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JoVE Journal Cancer Research

Cancer Research

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published: July 22, 2020 doi: 10.3791/61077
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1, 4, 1, 1,2,3
1Radiation Biology Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital at Linkou, 2Department of Medical Imaging and Radiological Sciences, Chang Gung University, 3Department of Radiation Oncology, Chang Gung Memorial Hospital, 4Department of Laboratory Medicine, Mackay Memorial Hospital

Summary

Presented here is a protocol to discover the overexpressed driver genes maintaining the established cancer stem-like cells derived from colorectal HT29 cells. RNAseq with available bioinformatics was performed to investigate and screen gene expression networks for elucidating a potential mechanism involved in the survival of targeted tumor cells.

Abstract

Cancer stem cells play a vital role against clinical therapies, contributing to tumor relapse. There are many oncogenes involved in tumorigenesis and the initiation of cancer stemness properties. Since gene expression in the formation of colorectal cancer-derived tumorspheres is unclear, it takes time to discover the mechanisms working on one gene at a time. This study demonstrates a method to quickly discover the driver genes involved in the survival of the colorectal cancer stem-like cells in vitro. Colorectal HT29 cancer cells that express the LGR5 when cultured as spheroids and accompany an increase CD133 stemness markers were selected and used in this study. The protocol presented is used to perform RNAseq with available bioinformatics to quickly uncover the overexpressed driver genes in the formation of colorectal HT29-derived stem-like tumorspheres. The methodology can quickly screen and discover potential driver genes in other disease models.

Introduction

Colorectal cancer (CRC) is a leading cause of death with high prevalence and mortality worldwide1,2. Due to gene mutations and amplifications, cancer cells grow without proliferative control, which contributes to cell survival3, anti-apoptosis4, and cancer stemness5,6,7. Within a tumor tissue, tumor heterogeneity allows tumor cells to adapt and survive during therapeutic treatments8. Cancer stem cells (CSCs), with a higher rate of self-renewal and pluripotency than differential cancer types, are principally responsible for tumor recurrence9,10 and metastatic CRC11. CSCs present more drug resistance12,13,14 and anti-apoptosis properties15,16, thus surviving tumor chemotherapies.

Here, in order to investigate the potential mechanism for stemness in the selected CRC stem cells, RNAseq was performed to screen differentially expressed genes in tumor spheroids. The cancer cells can form spheroids (also called tumorspheres) when grown in low adherence conditions and stimulated by growth factors added to the cultured medium, including EGF, bFGF, HGF, and IL6. Therefore, we selected CRC HT29 tumor cells that resist chemotherapies with an increase in phosphorylated STAT3 when treated with oxaliplatin and irinotecon17. In addition, HT29 expressed higher stemness markers when cultured in the described culture conditions. The HT29-derived CSC model expressed higher amounts of leucine-rich repeat-containing G-protein-coupled receptor 5 (LGR5)18, a specific marker of CRC stem cells19,20. Moreover, CD133, considered a general biomarker for cancer stem cells, is also highly expressed in the HT29 cell line21. This protocol's purpose is to discover groups of driver genes in the established cancer stem-like tumorspheres based on bioinformatics datasets as opposed to investigating individual oncogenes22. It investigates potential molecular mechanisms through RNAseq analysis followed by available bioinformatics analyses.

Next generation sequencing is a high-throughput, easily available, and reliable DNA sequencing method based on computational help, used to comprehensively screen driver genes for guiding tumor therapies23. The technology is also used for detecting gene expression from reverse transcription of an isolated RNA sample24. However, when screening with RNAseq, the most important genes to target with therapy may not have the highest expression differential between experimental and control samples. Therefore, some bioinformatics were developed for classifying and identifying genes based on current datasets such as KEGG25, GO26,27, or PANTHER28, including Ingenuity Pathway Analysis (IPA)29 and NetworkAnalyst30. This protocol shows the integration of RNAseq and NetworkAnalyst to quickly discover a group of genes in the selected HT29-derived spheroids compared to parental HT29 cells. Application of this method to other disease models is also suggested for discovering differences in important genes.

Compared to investigation of individual gene expression, a high-throughput technique provides advantages to find potential driver genes easily for tumor precision medicine. With useful datasets such as KEGG, GO, or PANTHER, specific genes can be identified based on the disease models, signaling pathways, or specific functions, and this allows quickly focusing on specific, important genes, saving time and research costs. A similar application is used in previous studies14,18,31. Particularly, a tumor is more complicated because different types of tumors express distinguishing genes and pathways for survival and proliferation. Therefore, this protocol can pick up genes distinguishing different tumor types under different circumstances. There is the potential to find effective strategies against cancers by understanding the mechanism of specific gene expression.

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Protocol

1. Cell culture and tumorsphere formation

  1. Culture HT29 cells in a 10 cm dish containing Dulbecco’s modified eagle medium (DMEM) with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin antibiotic (P/S).
  2. Grow the cells in an incubator at 37 °C with 5% CO2 and 95% humidity under aseptic conditions, until they reach 80% confluency.
  3. Trypsinize HT29 cells with 1 mL of 0.25% trypsin for 5 min at 37 °C and consequently neutralize the trypsin by adding 2 mL of DMEM with 10% FBS and 1% P/S.
  4. Count the HT29 cells using a hemocytometer.
  5. Add 2,000 cells/well to a low attached 6 well plates with 2 mL of serum-free DMEM with 1% P/S and supplemented with 0.2% B27, 20 ng/mL of epidermal growth factor (EGF), 20 ng/mL of fibroblast growth factor (bFGF), 20 ng/mL of hepatocyte growth factor (HGF), and 20 ng/mL of interleukin 6 (IL6).
  6. Grow the cells at 37 °C with 5% CO2 and 95% humidity under aseptic conditions.
  7. Add 0.5 mL of cancer stem cell medium every 2 days until the tumorspheres measure >100 μm in diameter for at least 7 days.
  8. Observe and measure the tumorsphere diameter using an inverted microscope with a digital cell imaging system.
  9. When the tumorspheres reach >100 μm in diameter, trypsinize the tumorspheres with 0.25% trypsin for 5 min at 37 °C and neutralize the trypsin by adding 2x the volume of growth medium. Count the cells using a hemocytometer.
  10. Centrifuge at 1,200 rpm for 10 min and remove the supernatant.
  11. Incubate 5 x 104 cells with 2 µL of anti-LGR5-PE and 2 µL of anti-CD133-PE in 100 µL of DMEM individually for 30 min at room temperature, shaking at 200 rpm.
    NOTE: CD133 is a general cancer stem cell biomarker that is highly expressed in HT29 cells.
  12. Add 900 µL of PBS and analyze the LGR5 and CD133 expression using flow cytometry. A change in fluorescence in the FL2-H channel indicates gene expression.

2. RNA isolation

NOTE: Use a commercial kit (see Table of Materials) with a rapid column for RNA isolation following the manufacturer’s instructions.

  1. Add 50 µL of PBS to the harvested cells (2 x 105 cells) and resuspend them with pipetting.
  2. Add 200 µL of lysis buffer containing 2 µL of beta-mercaptoethanol (β-ME). Vortex quickly and let stand for 5 min.
  3. Centrifuge the solution at 16,000 x g for 10 min. Collect the supernatant and mix with 200 µL of 70% ethanol.
  4. Use the attached column to remove the solvent by centrifugation at 14,000 x g for 1 min.
  5. Wash using wash solution 1 and 2 to completely remove non-RNAs by centrifugation at 14,000 x g for 1 min.
  6. Centrifuge again at 14,000 x g for 2 min to remove residual ethanol.
  7. Add 50 µL of distilled water, centrifuge at 14,000 x g for 1 min, and collect the solution.
  8. Measure the RNA concentration using OD260 with a spectrophotometer.
    RNA concentration (µg/mL) = (OD260) x (40 µg RNA/mL) and OD260/OD280 > 2. RNA samples should have an RNA integrity number (RIN) > 7.

3. RNAseq profiling and bioinformatics analysis

NOTE: RNAseq analysis was performed commercially (see Table of Materials) to investigate the differential genes in the HT29-derived tumorspheres compared to parental HT29 cells.

  1. Use commercial services for RNAseq steps, including library construction, library quality control, and DNA sequencing.
  2. The data report should contain important information, including the read counts, log2 fold change, and p value. Select the differential genes according to the following parameters: genes with a > 1 log2 fold change with read counts >100 in the HT29 tumorsphere group, and genes <-1 log2 fold change with read count >100 in the HT29 parental group. In this case, a p value < 0.05 was considered acceptable and the data were used (Table 1).
    NOTE: Here, a gene count >100 was used as the threshold to continue the study of a particular gene and validate its expression.
  3. Use statistical analysis software (see Table of Materials), to show a heatmap and identify overexpressed genes >1 and downregulated genes < 1 in log2 fold change.
  4. Use R software to draw the Volcano Plot with x: log2 fold change; y: -log10 (p value) to show the differential genes.
    1. Install R library
      install.packages(library(calibrate)) 
    2. Read the data in RStudio with the following program:
      res <-read.csv("/Users/xxx.csv", header=T)
      head(res)
      with(res, plot(log2FoldChange, -log10(pvalue), pch=19, main="HT29CSC vs HT29", xlim=c(-6,6), col="#C0C0C0"))
      with(subset(res, pvalue<.05 & log2FoldChange>1), points(log2FoldChange, -log10(pvalue), pch=19, col="red"))
      with(subset(res, pvalue<.05 & log2FoldChange<(-1)), points(log2FoldChange, -log10(pvalue), pch=19, col="blue"))
      with(subset(res, pvalue>.05), points(log2FoldChange, -log10(pvalue), pch=19, col="#444444"))
      abline(h=1.3, lty=2)
      abline(v=1, lty=2)
      abline(v=(-1), lty=2)
    3. Execute the run to obtain the Volcano Plot.

4. Driver gene selection

  1. Select Single gene Input in NetworkAnalyst.
  2. Copy and paste the selected overexpressed genes from Table 1 with “human” specified as the organism and ID type Official Gene Symbol.
    NOTE: Alternatively, use Ensembl Gene ID for copy and paste.
  3. Insert the data by clicking Upload and Proceed to analyze it using protein-protein interaction (PPI) following genetic PPI.
  4. Use the STRING interactome database with a confidence score cutoff of 900 to show the seed genes cross-linking the uploaded genes. The seed genes associating with more individual genes were selected as driver genes that may be involved in maintaining formation of HT29-derived tumorspheres.
    NOTE: There are three interactome datasets for use: IMEx, STRING, and Rolland. STRING contains higher confidence experimental evidence. With lower uploaded gene numbers, IMEx can be selected to predict and pick up the driver genes in the interactome networks.
  5. Select Proceed in the mapping overview.
  6. Select White in the Background and Force Atlas in the Layout knob.
  7. Select PANTHER BP to analyze the upregulation gene group.
    NOTE: This shows that HSPA5 was responsible for anti-apoptosis in the HT29-derived tumorspheres in this study (Figure 3A). To narrow down the specific functional field, KEGG, GO, or PANTHER classification can be used alternatively to select the specific driver genes.

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Representative Results

To establish the model for investigating the mechanism in cancer stem cells, colorectal HT29 cells were used to culture the cancer stem-like tumorspheres in vitro in a low-attachment plate containing B27, EGF, bFGF, HGF, and IL6. The tumorspheres >100 µm in diameter were formed in 7 days (Figure 1A). The tumorspheres were trypsinized to single cells and analyzed using flow cytometry to detect LGR5 and CD133 expression. LGR5 increased in the HT29-drived tumorspheres from 1.1% to 11.4% and the cells were detected using flow cytometry (Figure 1B). Another stemness marker, CD133, also increased from 61.8% to 81.1% in the cultured HT29-derived tumorspheres compared to parental HT29 cells (Figure 1C). Then, the tumorspheres were ready for RNAseq investigation.

RNAseq was used to investigate the gene expression profile in the HT29-derived tumorspheres compared to the parental HT29 cells. The error rate in nucleotide reading was 0.03% for both samples. The total gene mapping rate was 87.87% for HT29 and 87.25% for HT29-derived tumorspheres. The fragments per kilobase of transcript per million (FPKM), normalizing the detective counts for indicating the transcript (mRNA) expression, interval between 0 and 1 was 75.87% for HT29 cells, and 77.16% for HT29-derived tumorspheres. The results were suitable for consequent sequencing. After sequencing reads, the genes with log2 fold change >1 in upregulation and <-1 in downregulation with p value < 0.05 shown by the heatmap (Figure 2A,Table 1,Table 2) were selected. There were 79 upregulated genes and 33 downregulated genes selected. In addition, the Volcano plot using log2 fold change and p value (-log10 p value) was used to distinguish the significant genes between HT29-derived tumorspheres and parental HT29 cells (Figure 2B). Based on the preliminary selection, three potentially upregulated genes were identified, including ACSS2, HMGCS1, and PCSK9, in the HT29-derived tumorspheres (Figure 2A). Furthermore, in order to identify the driver genes not solely according to the log2 fold change, NetworkAnalyst was used. The upregulated genes with log2 fold change >1 with p value <0.05 were analyzed and resulted in 10 seed genes cross-linking the gene networks, including HSPA5, HSP90AA1, BRCA1, SFN, E2F1, CYCS, CDC6, ALYREF, SQSTM1, and TOMM40 (Figure 3A). To identify the function and significance of the seed genes, the classification interface could be used to perform PANTHER BP to determine the genes involved in anti-apoptosis in HT29-derived tumorspheres. The results indicated that HSPA5 and SQSTM1 were associated with negative regulation of apoptosis (Figure 3A). Moreover, the selected 10 genes were consequently validated; expression increased in the HT29-derived tumorspheres as confirmed using qPCR (Figure 3B).

Figure 1
Figure 1: Establishment of cancer stem-like tumorsphere in vitro. (A) HT29 was used to form tumorspheres, and (B) LGR5 was detected in the HT29-derived tumorspheres (Scale bar = 100 µm). (C) CD133 was used as another marker to identify the stemness characters in the established tumorspheres. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Heatmap and Volcano plot were used to select the differential genes in the HT29-derived tumorspheres. Log2 fold change >1 and <-1 with read count >100, p value < 0.05 were used to exclude nonsignificant genes and make sure the data were accurate. Please click here to view a larger version of this figure.

Figure 3
Figure 3: NetworkAnalyst was used to identify the driver genes in the HT29-derived tumorspheres. (A) The upregulated genes were selected and analyzed consequently, and a classification interface, PANTHER BP, provided the potential functions for the differential driver genes. (B) Then, qPCR was used to validate the 10 genes upregulated in HT29-derived tumorspheres compared to parental HT29 cells. Please click here to view a larger version of this figure.

Table 1: The upregulated genes in HT29-serived tumorspheres compared to parental HT29 cells analyzed by RNAseq. Please click here to download this table.

Table 2: The downregulated genes in HT29-serived tumorspheres compared to parental HT29 cells analyzed by RNAseq. Please click here to download this table.

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Discussion

In this study, cultured cancer stem-like tumorspheres were used as a model in analyzing RNAseq data with available bioinformatics. For a disease model, HT29-derived tumorspheres were used. Because the tumorspheres have drug resistance against tumor therapies, the established model can be used to investigate the detailed mechanisms of resistance by investigating differences in gene expression. Moreover, genomic technology using RNAseq with available bioinformatics provides rapid understanding of the study model so the genes potentially involved can be validated with higher confidence. Also, the kind of genes involved in the formation of tumorspheres can be identified.

RNA quality is critical for the RNAseq analysis32. Ensure that the sample has RIN >7, because it increases certainty between mapping reads, mapping genes, and FPKM. When analyzing RNAseq data, IPA29 and NetworkAnalyst30 were available to identify potential genes and signaling pathways. However, it is essential to rule out the unnecessary genes according to the following parameters: genes showing a >1 log2 fold change with read counts >100 in the experimental group, and genes <-1 log2 fold change with read count > 100 in the control group. Higher read counts are easier for consequent validation using qPCR or Western blots.

Based on the understanding of biological functions and processes, there are many bioinformatics tools allowing rapid investigation of the potential mechanisms of diseases of interest. Combined with a high-throughput tool to screen for gene expression such as RNAseq, potential mechanisms regulating the development of the studied diseases can be proposed and investigated. Here, the method for investigating the formation of CRC stem-like tumorspheres derived from HT29 revealed that SQSTM1 and HSPA5 were the target genes upregulated and involved in anti-apoptosis in tumorspheres. Therefore, more experiments can be designed to investigate the detailed mechanism of these genes, which results in more confidence and efficacy when conducting research studies.

Here, only the upregulated genes in the tumorspheres were analyzed because upregulation was considered to be induced by the addition of the growth factors. Otherwise, if the experiment used gene knockdown in the tumor cells, the downregulated genes would be considered as targets that can be selected for investigation using the bioinformatics. Although the methodology is rapid and dependable, subsequent validation is still needed via qPCR and Western blots. It is also suggested that more cell lines be used for gene expression validation, especially for clinical samples.

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Disclosures

The authors have no relevant financial disclosures.

Acknowledgments

The authors thank the Radiation Biology Core Laboratory of Institute for Radiological Research, Chang Gung Memorial Hospital, for technical support. This study was supported by grants from Chang Gung Memorial hospital (CMRPD1J0321), Cheng Hsin General Hospital (CHGH 106-06), and Mackay Memorial Hospital (MMH-CT-10605 and MMH-106-61). Funding bodies did not have any influence in the design of the study and data collection, analysis and interpretation of data or in writing the manuscript.

Materials

Name Company Catalog Number Comments
iRiS Digital Cell Imaging System Logos Biosystems, Inc I10999 for observing the formation of tumorspheres
Flow cytometry BD biosciences FACSCalibur for detecting the LGR5 and CD133 in the tumorspheres
anti-LGR5-PE Biolegend 373803 LGR5 detection reagent
anti-CD133-PE Biolegend 372803 CD133 detection reagent
EGF GenScript Z00333 for culture of tumorspheres
bFGF GenScript Z03116 for culture of tumorspheres
HGF GenScript Z03229 for culture of tumorspheres
IL6 GenScript Z03034 for culture of tumorspheres
PureLink RNA extraction kit Invitrogen 12183025 isolate total RNA for RNAseq analysis
RNAseq performance Biotools, Taiwan RNAseq analysis is done commerially by Biotools, Ttaiwan
NetworkAnalyst Institute of Parasitology, McGill University, Montreal, Quebec, Canada http://www.networkanalyst.ca/
Prism GraphPad Software a statistical analysis software

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References

  1. Rawla, P., Sunkara, T., Barsouk, A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Przegląd Gastroenterologiczny. 14 (2), 89-103 (2019).
  2. Wong, M. C., Ding, H., Wang, J., Chan, P. S., Huang, J. Prevalence and risk factors of colorectal cancer in Asia. Intestinal Research. 17 (3), 317-329 (2019).
  3. Liu, Q., et al. Positive expression of basic transcription factor 3 predicts poor survival of colorectal cancer patients: possible mechanisms involved. Cell Death & Disease. 10 (7), 509 (2019).
  4. Slattery, M. L., et al. Dysregulated genes and miRNAs in the apoptosis pathway in colorectal cancer patients. Apoptosis. 23 (3-4), 237-250 (2018).
  5. Arteaga, C. L., Engelman, J. A. ERBB receptors: from oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell. 25 (3), 282-303 (2014).
  6. Yarden, Y., Pines, G. The ERBB network: at last, cancer therapy meets systems biology. Nature Reviews Cancer. 12 (8), 553-563 (2012).
  7. Cheng, C. C., et al. YM155 as an inhibitor of cancer stemness simultaneously inhibits autophosphorylation of epidermal growth factor receptor and G9a-mediated stemness in lung cancer cells. PLoS One. 12 (8), 0182149 (2017).
  8. Prasetyanti, P. R., Medema, J. P. Intra-tumor heterogeneity from a cancer stem cell perspective. Molecular Cancer. 16 (1), 41 (2017).
  9. Zhao, Y., et al. CD133 expression may be useful as a prognostic indicator in colorectal cancer, a tool for optimizing therapy and supportive evidence for the cancer stem cell hypothesis: a meta-analysis. Oncotarget. 7 (9), 10023-10036 (2016).
  10. Choi, J. E., et al. Expression of epithelial-mesenchymal transition and cancer stem cell markers in colorectal adenocarcinoma: Clinicopathological significance. Oncology Reports. 38 (3), 1695-1705 (2017).
  11. Massard, C., Deutsch, E., Soria, J. C. Tumour stem cell-targeted treatment: elimination or differentiation. Annals of Oncology. 17 (11), 1620-1624 (2006).
  12. Grillet, F., et al. Circulating tumour cells from patients with colorectal cancer have cancer stem cell hallmarks in ex vivo culture. Gut. 66 (10), 1802-1810 (2017).
  13. Dallas, N. A., et al. Chemoresistant colorectal cancer cells, the cancer stem cell phenotype, and increased sensitivity to insulin-like growth factor-I receptor inhibition. Cancer Research. 69 (5), 1951-1957 (2009).
  14. Chang, Y. F., et al. STAT3 induces G9a to exacerbate HER3 expression for the survival of epidermal growth factor receptor-tyrosine kinase inhibitors in lung cancers. BMC Cancer. 19 (1), 959 (2019).
  15. Catalano, V., et al. Colorectal cancer stem cells and cell death. Cancers (Basel). 3 (2), 1929-1946 (2011).
  16. Piggott, L., et al. Suppression of apoptosis inhibitor c-FLIP selectively eliminates breast cancer stem cell activity in response to the anti-cancer agent, TRAIL. Breast Cancer Research. 13 (5), 88 (2011).
  17. Chung, S. Y., et al. Two novel SHP-1 agonists, SC-43 and SC-78, are more potent than regorafenib in suppressing the in vitro stemness of human colorectal cancer cells. Cell Death Discovery. 4, 25 (2018).
  18. Cheng, C. C., et al. STAT3 exacerbates survival of cancer stem-like tumorspheres in EGFR-positive colorectal cancers: RNAseq analysis and therapeutic screening. Journal of Biomedical Science. 25 (1), 60 (2018).
  19. Kleist, B., Xu, L., Li, G., Kersten, C. Expression of the adult intestinal stem cell marker Lgr5 in the metastatic cascade of colorectal cancer. International Journal of Clinical and Experimental Pathology. 4 (4), 327-335 (2011).
  20. Medema, J. P. Targeting the Colorectal Cancer Stem Cell. New England Journal of Medicine. 377 (9), 888-890 (2017).
  21. Sahlberg, S. H., Spiegelberg, D., Glimelius, B., Stenerlow, B., Nestor, M. Evaluation of cancer stem cell markers CD133, CD44, CD24: association with AKT isoforms and radiation resistance in colon cancer cells. PLoS One. 9 (4), 94621 (2014).
  22. Xia, J., Gill, E. E., Hancock, R. E. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nature Protocols. 10 (6), 823-844 (2015).
  23. Gagan, J., Van Allen, E. M. Next-generation sequencing to guide cancer therapy. Genome Medicine. 7 (1), 80 (2015).
  24. Panichnantakul, P., Bourgey, M., Montpetit, A., Bourque, G., Riazalhosseini, Y. RNA-Seq as a Tool to Study the Tumor Microenvironment. Methods in Molecular Biology. 1458, 311-337 (2016).
  25. Kanehisa, M., Sato, Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Science. 29 (1), 28-35 (2020).
  26. Ashburner, M., et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics. 25 (1), (2000).
  27. The Gene Ontology Collective. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research. 47, 330-338 (2019).
  28. Mi, H., Muruganujan, A., Thomas, P. D. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Research. 41, Database issue 377-386 (2013).
  29. Yu, F., Shen, X. Y., Fan, L., Yu, Z. C. Genome-wide analysis of genetic variations assisted by Ingenuity Pathway Analysis to comprehensively investigate potential genetic targets associated with the progression of hepatocellular carcinoma. European Review for Medical and Pharmacological Sciences. 18 (15), 2102-2108 (2014).
  30. Zhou, G., et al. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Research. 47 (1), 234-241 (2019).
  31. Cheng, C. C., et al. Epidermal growth factor induces STAT1 expression to exacerbate the IFNr-mediated PD-L1 axis in epidermal growth factor receptor-positive cancers. Molecular Carcinogenesis. 57 (11), 1588-1598 (2018).
  32. Gallego Romero, I., Pai, A. A., Tung, J., Gilad, Y. RNA-seq: impact of RNA degradation on transcript quantification. BMC Biology. 12, 42 (2014).

Tags

Driver Genes Colorectal Cancer Tumorspheres RNA-Seq Bioinformatical Tools Overexpressed Genes Molecular Mechanism Cancer Stemness Inverted Microscope Digital Cell Imaging System Diameter Measurement Trypsin Treatment Cell Count Hemocytometer Centrifugation Antibody Incubation Flow Cytometry Analysis RNA Isolation
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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Cite this Article

Cheng, C. C., Hsu, P. J., Sie, Z.… More

Cheng, C. C., Hsu, P. J., Sie, Z. L., Chen, F. H. Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres. J. Vis. Exp. (161), e61077, doi:10.3791/61077 (2020).

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