cells on ice for a few minutes to ensure complete lysis and centrifuge at 4 &#176;C, 12,000 &#215; g for 15 min. Collect the supernatant to determine its protein concentration.</li>
	<li>Determine the protein concentration in the cell lysate using a BCA Protein Assay Kit according to the manufacturer&#39;s instructions. Mix the cell lysate with a loading buffer and heat the mixture at 95-100 &#176;C for 5-10 min to denature the proteins and ensure uniform separation on the gel.</li>
	<li>From each sample, load 30 &#181;g of total protein onto an SDS-PAGE gel and run the SDS-PAGE under a constant voltage of 80 V. Stop the electrophoresis when the bromophenol blue reaches the bottom of the separation gel.</li>
	<li>Transfer the separated proteins from the gel onto a polyvinyldifluoride membrane using a wet transfer system on an ice bath with a current of 200 mA for 1.5 h.</li>
	<li>Block the membrane with 5% nonfat milk in Tris-buffered saline with 0.05% Tween-20 buffer (TBST) for 1 h at RT and incubate overnight at 4 &#176;C with respective antibodies given in Table of Materials.</li>
	<li>Wash the membrane multiple times with TBST and incubate with the secondary antibodies (Table of Materials) for 1 h at RT, followed by washing with TBST. Visualize the protein bands using an enhanced chemiluminescence detection kit and imager system.</li>
</ol>
12. Statistical analysis
<ol>
	<li>As all experimental data points will be independent, present the data as the mean &#177; SD. Perform statistical analysis.</li>
	<li>Use one-way analysis of variance (ANOVA) for multiple comparisons and Dunnett&#39;s test for comparing each group with the control group. Use Student&#39;s t-test (unpaired two-tailed) for comparing two groups; consider P &#60; 0.05 to be significant.</li>
</ol>

Validation of FAM83A as a Target Gene in Cervical Cancer

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M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FAM83+family+oncogenes+are+broadly+involved+in+human+cancers:+an+integrative+multi-omics+approach.">FAM83 family oncogenes are broadly involved in human cancers: an integrative multi-omics approach.</a> Molecular Oncology. 11 (2), 167-179 (2017).</li><li>Gan, J., Meng, Q., Li, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Corrigendum:+systematic+analysis+of+expression+profiles+and+prognostic+significance+for+fam83+family+in+non-small-cell+lung+cancer.">Corrigendum: systematic analysis of expression profiles and prognostic significance for fam83 family in non-small-cell lung cancer.</a> Frontiers In Molecular Biosciences. 8, 653454 (2021).</li><li>Jin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+analysis+of+the+expression,+prognostic+significance,+and+function+of+fam83+family+members+in+breast+cancer.">Comprehensive analysis of the expression, prognostic significance, and function of fam83 family members in breast cancer.</a> World Journal of Surgical Oncology. 20 (1), 172 (2022).</li><li>Lin, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+prognostic+biomarkers+among+fam83+family+genes+in+human+ovarian+cancer+through+bioinformatic+analysis+and+experimental+verification.">Identification of prognostic biomarkers among fam83 family genes in human ovarian cancer through bioinformatic analysis and experimental verification.</a> Cancer Management and Research. 13, 8611-8627 (2021).</li><li>Ma, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+prognostic+and+therapeutic+biomarkers+among+fam83+family+members+for+pancreatic+ductal+adenocarcinoma.">Identification of prognostic and therapeutic biomarkers among fam83 family members for pancreatic ductal adenocarcinoma.</a> Disease Markers. 2021, 6682697 (2021).</li><li>Xu, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+profiling+of+cervical+RNA-binding+proteins+identifies+human+papillomavirus+regulation+of+rnaseh2a+expression+by+viral+e7+and+e2f1.">Genome-wide profiling of cervical RNA-binding proteins identifies human papillomavirus regulation of rnaseh2a expression by viral e7 and e2f1.</a> mBio. 10 (1), e02687-e02618 (2019).</li><li>Wong, R. 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The authors report no conflicts of interest in this work.

The investigation of tumor target genes is of utmost importance for both the prevention and treatment of cervical cancer. Understanding the specific genes that play a significant role in cervical cancer development and progression provides valuable insight into the underlying molecular mechanisms of the disease. Furthermore, identifying these target genes can lead to the development of novel therapeutic strategies and targeted therapies. In this study, we describe the use of TCGA dataset analysis to identify FAM83A <...

Cervical cancer is a global concern as it is one of the leading types of gynecological malignancy worldwide and is the major cause of cancer-related mortality in women1. Radical surgery and chemoradiotherapy are associated with high cure rates at the primary stage. However, treatment outcomes for patients at the advanced stage of cervical cancer who develop metastatic disease are very unfavorable2. Therefore, it is crucial to further understand the biological mechanisms underlying the migration and invasion of cervical cancer cells and identify potential therapeutic targets for the prevention and treatment of this disease.
Identifying target genes involved in cancer progression and finding ways to inhibit their expression or action present promising treatment options. In this study, we identified FAM83 as a cancer-causing gene and further investigated its inhibitory effects on C33a and HeLa cells. FAM83 family oncogenes (FAM83A-H) are widely reported in human cancers3,4. Recently, FAM83A was reported to be upregulated in lung5, breast6, ovarian7, and pancreatic8 cancers,indicating that FAM83A plays an important role in cancer progression via promoting the proliferation, invasion, stem-cell-like traits, and drug resistance in the tumor cells. Importantly, FAM83A was identified as one of the novel candidate genes associated with cervical lesion progression and carcinogenesis9. Despite the confirmation of elevated FAM83A expression in human cervical cancer cells, the specific impact and underlying mechanisms of FAM83A in cervical cancer remain unclear.
In this study, we outline the protocols involved in the identification of FAM83A as a tumor target gene in cervical cancer and use a small interfering RNA (siRNA) for the knockdown of the FAM83A gene in HeLa and C33a cells. 5-Ethynyl-2&#39;-deoxyuridine (EdU) staining was performed to determine the effects on tumor cell proliferation, while wound healing and porous membrane insert assays helped evaluate tumor cell migration and invasion abilities.
Western blotting was performed to determine the levels of apoptosis-related proteins, and JC-1 staining was employed to evaluate mitochondrial function alterations. Thus, we reported that FAM83A plays a critical role in cell proliferation, metastasis, and invasion in cervical cancer. Through PI3K/AKT-pathway-associated mitochondrial dysfunction and apoptosis, FAM83A knockdown sensitized cervical cancer cells to cisplatin (diaminedichloroplatinum, DDP). This study provides a new target for cervical cancer and possibly other cancers and a reference for the development of strategies to overcome the resistance of cancer cells to certain chemotherapeutic drugs.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr >
			<td colspan="4" >Cells and Medium Formulation</td>
		</tr>
		<tr >
			<td >C33a</td>
			<td >American Type Culture Collection</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >Hela</td>
			<td >American Type Culture Collection</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >Modified medium</td>
			<td ></td>
			<td ></td>
			<td >10% fetal bovine serum and + antibiotics (100 U/mL penicillin and 100 U/mL streptomycin)</td>
		</tr>
		<tr >
			<td >Antibody Information</td>
			<td ></td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >AKT</td>
			<td >4691, Cell Signaling Technology Inc.</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >Bcl2&#160;</td>
			<td >26593-1-AP, Proteintech Group, Inc</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >Caspase 3</td>
			<td >19677-1-AP, Proteintech Group, Inc</td>
			<td ></td>
			<td >&#8216;1:2,000</td>
		</tr>
		<tr >
			<td >cleaved-caspase3</td>
			<td >abs132005; Absin Bioscience Inc.</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >Cytc</td>
			<td >10993-1-AP; Proteintech Group</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >GAPDH&#160;</td>
			<td >10494-1-AP, Proteintech Group, Inc.</td>
			<td ></td>
			<td >&#8216;1:8,000</td>
		</tr>
		<tr >
			<td >mTOR</td>
			<td >2983, Cell Signaling Technology Inc.</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >PI3K</td>
			<td >4292, Cell Signaling Technology Inc</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >p-AKT&#160;</td>
			<td >4060, Cell Signaling Technology Inc.</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >p-mTOR (Ser2448)</td>
			<td >#5536, Cell Signaling Technology Inc.</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >p-PI3K p85 subunit</td>
			<td >17366, Cell Signaling Technology Inc.</td>
			<td ></td>
			<td >&#8216;1:1,000</td>
		</tr>
		<tr >
			<td >Secondary antibodies</td>
			<td >GB23303, Servicebio</td>
			<td ></td>
			<td >&#8216;1:2,000</td>
		</tr>
		<tr >
			<td >Materials</td>
			<td ></td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >6-well plate</td>
			<td >Corning, NPY</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >Alexa Fluor 555</td>
			<td >Beyotime</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >BCA Protein assay kit&#160;</td>
			<td >Beyotime, China&#160;</td>
			<td ></td>
			<td >P0011</td>
		</tr>
		<tr >
			<td >ChemiDoc XRS Imager System&#160;</td>
			<td >BioRad</td>
			<td ></td>
			<td ></td>
		</tr>
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			<td >Enhanced chemiluminescence detection kit&#160;</td>
			<td >Servicebio, Inc.,China</td>
			<td ></td>
			<td >cat. no. G2014</td>
		</tr>
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			<td >Fluorescence microscope&#160;</td>
			<td >Olympus Corporation, Tokyo, Japan</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td rowspan="2" >Hifair II 1st Strand cDNA Synthesis Super Mix&#160;</td>
			<td rowspan="2" >11123ES60, Yeasen Biotech o., Ltd., China</td>
			<td rowspan="2" ></td>
			<td ></td>
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			<td >Inverted microscope&#160;</td>
			<td >Olympus, Tokyo, Japan;</td>
			<td ></td>
			<td ></td>
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			<td >Millicell transwell inserts&#160;</td>
			<td >Millipore,Bedford, MA, USA</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >Mitochondrial membrane potential assay kit&#160;</td>
			<td >Beyotime, China</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >PMSF&#160;</td>
			<td >ST506, Beyotime Biotech, Jiangsu, China</td>
			<td ></td>
			<td >#ST506</td>
		</tr>
		<tr >
			<td rowspan="2" >Real-time quantitative PCR instrument&#160;</td>
			<td rowspan="2" >Applied Biosystems, Thermo Fisher Scientific. China.</td>
			<td rowspan="2" ></td>
			<td ></td>
		</tr>
		<tr >
			<td >RIPA Lysis Buffer&#160;</td>
			<td >Beyotime Biotech, Jiangsu, China</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td >TRIzol reagent</td>
			<td >Invitrogen</td>
			<td ></td>
			<td >15596026</td>
		</tr>
		<tr >
			<td >TRIzol reagent&#160;</td>
			<td >Takara Bio Inc., Otsu, Japan</td>
			<td ></td>
			<td ></td>
		</tr>
		<tr >
			<td colspan="2" >Software</td>
			<td></td>
			<td></td>
		</tr>
		<tr >
			<td >Image-Pro</td>
			<td >&#160;plus 6.0&#160;&#160;</td>
			<td></td>
			<td></td>
		</tr>
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knockdown of fam83a to verify its role in cervical cancer cell growth and cisplatin sensitivity

The exploration of tumor target genes holds paramount importance for the prevention and treatment of cervical cancer. In this study, we outline the steps involved in the identification of a tumor target gene FAM83A in cervical cancer. First, the Cancer Genome Atlas dataset was employed to validate the expression and prognostic significance of FAM83A in women. A small interfering RNA (siRNA) was used for knockdown of the FAM83A gene in HeLa and C33a cells. Next, 5-ethynyl-2&#39;-deoxyuridine (EdU) staining was conducted to determine the effects on the proliferation capabilities of the tumor cells. Wound healing and porous membrane insert assays were performed to evaluate tumor cell migration and invasion abilities.
Western blotting was used to quantify apoptosis-related protein levels. JC-1 staining was employed to evaluate mitochondrial function alterations. Furthermore, cisplatin (diaminedichloroplatinum, DDP) intervention was used to assess the therapeutic potential of the target gene. Flow cytometry and colony formation assays were conducted to further validate the anticancer characteristics of the gene. As a result, FAM83A knockdown was shown to inhibit the proliferation, migration, and invasion of cervical cancer cells and sensitize these cells to cisplatin. These comprehensive methodologies collectively validate FAM83A as a tumor-associated target gene, holding promise as a potential therapeutic target in the prevention and treatment of cervical cancer.

TCGA database analysis and PCR validation
From the TCGA database analysis, we conducted a comparative analysis of mRNA expression levels in 306 cervical cancer cell samples and 13 normal cell samples to investigate the differential expression of FAM83A. FAM83A was upregulated in cervical cancer, while its expression in normal cervical tissue was negligible (Figure 1A</stron...

Watch this Scientific Journal Video about Exploring the Role of FAM83A in Cervical Cancer at JoVE.com

Author Spotlight: Exploring the Role of FAM83A in Cervical Cancer 

Here, we show the procedures for FAM83A knockdown; the assays to detect its effects on proliferation, migration, and invasion of cervical cancer cells; and the sensitization of these cells to cisplatin. This study provides a promising target gene for cervical cancer and a reference for further drug research.

Knockdown of FAM83A to Verify Its Role in Cervical Cancer Cell Growth and Cisplatin Sensitivity

The exploration of tumor target genes holds paramount importance for the prevention and treatment of cervical cancer. In this study, we outline the steps ...

knockdown-fam83a-to-verify-its-role-cervical-cancer-cell-growth

Research

JoVE Journal

Cancer Research

835 Views.  Yangtze University. Here, we show the procedures for FAM83A knockdown; the assays to detect its effects on proliferation, migration, and invasion of cervical cancer cells; and the sensitization of these cells to cisplatin. This study provides a promising target gene for cervical cancer and a reference for further drug research.

Video: Author Spotlight: Exploring the Role of FAM83A in Cervical Cancer 

A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model

The intravenous injection of C1498 cells into syngeneic or congenic mice has been performed since 1941. These injections result in the development of acute leukemia. However, the nature of this disease has not been well documented in the literature. Here, we provide a technical protocol for characterizing C1498 cells in vitro and for determining the nature of the induced leukemia in vivo. The first part of this procedure is focused on determining the hematopoietic lineage and the stage of differentiation of cultured C1498 cells. To achieve this, multi-parametric flow cytometric staining is used to detect hematopoietic cell markers. Immunofluorescence microscopy, cytochemistry and a May-Gr&#252;nwald Giemsa staining are then performed to assess the expression of myeloperoxidase, the activity of esterases and cellular morphology, respectively. The second part of this protocol is dedicated to describing the leukemia disease that is induced in vivo. The latter can be achieved by determining the frequencies of leukemic and inherent cells in the blood, hematopoietic organs (e.g., bone marrow and spleen) and non-lymphoid tissues (e.g., the liver and lungs) using specific staining and flow cytometry analyses. The nature of the leukemia is then confirmed using May-Gr&#252;nwald Giemsa staining and staining for specific esterases in the bone marrow. Here, we present the results that were obtained using this protocol in age-matched C1498- and PBS-injected mice.

This manuscript provides a technical procedure that can be used to characterize C1498 cell cultures in vitro and the acute leukemia induced in mice after their injection. Phenotypic and functional analyses are performed using flow cytometry, immunofluorescence microscopy, cytochemistry and May-Gr&#252;nwald Giemsa staining.

The intravenous injection of C1498 cells into syngeneic or congenic mice has been performed since 1941. These injections result in the development of ...

Detection of Mitochondria Membrane Potential to Study CLIC4 Knockdown-induced HN4 Cell Apoptosis In Vitro

Depletion of the mitochondrial membrane potential (MMP, &#916;&#936;m) is considered the earliest event in the apoptotic cascade. It even occurs ahead of nuclear apoptotic characteristics, including chromatin condensation and DNA breakage. Once the MMP collapses, cell apoptosis will initiate irreversibly. A series of lipophilic cationic dyes can pass through the cell membrane and aggregate inside the matrix of mitochondrion, and serve as fluorescence marker to evaluate MMP change. As one of the six members of the Cl- intracellular channel (CLIC) family, CLIC4 participates in the cell apoptotic process mainly through the mitochondrial pathway. Here we describe a detailed protocol to measure MMP via monitoring the fluorescence fluctuation of Rhodamine 123 (Rh123), through which we study apoptosis induced by CLIC4 knockdown. We discuss the advantages and limitations of the application of confocal laser scanning and normal fluorescence microscope in detail, and also compare it with other methods.

Detection of Mitochondria Membrane Potential to Study CLIC4 Knockdown-induced HN4 Cell Apoptosis In Vitro

Here we present a detailed protocol for the application of rhodamine 123 to identify the mitochondrial membrane potential (MMP) and study CLIC4 knockdown-induced HN4 cell apoptosis in vitro. Under common fluorescence microscope and confocal laser scanning fluorescence microscope, the real-time change of the MMP was recorded.

Depletion of the mitochondrial membrane potential (MMP, &#916;&#936;m) is considered the earliest event in the apoptotic cascade. It even occurs ahead of ...

Conditional Knockdown of Gene Expression in Cancer Cell Lines to Study the Recruitment of Monocytes/Macrophages to the Tumor Microenvironment

siRNA and shRNA-mediated knock down (KD) methods of regulating gene expression are invaluable tools for understanding gene and protein function. However, in the case that the KD of the protein of interest has a lethal effect on cells or the anticipated effect of the KD is time-dependent, unconditional KD methods are not appropriate. Conditional systems are more suitable in these cases and have been the subject of much interest. These include Ecdysone-inducible overexpression systems, Cytochrome P-450 induction system1, and the tetracycline regulated gene expression systems. 
The tetracycline regulated gene expression system enables reversible control over protein expression by induction of shRNA expression in the presence of tetracycline. In this protocol, we present an experimental design using functional Tet-ON system in human cancer cell lines for conditional regulation of gene expression. We then demonstrate the use of this system in the study of tumor cell-monocyte interaction.

This protocol serves as a scheme for setting up a functional Tet-ON system in cancer cell lines and its subsequent use, in particular for studying the role of tumor cell-derived proteins in recruitment of monocytes/macrophages to the tumor microenvironment.

siRNA and shRNA-mediated knock down (KD) methods of regulating gene expression are invaluable tools for understanding gene and protein function. However, ...

Evaluating the Role of Mitochondrial Function in Cancer-related Fatigue

Fatigue is a common and debilitating condition that affects most cancer patients. To date, fatigue remains poorly characterized with no diagnostic test to objectively measure the severity of this condition. Here we describe an optimized method for assessing mitochondrial function of PBMCs collected from fatigued cancer patients. Using a compact extracellular flux system and sequential injection of respiratory inhibitors, we examined PBMC mitochondrial functional status by measuring basal mitochondrial respiration, spare respiratory capacity, and energy phenotype, which describes the preferred energy pathway to respond to stress. Fresh PBMCs are readily available in the clinical setting using standard phlebotomy. The entire assay described in this protocol can be completed in less than 4 hours without the involvement of complex biochemical techniques. Additionally, we describe a normalization method that is necessary for obtaining reproducible data. The simple procedure and normalization methods presented allow for repeated sample collection from the same patient and generation of reproducible data that can be compared between time points to evaluate potential treatment effects.

Our goal was to develop a practical protocol to evaluate mitochondrial dysfunction associated with fatigue in cancer patients. This innovative protocol is optimized for clinical use involving only standard phlebotomy and basic laboratory procedures.

Fatigue is a common and debilitating condition that affects most cancer patients. To date, fatigue remains poorly characterized with no diagnostic test to ...

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

Phosphoproteomics involves the large-scale study of phosphorylated proteins. Protein phosphorylation is a critical step in many signal transduction pathways and is tightly regulated by kinases and phosphatases. Therefore, characterizing the phosphoproteome may provide insights into identifying novel targets and biomarkers for oncologic therapy. Mass spectrometry provides a way to globally detect and quantify thousands of unique phosphorylation events. However, phosphopeptides are much less abundant than non-phosphopeptides, making biochemical analysis more challenging. To overcome this limitation, methods to enrich phosphopeptides prior to the mass spectrometry analysis are required. We describe a procedure to extract and digest proteins from tissue to yield peptides, followed by an enrichment for phosphotyrosine (pY) and phosphoserine/threonine (pST) peptides using an antibody-based and/or titanium dioxide (TiO2)-based enrichment method. After the sample preparation and mass spectrometry, we subsequently identify and quantify phosphopeptides using liquid chromatography-mass spectrometry and analysis software.

This protocol describes a procedure to extract and enrich phosphopeptides from prostate cancer cell lines or tissues for an analysis of the phosphoproteome via mass spectrometry-based proteomics.

Phosphoproteomics involves the large-scale study of phosphorylated proteins. Protein phosphorylation is a critical step in many signal transduction ...

Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence

The demonstration of tyrosine kinase inhibitors (TKIs) in treating chronic myeloid leukemia (CML) has heralded a new era in cancer therapeutics. However, a small population of cells does not respond to TKI treatment, resulting in minimal residual disease (MRD); even the most potent TKIs fail to eradicate these cells. These MRD cells serve as a reservoir to develop resistance to therapy. Why TKI treatment is ineffective against MRD cells is not known. Growth factor signaling is implicated in supporting the survival of MRD cells during TKI treatment, but a mechanistic understanding is lacking. Recent studies demonstrated that an elevated c-Fos and Dusp1 expression as a result of convergent oncogenic and growth factor signaling in MRD cells mediate TKI resistance. The genetic and chemical inhibition of c-Fos and Dusp1 renders CML exquisitely sensitive to TKIs and cures CML in both genetic and humanized mouse models. We identified these target genes using multiple microarrays from TKI-sensitive and -resistant cells. Here, we provide methods for target validation using in vitro and in vivo mouse models. These methods can easily be applied to any target for genetic validation and therapeutic development.

Here, we describe protocols for the genetic and chemical validation of c-Fos and Dusp1 as a drug target in leukemia using in vitro and in vivo genetic and humanized mouse models. This method can be applied to any target for genetic validation and therapeutic development.

The demonstration of tyrosine kinase inhibitors (TKIs) in treating chronic myeloid leukemia (CML) has heralded a new era in cancer therapeutics. However, ...

Repression of Multiple Myeloma Cell Growth In Vivo by Single-wall Carbon Nanotube (SWCNT)-delivered MALAT1 Antisense Oligos

The single-wall carbon nanotube (SWCNT) is a new type of nanoparticle, which has been used to deliver multiple kinds of drugs into cells, such as proteins, oligonucleotides, and synthetic small-molecule drugs. The SWCNT has customizable dimensions, a large superficial area, and can flexibly bind with drugs through different modifications on its surface; therefore, it is an ideal system to transport drugs into cells. Long noncoding RNAs (lncRNAs) are a cluster of noncoding RNA longer than 200 nt, which cannot be translated to protein but play an important role in biological and pathophysiological processes. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a highly conserved lncRNA. It was demonstrated that higher MALAT1 levels are related to the poor prognosis of various cancers, including multiple myeloma (MM). We have revealed that MALAT1 regulates DNA repair and cell death in MM; thus, MALAT1 can be considered as a therapeutic target for MM. However, the efficient delivery of the antisense oligo to inhibit/knockdown MALAT1 in vivo is still a problem. In this study, we modify the SWCNT with PEG-2000 and conjugate an anti-MALAT1 oligo to it, test the delivery of this compound in vitro, inject it intravenously into a disseminated MM mouse model, and observe a significant inhibition of MM progression, which indicates that SWCNT is an ideal delivery shuttle for anti-MALAT1 gapmer DNA.

Repression of Multiple Myeloma Cell Growth In Vivo by Single-wall Carbon Nanotube SWCNT-delivered MALAT1 Antisense Oligos

This manuscript describes the synthesis of a single-wall carbon nanotube (SWCNT)-conjugated MALAT1 antisense gapmer DNA oligonucleotide (SWCNT-anti-MALAT1), which demonstrates the reliable delivery of the SWCNT and the potent therapeutic effect of anti-MALAT1 in vitro and in vivo. Methods used for synthesis, modification, conjugation, and injection of SWCNT-anti-MALAT1 are described.

The single-wall carbon nanotube (SWCNT) is a new type of nanoparticle, which has been used to deliver multiple kinds of drugs into cells, such as ...

In Vivo Inhibition of MicroRNA to Decrease Tumor Growth in Mice

MicroRNAs (miRNAs) are important regulators of gene expression through their ability to destabilize mRNA and inhibit translation of target mRNAs. An ever-increasing number of studies have identified miRNAs as potential biomarkers for cancer diagnosis and prognosis, and also as therapeutic targets, adding an extra dimension to cancer evaluation and treatment. In the context of thyroid cancer, tumorigenesis results not only from mutations in important genes, but also from the overexpression of many miRNAs. Accordingly, the role of miRNAs in the control of thyroid gene expression is evolving as an important mechanism in cancer. Herein, we present a protocol to examine the effects of miRNA-inhibitor delivery as a therapeutic modality in thyroid cancer using human tumor xenograft and orthotopic mouse models. After engineering stable thyroid tumoral cells expressing GFP and luciferase, cells are injected into nude mice to develop tumors, which can be followed by bioluminescence. The in vivo inhibition of a miRNA can reduce tumor growth and upregulate miRNA gene targets. This method can be used to assess the importance of a determined miRNA in vivo, in addition to identifying new therapeutic targets.

This protocol describes xenograft and orthotopic mouse models of human thyroid tumorigenesis as a platform to test microRNA-based inhibitor treatments. This approach is ideal to study the function of non-coding RNAs and their potential as new therapeutic targets.

MicroRNAs (miRNAs) are important regulators of gene expression through their ability to destabilize mRNA and inhibit translation of target mRNAs. An ...

Establishment of Gastric Cancer Patient-derived Xenograft Models and Primary Cell Lines

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. Although there are many established gastric cancer cell lines and many conventional transgenic mouse models for preclinical research, the disadvantages of these in vitro and in vivo models limit their applications. Because the characteristics of these models have changed in culture, they no longer model tumor heterogeneity, and their responses have not been able to predict responses in humans. Thus, alternative models that better represent tumor heterogeneity are being developed. Patient-derived xenograft (PDX) models preserve the histologic appearance of cancer cells, retain intratumoral heterogeneity, and better reflect the relevant human components of the tumor microenvironment. However, it usually takes 4-8 months to develop a PDX model, which is longer than the expected survival of many gastric patients. For this reason, establishing primary cancer cell lines may be an effective complementary method for drug response studies. The current protocol describes methods to establish PDX models and primary cancer cell lines from surgical gastric cancer samples. These methods provide a useful tool for drug development and cancer biology research.

The current protocol describes methods to establish patient-derived xenograft (PDX) models and primary cancer cell lines from surgical gastric cancer samples. The methods provide a useful tool for drug development and cancer biology research.

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. ...

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to interference with proper development and may even result in initiation or progression of cancers in certain cases. Because this pathway serves complex and versatile functions, it can be studied extensively through many different approaches. Of these, bioinformatics provides an undeniably cost-efficient, approachable, and user-friendly method of study. Bioinformatics is a useful way to extract smaller pieces of information from large-scale datasets. Through the implementation of various bioinformatics approaches, researchers can quickly, reliably, and efficiently interpret these large datasets, yielding insightful applications and scientific discoveries. Here, a protocol is presented for integration of bioinformatics approaches to investigate the role of Notch signaling in ovarian cancer. Furthermore, bioinformatics findings are validated through experimentation.

Bioinformatics is a useful way to process large-scale datasets. Through the implementation of bioinformatics approaches, researchers can quickly, reliably, and efficiently obtain insightful applications and scientific discoveries. This article demonstrates the utilization of bioinformatics in ovarian cancer research. It also successfully validates bioinformatics findings through experimentation.

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to ...