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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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 as a target gene&#160;and investigate its mechanistic roles in cervical cancer. We observe a significantly high expression of FAM83A in cervical cancer cells, which is associated with poor prognosis of patients with cervical cancer. A series of cell-based experiments revealed high FAM83A expression in cervical cancer cells, which plays a critical role in regulating cell proliferation, migration, and invasion. Suppression of FAM83A expression inhibited proliferation and migration and promoted apoptosis in cervical cancer cells.
Constructing FAM83A-knockdown cervical cancer cell lines is the most critical step in this study. siRNAs are designed and validated according to the target gene sequences to ensure their effectiveness. Further, the cervical cancer cell lines are transfected using siRNA. The success rate of plasmid transfection is crucial for subsequent experiments, which require strict control of cell density and plasmid density. Further, the transfection efficiency is observed under a fluorescence microscope to ensure that subsequent experiments can be conducted. Western blotting and qRT-PCR results confirm that the inhibition of FAM83A suppressed the PI3K/AKT pathway and induced mitochondrial dysfunction. We hypothesize that the mechanism involves the inhibition of the translocation of Bax from the cytoplasm to the mitochondria by PI3K/AKT, which promotes cell survival13.
Advances in the treatment of cervical cancer have made it possible to develop new target molecules. The identification of a novel oncogene can be applied therapeutically to improve the clinical prognosis of cervical malignancies14,15. In this study, we successfully constructed a FAM83A knockdown system in HeLa and C33a cells and verified the effects of FAM83A knockdown on cervical cancer cells at the cellular level. We propose that the inhibition of FAM83A can be used to treat cervical cancer.
However, this study has some limitations. First, this study only performs gene screening through the database and lacks pathological data of clinical patients. Second, this study has only validated the effect in vitro, and further in vivo studies were needed to verify the results. Nevertheless, the methods of target gene identification, gene knockout, and cellular validation to identify targeted therapeutic loci elaborated in this study can provide research ideas for the discovery and validation of target genes for other diseases.
In summary, FAM83A was overexpressed and correlated with a worse prognosis in cervical cancer. FAM83A regulates the proliferation, migration, and invasion of cervical cancer cells. Suppression of FAM83A-induced mitochondrial dysfunction and apoptosis, sensitizing cervical cancer cells to cisplatin. These results indicated that FAM83A is a suitable research target for the treatment of cervical cancer. This study contributed to the advances in adjuvant chemotherapyaiming at improving the prognosis of patients with cervical cancer.

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><tbody><tr><td>&lt;strong&gt;Cells and Medium Formulation&lt;/strong&gt;</td><td></td><td></td><td></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>&lt;strong&gt;Antibody Information&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>AKT</td><td>4691, Cell Signaling Technology Inc.</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>Bcl2&amp;nbsp;</td><td>26593-1-AP, Proteintech Group, Inc</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>Caspase 3</td><td>19677-1-AP, Proteintech Group, Inc</td><td></td><td>&amp;lsquo;1:2,000</td></tr><tr><td>cleaved-caspase3</td><td>abs132005; Absin Bioscience Inc.</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>Cytc</td><td>10993-1-AP; Proteintech Group</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>GAPDH&amp;nbsp;</td><td>10494-1-AP, Proteintech Group, Inc.</td><td></td><td>&amp;lsquo;1:8,000</td></tr><tr><td>mTOR</td><td>2983, Cell Signaling Technology Inc.</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>PI3K</td><td>4292, Cell Signaling Technology Inc</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>p-AKT&amp;nbsp;</td><td>4060, Cell Signaling Technology Inc.</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>p-mTOR (Ser2448)</td><td>#5536, Cell Signaling Technology Inc.</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>p-PI3K p85 subunit</td><td>17366, Cell Signaling Technology Inc.</td><td></td><td>&amp;lsquo;1:1,000</td></tr><tr><td>Secondary antibodies</td><td>GB23303, Servicebio</td><td></td><td>&amp;lsquo;1:2,000</td></tr><tr><td>&lt;strong&gt;Materials&lt;/strong&gt;</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&amp;nbsp;</td><td>Beyotime, China&amp;nbsp;</td><td></td><td>P0011</td></tr><tr><td>ChemiDoc XRS Imager System&amp;nbsp;</td><td>BioRad</td><td></td><td></td></tr><tr><td>Enhanced chemiluminescence detection kit&amp;nbsp;</td><td>Servicebio, Inc.,China</td><td></td><td>cat. no. G2014</td></tr><tr><td>Fluorescence microscope&amp;nbsp;</td><td>Olympus Corporation, Tokyo, Japan</td><td></td><td></td></tr><tr><td>Hifair II 1st Strand cDNA Synthesis Super Mix&amp;nbsp;</td><td>11123ES60, Yeasen Biotech o., Ltd., China</td><td></td><td></td></tr><tr><td>Inverted microscope&amp;nbsp;</td><td>Olympus, Tokyo, Japan;</td><td></td><td></td></tr><tr><td>Millicell transwell inserts&amp;nbsp;</td><td>Millipore,Bedford, MA, USA</td><td></td><td></td></tr><tr><td>Mitochondrial membrane potential assay kit&amp;nbsp;</td><td>Beyotime, China</td><td></td><td></td></tr><tr><td>PMSF&amp;nbsp;</td><td>ST506, Beyotime Biotech, Jiangsu, China</td><td></td><td>#ST506</td></tr><tr><td>Real-time quantitative PCR instrument&amp;nbsp;</td><td>Applied Biosystems, Thermo Fisher Scientific. China.</td><td></td><td></td></tr><tr><td>RIPA Lysis Buffer&amp;nbsp;</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&amp;nbsp;</td><td>Takara Bio Inc., Otsu, Japan</td><td></td><td></td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Image-Pro</td><td>&amp;nbsp;plus 6.0&amp;nbsp;&amp;nbsp;</td><td></td><td></td></tr></tbody></table>

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). To gain further insights into the prognostic implications of FAM83A expression, we performed a Kaplan-Meier curve analysis. Strikingly, patients with higher FAM83A expression displayed a markedly worse overall survival (Figure 1B). To determine the role of FAM83A in cervical cancer, we examined FAM83A expression levels in two cervical cancer cell lines, HeLa and C33a, as well as in an immortalized cervical cell line, End1/E6E7. Using qRT-PCR, we observed a significant overexpression of FAM83A in the HeLa and C33a cell lines compared with the End1/E6E7 cell line (Figure 1C). These findings underscore the importance of FAM83A in the context of cervical cancer.
Proliferation and apoptosis experiments to verify the biological function of FAM83A in cervical cancer
To investigate the functional role of FAM83A in cervical cancer, we utilized siRNA-mediated knockdown of FAM83A in C33a and HeLa cells (Figure 2A,B). We then assessed the impact of FAM83A suppression on cell proliferation. EdU staining revealed that decreased expression of FAM83A significantly inhibited cell proliferation in both C33a and HeLa cells (Figure 2C-F). Immortal malignant cells are associated with extremely low apoptosis rates10. Therefore, the levels of anti- and proapoptotic proteins were assessed in the control and si-FAM83A-treated cervical cancer cells. Western blot analyses revealed that suppressed expression of FAM83A increased the expression of Bax and cleaved caspase3 proteins (proapoptotic proteins), decreased the expression of Bcl-2 (antiapoptotic protein), and increased Cytc release (Figure 2C-E), which is consistent withthe observation that antiapoptotic proteins regulate apoptosis by blocking the mitochondrial release of Cytc&#160;(Figure 2G-J)10. Collectively, these data suggested that FAM83A plays an important role in regulating cervical cancer cell proliferation and apoptosis.
Wound-healing and porous membrane insert assays to verify the biological function of FAM83A in cervical cancer
Wound-healing and membrane insert assays were performed to explore the effects of FAM83A suppression on the migration and invasion of cervical cancer cells. The results revealed that cervical cancer cells with suppressed FAM83A expression migrated (Figure 3A-D) and invaded (Figure 3E-H) less efficiently than the control cells.
Effects on mitochondrial function and PI3K/AKT signaling pathway 
Given the role of PI3K/AKT in the induction of cell apoptosis, we aimed to determine whether the suppression of FAM83A expression would inhibit the constitutive phosphorylation of the PI3K/Akt/mTOR pathway in cervical cancer. Suppression of FAM83A significantly inhibited the key phosphorylated protein levels in the PI3K/Akt/mTOR pathway including p-PI3K, p-Akt, and p-mTOR in c333a and HeLa cells (Figure 4A-D). Mitochondria are the organelles responsible for cellular metabolism and cell apoptosis induction. Accumulating evidence indicates that cancer cell apoptosis involves mitochondrial dysfunction through the intrinsic mitochondrial pathway due to enhanced mitochondrial permeability and the release of proapoptotic molecules such as Cytc into the cytoplasm10. The PI3K/AKT pathway could regulate the translocation of Bax into the mitochondria, inducing Cytc release in response to apoptotic stimuli. Therefore, it is conceivable that the oncogenic components of the PI3K-AKT signaling pathway regulate cell apoptosis directly by influencing mitochondrial behavior. Thus, a live-cell mitochondrial permeability transition pore (mPTP) assay was performed to determine the mitochondrial status. mPTP opening is associated with apoptotic and necrotic cell death11. In this assay, a green fluorescent dye (calcein AM) is retained in the cytoplasm and mitochondria when the mPTP is closed, while the dye in the cytoplasm is quenched by CoCl2, leaving only the intensive fluorescence in the mitochondria. The results revealed that cell populations with intense green fluorescence in the mitochondria decreased significantly after FAM83A suppression, indicating an opened mPTP (Figure 4E,F). Further, we examined the changes in the mitochondrial membrane potential (&#916;&#936;m) using JC-1 staining. JC-1 forms aggregates (red fluorescence) in live cells containing intact mitochondria with high membrane potential and monomers (green fluorescence) under low MMP in apoptotic cells. FAM83A suppression gradually decreased MMP (Figure 4G).
Drug susceptibility analysis for proliferation and invasion
Cisplatin (DDP)-based chemotherapy is a standard treatment strategy for cervical cancer. However, chemoresistance remains a challenge. We investigated the role of FAM83A in the treatment of cervical cancer using cisplatin. Control, si-NC-treated, and si-FAM83A-treated HeLa cells were treated with or without 5 &#181;M cisplatin12. Further, the effects of FAM83A suppression on cell viability were assessed. DDP induced a more significant inhibitory effect on the proliferation of HeLa cells after FAM83A suppression, compared with only DDP treatment (Figure 5A and Figure 5C). After treating cervical cancer cells with 5 &#181;M cisplatin, FAM83A suppression also inhibited cell invasion (Figure 5B and Figure 5D).
Drug susceptibility analysis for mitochondrial function
By treating Si-NC-treated and si-FAM83A-treated HeLa cells with or without 5 &#181;M cisplatin (DDP), we evaluated the mitochondrial membrane potential (MMP) and assessed the mitochondrial damage caused by cell death using JC-1 staining. DDP induced more mitochondrial damage as revealed by a decrease in JC-1 monomer percentage after FAM83A knockdown, compared with only DDP treatment (Figure 6A and Figure 6D). After treating cervical cancer cells with 5 &#181;M cisplatin, FAM83A knockdown also inhibited colony formation (Figure 6B and Figure 6E) and enhanced cell apoptosis (Figure 6C and Figure 6F). In addition, FAM83A knockdown in the presence of DDP markedly increased the expression of proapoptotic proteins, Bax and cleaved caspase3, promoted Cytc release, and decreased Bcl-2 expression (Figure 6G,H). These results indicated that FAM83A knockdown sensitized cervical cancer cells to DDP via apoptosis induction.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65667/65667fig1.jpg" /> 
Figure 1:&#160;FAM83A overexpression in cervical cancer cells and correlation with worse prognosis. (A) Boxplot analysis of the mRNA levels of FAM83A in cervical cancer samples. (B) Kaplan-Meier analysis of FAM83A expression for the determination of overall survival in patients with cervical cancer. (C) Relative mRNA levels of FAM83A in cervical cancer cell lines HeLa and C33a and human immortalized cervical cell line End1/E6E7. The data are presented as the mean &#177; SD for three independent experiments. *P &#60; 0.05, compared with End1/E6E7 cells using one-way ANOVA. Abbreviations: CESE = Cervical squamous cell carcinoma and endocervical adenocarcinoma; HR = hazard ratio. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65667/65667fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65667/65667fig2.jpg" /> 
Figure 2: Effects of suppression of FAM83A on the proliferation and apoptosis of cervical cancer. C33a and HeLa cells were transfected with FAM83A-specific siRNAs. (A,B) mRNA levels of FAM83A were assessed in cervical cancer cell lines using qRT-PCR.Cell proliferation in (C) C33a and (D) HeLa cells was determined using the EdU assay. (E,F) The EDU fluorescence images of C33a and Hela cells. (G-J) Western blot analysis for examining the levels of apoptosis-related proteins, including Cytc, Bcl-2, Bax, caspase3, and cleaved-caspase-3; GAPDH was used as the loading control. The data are presented as the mean &#177; SD for three independent experiments. *P &#60; 0.05; **P &#60; 0.01; **P &#60; 0.001, compared with control cells using one-way ANOVA. Abbreviations: NC = negative control; EdU = 5-ethynyl-2&#39;-deoxyuridine; CytC = cytochrome C. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65667/65667fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65667/65667fig3.jpg" /> 
Figure 3: Effect of FAM83A on the migration and invasion of cervical cancer cells in vitro. (A-D) Wound-healing assay was performed to assess cell migration. Images were taken at 0, 24, and 48 h. (E-H) Transwell assays were performed to examine cell invasion. Scale bars = 100 &#181;m (E,F). The data are presented as the mean &#177; SD for three independent experiments. *P &#60; 0.05; **P &#60; 0.01; **P &#60; 0.001, compared with control cells using one-way ANOVA. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65667/65667fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65667/65667fig4.jpg" /> 
Figure 4: Effect of FAM83A suppression on the PI3K/AKT/mTOR pathway and mitochondrial function. (A-D) Western blot analysis to determine the levels of proteins from PI3K/AKT/mTOR pathway in C33a and HeLa cells after FAM83A suppression. (E-H) Mitochondrial membrane potential analysis using JC-1 staining. JC-1 aggregates were stained in red, and JC-1 monomers were stained in green indicating a lower MMP. (I,J) Mitochondrial permeability was assessed using an mPTP kit and flow cytometry. Scale bars = 50 &#181;m (E, F). The data are presented as the mean &#177; SD for three independent experiments. *P &#60; 0.05; **P &#60; 0.01; ***P &#60; 0.001, compared with control cells using one-way ANOVA. Abbreviations: MMP = mitochondrial membrane potential; mPTP = mitochondrial permeability transition pore. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65667/65667fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65667/65667fig5.jpg" /> 
Figure 5: Effects of FAM83A suppression on inhibition by DDP on cell proliferation and invasion of cervical cancer cells. HeLa cells were transfected with si-NC or si-FAM83A and treated with or without 5 &#181;M DDP. (A,C) Effect of DDP on the cell proliferation in control, si-NC-treated, and si-FAM83-treated HeLa cells. (B,D) Effect of DDP on the cell invasion in control, si-NC-treated, and si-FAM83-treated HeLa cells. The data are presented as the mean &#177; SD for three independent experiments. *P &#60; 0.05; **P &#60; 0.01; ***P &#60; 0.001, compared with the control cells. &#38;&#38;P &#60; 0.01; &#38;&#38;&#38;P &#60; 0.001, compared with DDP using one-way ANOVA. Abbreviations: DDP = diaminedichloroplatinum; EdU = 5-ethynyl-2&#39;-deoxyuridine; DAPI = 4&#39;,6-diamidino-2-phenylindole. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65667/65667fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65667/65667fig6.jpg" /> 
Figure 6:&#160;Effects of&#160;FAM83A suppression on the effects of DDP on apoptosis in cervical cancer cells. HeLa cells were transfected with si-NC or si-FAM83A and treated with or without 5 &#181;M DDP. (A,D) MMP analysis using JC-1 staining. (B,E) Plate cloning results. (C,F) Flow cytometry detecting apoptosis rates in different treatment groups. (G,H) Western blot analysis of mitochondria-related apoptotic proteins, including Bcl-2, Bax, Cytc, and the downstream cleaved caspase-3. GAPDH was used as the loading control. The data are presented as the mean &#177; SD for three independent experiments. *P &#60; 0.05; **P &#60; 0.01; ***P &#60; 0.001, compared with the control cells. &#38;P &#60; 0.05; &#38;&#38;P &#60; 0.01; &#38;&#38;&#38;P &#60; 0.001, compared with DDP using one-way ANOVA. Abbreviations: DDP = diaminedichloroplatinum; EdU = 5-ethynyl-2&#39;-deoxyuridine; DAPI = 4&#39;,6-diamidino-2-phenylindole. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65667/65667fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: siRNA, qRTPCR primers, and the qRT-PCR reaction setup used in this study. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65667/65667_Table 1_rev.xlsx" target="_blank">Please click here to download this table.</a>

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

Nanyang Technological University

Research

JoVE Journal

Cancer Research

839 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 

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma

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We describe the evolution of a spheroid-based, three-dimensional in vitro model that enables us to test the current standard of experimental therapy regimens for head and neck squamous cell carcinoma on cell lines, aiming at evaluating therapy susceptibility and resistance on primary cells from human specimens in the future.

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The aim of this methodology is to identify cancer stem cells (CSC) in cancer cell lines and primary human tumor samples with the sphere-forming protocol, in a robust manner, using functional assays and phenotypic characterization with flow cytometry and Western blot.

Cancer stem cells (CSC) are a small population with self-renewal and plasticity which are responsible for tumorigenesis, resistance to treatment and ...

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

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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 ...

Using the E1A Minigene Tool to Study mRNA Splicing Changes

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Enrichment for Chemoresistant Ovarian Cancer Stem Cells from Human Cell Lines

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Cancer stem cells (CSCs) are&#160;implicated in tumor relapse due to chemoresistance. We have optimized a protocol for selection and expansion of CSCs from ovarian cancer cell lines. By treating cells with the chemotherapeutic cisplatin and culturing&#160;in a stem cell promoting media we enrich for non-adherent CSC cultures.

Cancer stem cells (CSCs) are defined as a subset of slow cycling and undifferentiated cells that divide asymmetrically to generate highly proliferative, ...

Medicine

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Due to the high level of heterogeneity and mutations inherent in human cancers, single agent therapies, or combination regimens which target the same ...

TMOD3 Expression and Its Role in Ovarian Cancer Progression

Tropomodulin 3 Overexpression as a Marker for Platinum Resistance and Immune Infiltration in Ovarian Cancer

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, but its role in the drug resistance of ovarian cancer remains unexplored. By analyzing data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, this study compared TMOD3 expression in ovarian cancer and normal tissues, and examined the expression of TMOD3 after platinum treatment in platinum-sensitive and platinum-resistant ovarian cancers. The Kaplan-Meier method was used to assess the effect of TMOD3 on overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients. microRNAs (miRNAs) targeting TMOD3 were predicted using TargetScan and analyzed using the TCGA database. Tumor Immune Estimation Resource (TIMER) and an integrated repository portal for tumor-immune system interactions (TISIDB) were used to determine the relationship between TMOD3 expression and immune infiltration. TMOD3 coexpression networks in ovarian cancer were explored using LinkedOmics, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and The Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics. The results showed that TMOD3 was highly expressed in ovarian cancer and was associated with the grading, staging, and metastasis of ovarian cancer. TMOD3 expression was significantly reduced in platinum-treated ovarian cancer cells and patients. However, TMOD3 expression was higher in platinum-resistant ovarian cancer cells and tissues compared to platinum-sensitive ones. Higher TMOD3 expression was significantly associated with lower OS and PFS in ovarian cancer patients treated with platinum-based chemotherapy. miRNA-mediated post-transcriptional regulation is likely responsible for high TMOD3 expression in ovarian cancer and platinum-resistant ovarian tissues. The expression of TMOD3 mRNA was associated with immune infiltration in ovarian cancer. These findings indicate that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration.

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Tropomodulin 3 (TMOD3) has been increasingly studied in tumors in recent years. This study is the first to report that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration. These results could help improve the therapeutic outcomes for ovarian cancer.

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, ...

Through the Looking Glass: Time-lapse Microscopy and Longitudinal Tracking of Single Cells to Study Anti-cancer Therapeutics

The response of single cells to anti-cancer drugs contributes significantly in determining the population response, and therefore is a major contributing factor in the overall outcome. Immunoblotting, flow cytometry and fixed cell experiments are often used to study how cells respond to anti-cancer drugs. These methods are important, but they have several shortcomings. Variability in drug responses between cancer and normal cells, and between cells of different cancer origin, and transient and rare responses are difficult to understand using population averaging assays and without being able to directly track and analyze them longitudinally. The microscope is particularly well suited to image live cells. Advancements in technology enable us to routinely image cells at a resolution that enables not only cell tracking, but also the observation of a variety of cellular responses. We describe an approach in detail that allows for the continuous time-lapse imaging of cells during the drug response for essentially as long as desired, typically up to 96 hr. Using variations of the approach, cells can be monitored for weeks. With the employment of genetically encoded fluorescent biosensors numerous processes, pathways and responses can be followed. We show examples that include tracking and quantification of cell growth and cell cycle progression, chromosome dynamics, DNA damage, and cell death. We also discuss variations of the technique and its flexibility, and highlight some common pitfalls.

Here, we describe a method of long-term time-lapse microscopy to longitudinally track single cells in response to anti-cancer therapeutics.

The response of single cells to anti-cancer drugs contributes significantly in determining the population response, and therefore is a major contributing ...

A High-Throughput Comet Assay Approach for Assessing Cellular DNA Damage

Cells are continually exposed to agents arising from the internal and external environments, which may damage DNA. This damage can cause aberrant cell function, and therefore DNA damage may play a critical role in the development of, conceivably, all major human diseases, e.g., cancer, neurodegenerative and cardiovascular disease, and aging. Single-cell gel electrophoresis (i.e., the comet assay) is one of the most common and sensitive methods to study the formation and repair of a wide range of types of DNA damage (e.g., single- and double-strand breaks, alkali-labile sites, DNA-DNA crosslinks, and, in combination with certain repair enzymes, oxidized purines, and pyrimidines), in both in vitro and in vivo systems. However, the low sample throughput of the conventional assay and laborious sample workup are limiting factors to its widest possible application. With the "scoring" of comets increasingly automated, the limitation is now the ability to process significant numbers of comet slides. Here, a high-throughput (HTP) variant of the comet assay (HTP comet assay) has been developed, which significantly increases the number of samples analyzed, decreases assay run time, the number of individual slide manipulations, reagent requirements, and risk of physical damage to the gels. Furthermore, the footprint of the electrophoresis tank is significantly decreased due to the vertical orientation of the slides and integral cooling. Also reported here is a novel approach to chilling comet assay slides, which conveniently and efficiently facilitates the solidification of the comet gels. Here, the application of these devices to representative comet assay methods has been described. These simple innovations greatly support the use of the comet assay and its application to areas of study such as exposure biology, ecotoxicology, biomonitoring, toxicity screening/testing, together with understanding pathogenesis.

The comet assay is a popular means of detecting DNA damage. This study describes an approach to running slides in representative variants of the comet assay. This approach significantly increased the number of samples while decreasing assay run-time, the number of slide manipulations, and the risk of damage to gels.

Cells are continually exposed to agents arising from the internal and external environments, which may damage DNA. This damage can cause aberrant cell ...

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

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Tropomodulin 3 Overexpression as a Marker for Platinum Resistance and Immune Infiltration in Ovarian Cancer

Through the Looking Glass: Time-lapse Microscopy and Longitudinal Tracking of Single Cells to Study Anti-cancer Therapeutics

A High-Throughput Comet Assay Approach for Assessing Cellular DNA Damage