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> Disorders > Neoplastic Process > Squamous Cell Carcinoma

Squamous Cell Carcinoma

Squamous Cell Carcinoma: A type of cancer that originates from squamous cells, which are flat cells that line many of the body's organs.
Squamous cell carcinomas can occur in various parts of the body, such as the skin, lungs, esophagus, and cervix.
These cancers are typically slow-growing and can often be treated effectively if caught early.
Understanding the characteristics and treatment options for squamous cell carcinoma is crucial for improving patient outcomes and advancing medical research in this field.

Most cited protocols related to «Squamous Cell Carcinoma»

1
Customizing Leukocyte Signature Matrices for Tumor Profiling
Cited 510 times
In the following two sections, we describe how to create a custom leukocyte signature matrix and apply it to study cellular heterogeneity and TIL survival associations in melanoma tumors profiled by The Cancer Genome Atlas (TCGA). Readers can follow along by creating ‘LM6’, a leukocyte RNA-Seq signature matrix comprised of six peripheral blood immune subsets (B cells, CD8 T cells, CD4 T cells, NK cells, monocytes/macrophages, neutrophils; GSE60424 [20 ]). Key input files are provided on the CIBERSORT website (‘Menu>Download’).
A custom signature file can be created by uploading the Reference sample file and the Phenotype classes file (section 3.3.2) to the online CIBERSORT application (SeeFigure 2) or can be created using the downloadable Java package. To build a custom gene signature matrix with the latter, the user should download the Java package from the CIBERSORT website and place all relevant files under the package folder. To link Java with R, run the following in R:
Within R:

> library(Rserve)

> Rserve(args=“–no-save”)

Command line:

> java -Xmx3g -Xms3g -jar CIBERSORT.jar -M Mixture_file -P Reference_sample_file -c phenotype_class_file -f

The last argument (-f) will eliminate non-hematopoietic genes from the signature matrix and is generally recommended for signature matrices tailored to leukocyte deconvolution. The user can also run this step on the website by choosing the corresponding reference sample file and phenotype class file (seeFigure 2). The CIBERSORT website will generate a gene signature matrix located under ‘Uploaded Files’ for future download.
Following signature matrix creation, quality control measures should be taken to ensure robust performance (see ‘Calibration of in silico TIL profiling methods’ in Newman et al.) [17 (link)]. Factors that can adversely affect signature matrix performance include poor input data quality, significant deviations in gene expression between cell types that reside in different tissue compartments (e.g., blood versus tissue), and cell populations with statistically indistinguishable expression patterns. Manual filtering of poorly performing genes in the signature matrix (e.g., genes expressed highly in the tumor of interest) may improve performance.
To benchmark our custom leukocyte matrix (LM6), we compared it to LM22 using a set of TCGA lung squamous cell carcinoma tumors profiled by RNA-Seq and microarray (n = 130 pairs). Deconvolution results were significantly correlated for all cell subsets shared between the two signature matrices (P < 0.0001). Notably, since LM6 was derived from leukocytes isolated from peripheral blood [20 ,21 (link)], we restricted the CD4 T cell comparison to naïve and resting memory CD4 T cells in LM22. Once validation is complete, a CIBERSORT signature matrix can be broadly applied to mixture samples as described in section 3.3 (e.g., SeeFigure 4).
Chen B., Khodadoust M.S., Liu C.L., Newman A.M, & Alizadeh A.A. (2018). Profiling tumor infiltrating immune cells with CIBERSORT. Methods in molecular biology (Clifton, N.J.), 1711, 243-259.
Publication 2018
B-Lymphocytes BLOOD CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes cDNA Library Cells Genes, vif Genetic Diversity Genetic Heterogeneity Hematopoietic System Leukocytes Lung Neoplasms Macrophage Malignant Neoplasms Melanoma Memory Microarray Analysis Monocytes Natural Killer Cells Neoplasms Neutrophil Phenotype Population Group RNA-Seq RNA Motifs Squamous Cell Carcinoma Strains Tissues
2
GEPIA Survival Analysis Protocol
Cited 394 times
GEPIA performs survival analysis based on gene expression levels (Figure 2D). This function allows users to select their custom cancer types for overall or disease-free survival analysis. For example, to examine the survival curves of an input gene in lung cancer, a user can select lung squamous cell carcinoma (LUSC) only or choose both LUSC and lung adenocarcinoma for the survival analyses. GEPIA uses log-rank test, sometimes called the Mantel–Cox test, for the hypothesis evaluation. The cox proportional hazard ratio and the 95% confidence interval information can also be included in the survival plot. The thresholds for high/low expression level cohorts can be adjusted.
For survival analysis, GEPIA also provides a gene normalization feature that allows the relative expression of two different genes as input. For example, when investigating gene FOXP3 in cancer survival analysis, users can also input another gene such as CD3G to normalize the expression of FOXP3. In such case, GEPIA will perform the survival analysis based on the FOXP3/CD3G relative expression levels. Furthermore, GEPIA can also present top genes that are most associated with cancer patient survival. The gene list is ranked by P-values of survival analysis based on any input cancer types.
Tang Z., Li C., Kang B., Gao G., Li C, & Zhang Z. (2017). GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45(Web Server issue), W98-W102.
Publication 2017
Adenocarcinoma of Lung Gene, Cancer Gene Expression Genes Lung Lung Cancer Malignant Neoplasms Patients Squamous Cell Carcinoma
3
Identifying Mut-Driver Genes Through Mutation Patterns
Cited 267 times
If mutation frequency, corrected for mutation context, gene length, and other parameters, cannot reliably identify modestly mutated driver genes, what can? In our experience, the best way to identify Mut-driver genes is through their pattern of mutation rather than through their mutation frequency. The patterns of mutations in well-studied oncogenes and tumor suppressor genes are highly characteristic and nonrandom. Oncogenes are recurrently mutated at the same amino acid positions, whereas tumor suppressor genes are mutated through protein-truncating alterations throughout their length (Fig. 4 and table S2A).
On the basis of these mutation patterns rather than frequencies, we can determine which of the 18,306 mutated genes containing a total of 404,863 subtle mutations that have been recorded in the Catalogue of Somatic Mutations in Cancer (COSMIC) database (30 (link)) are Mut-driver genes and whether they are likely to function as oncogenes or tumor suppressor genes. To be classified as an oncogene, we simply require that >20% of the recorded mutations in the gene are at recurrent positions and are missense (see legend to table S2A). To be classified as a tumor suppressor gene, we analogously require that >20% of the recorded mutations in the gene are inactivating. This “20/20 rule” is lenient in that all well-documented cancer genes far surpass these criteria (table S2A).
The following examples illustrate the value of the 20/20 rule. When IDH1 mutations were first identified in brain tumors, their role in tumorigenesis was unknown (2 (link), 31 (link)). Initial functional studies suggested that IDH1 was a tumor suppressor gene and that mutations inactivated this gene (32 (link)). However, nearly all of the mutations in IDH1 were at the identical amino acid, codon 132 (Fig. 4). As assessed by the 20/20 rule, this distribution unambiguously indicated that IDH1 was an oncogene rather than a tumor suppressor gene, and this conclusion was eventually supported by biochemical experiments (33 (link), 34 (link)). Another example is provided by mutations in NOTCH1. In this case, some functional studies suggested that NOTCH1 was an oncogene, whereas others suggested it was a tumor suppressor gene (35 (link), 36 (link)). The situation could be clarified through the application of the 20/20 rule to NOTCH1 mutations in cancers. In “liquid tumors” such as lymphomas and leukemias, the mutations were often recurrent and did not truncate the predicted protein (37 (link)). In squamous cell carcinomas, the mutations were not recurrent and were usually inactivating (38 (link)–40 (link)). Thus, the genetic data clearly indicated that NOTCH1 functions differently in different tumor types. The idea that the same gene can function in completely opposite ways in different cell types is important for understanding cell signaling pathways.
Vogelstein B., Papadopoulos N., Velculescu V.E., Zhou S., Diaz LA J.r, & Kinzler K.W. (2013). Cancer Genome Landscapes. Science (New York, N.Y.), 339(6127), 1546-1558.
Publication 2013
Amino Acids Ataxia Telangiectasia Mutated Proteins Brain Neoplasms Cells Codon Cosmic composite resin Diploid Cell Gene, Cancer Genes Leukemia Lymphoma Malignant Neoplasms Mutation Neoplasms Neoplastic Cell Transformation Oncogenes Proteins Reproduction Signal Transduction Pathways Squamous Cell Carcinoma Tumor Suppressor Genes
4
Lung Cancer Biomarkers Meta-Analysis
Cited 227 times
A Pubmed search was performed to identify lung cancer survival associated biomarkers using all combinations of the keywords “lung cancer”, “NSCLC”, “adenocarcinoma”, “squamous cell carcinoma”, “survival”, “gene expression”, “signature” and “meta analysis”. Only studies published in English were included. Eligibility criteria also included the investigation of the biomarker in at least 50 patients - biomarkers described in experimental models only were omitted. For each gene/signature the exact conditions in which it was identified have been retrieved, and these have been used as filtering when selecting the patients for the survival analysis.
To visualize the performance of the various biomarkers in datasets including different number of patients, we have generated funnel plots depicting the hazard ratio (and confidence intervals) on the horizontal axis vs. the sample size on the vertical axis for each dataset. We also added an option to the online interface to simultaneously perform the analysis in each of the individual datasets. Finally, significance was set at p<0.01.
Győrffy B., Surowiak P., Budczies J, & Lánczky A. (2013). Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer. PLoS ONE, 8(12), e82241.
Publication 2013
Adenocarcinoma Biological Markers Eligibility Determination Epistropheus Gene Expression Genes Lung Lung Cancer Non-Small Cell Lung Carcinoma Patients Squamous Cell Carcinoma Tumor Markers
5
Pan-Cancer Transcriptomic and Proteomic Analysis
Cited 94 times
Results are based in part upon data generated by TCGA Research Network (http://cancergenome.nih.gov/). We aggregated TCGA transcriptomic and RPPA data from public repositories, listed in the “Data availability” section. RNA-seq expression data were processed by TCGA at the gene level, rather than at the transcript level. Tumors spanned 32 different TCGA projects, each project representing a specific cancer type, listed as follows: LAML, acute myeloid leukemia; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; LGG, lower grade glioma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; CRC, colorectal adenocarcinoma (combining COAD and READ projects); ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THYM, thymoma; THCA, thyroid carcinoma; UCS, uterine carcinosarcoma; UCEC, uterine corpus endometrial carcinoma; UVM, uveal melanoma. Cancer molecular profiling data were generated through informed consent as part of previously published studies and analyzed per each original study’s data use guidelines and restrictions.
Chen F., Chandrashekar D.S., Varambally S, & Creighton C.J. (2019). Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers. Nature Communications, 10, 5679.
Publication 2019
4-carboxyphenylglyoxal Adenocarcinoma Adenocarcinoma of Lung Adrenocortical Carcinoma Breast Carcinoma Carcinoma, Thyroid Carcinoma, Transitional Cell Carcinosarcoma Cells Cholangiocarcinoma Chromophobia Chronic Obstructive Airway Disease Diffuse Large B-Cell Lymphoma Endocervix Endometrial Carcinoma Esophageal Cancer Familial Atypical Mole-Malignant Melanoma Syndrome Gene Expression Profiling Genes Glioblastoma Multiforme Glioma Hepatocellular Carcinomas Hypernephroid Carcinomas Kidney Leukemia, Myelocytic, Acute Lung Lymph Malignant Neoplasms Mesothelioma Neck Neoplasms Ovary Pancreas Paraganglioma Pheochromocytoma Prostate Renal Cell Carcinoma RNA-Seq Sarcoma Serous Cystadenocarcinoma Squamous Cell Carcinoma Squamous Cell Carcinoma of the Head and Neck Stomach Testicular Germ Cell Tumor Thymoma Urinary Bladder Uterus Uveal melanoma X-Ray Photoelectron Spectroscopy

Most recents protocols related to «Squamous Cell Carcinoma»

1
Cytotoxicity of Plant Stem Cell Extracts
Not available on PMC !

Example 7

The MTT Cell Proliferation assay determines cell survival following apple stem cell extract treatment. The purpose was to evaluate the potential anti-tumor activity of apple stem cell extracts as well as to evaluate the dose-dependent cell cytotoxicity.

Principle: Treated cells are exposed to 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT). MTT enters living cells and passes into the mitochondria where it is reduced by mitochondrial succinate dehydrogenase to an insoluble, colored (dark purple) formazan product. The cells are then solubilized with DMSO and the released, solubilized formazan is measured spectrophotometrically. The MTT assay measures cell viability based on the generation of reducing equivalents. Reduction of MTT only occurs in metabolically active cells, so the level of activity is a measure of the viability of the cells. The percentage cell viability is calculated against untreated cells.

Method: A549 and NCI-H520 lung cancer cell lines and L132 lung epithelial cell line were used to determine the plant stem cell treatment tumor-specific cytotoxicity. The cell lines were maintained in Minimal Essential Media supplemented with 10% FBS, penicillin (100 U/ml) and streptomycin (100 μg/ml) in a 5% CO2 at 37 Celsius. Cells were seeded at 5×103 cells/well in 96-well plates and incubated for 48 hours. Triplicates of eight concentrations of the apple stem cell extract were added to the media and cells were incubated for 24 hours. This was followed by removal of media and subsequent washing with the phosphate saline solution. Cell proliferation was measured using the MTT Cell Proliferation Kit I (Boehringer Mannheim, Indianapolis, IN) New medium containing 50 μl of MTT solution (5 mg/ml) was added to each well and cultures were incubated a further 4 hours. Following this incubation, DMSO was added and the cell viability was determined by the absorbance at 570 nm by a microplate reader.

In order to determine the effectiveness of apple stem cell extracts as an anti-tumor biological agent, an MTT assay was carried out and IC50 values were calculated. IC50 is the half maximal inhibitory function concentration of a drug or compound required to inhibit a biological process. The measured process is cell death.

Results: ASC-Treated Human Lung Adenocarcinoma Cell Line A549.

TABLE 7
Results of cytotoxicity of apple stem cell extract on lung cancer cell
line A549 as measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concentration*replicatereplicatereplicateMean of% Live
(μg/ml)123replicatesSDSEMCells
25093.1890.8690.3491.461.510.878.54
10086.8885.1885.6985.920.870.5014.08
5080.5879.4981.0480.370.800.4619.63
2574.2873.8176.3974.831.380.7925.17
12.567.9868.1371.7569.282.131.2330.72
6.2561.6762.4567.1063.742.931.6936.26
3.12555.3756.7762.4558.203.752.1641.80
1.56249.0751.0857.8052.654.572.6447.35
0.78142.7745.4053.1547.115.403.1252.89

Results: ASC-Treated Human Squamous Carcinoma Cell Line NCI-H520.

TABLE 8
Results of cytotoxicity of apple stem cell extract on lung cancer
cell line NCI-H520 measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concen-%
tration*replicatereplicatereplicateMean ofLive
(μg/ml)123replicatesSDSEMcell
25088.2889.2987.7388.430.790.4611.57
10078.1379.1978.1378.480.610.3521.52
5067.9869.0968.5468.540.560.3231.46
2557.8358.9958.9458.590.660.3841.41
12.547.6848.8949.3448.640.860.5051.36
6.2537.5338.7939.7538.691.110.6461.31
3.12527.3728.6930.1528.741.390.8071.26
1.56217.2218.5920.5618.791.680.9781.21
0.781 7.07 8.4810.96 8.841.971.1491.16

Results: ASC-treated Lung Epithelial Cell Line L132.

TABLE 9
Results of cytotoxicity of apple stem cell extract on
lung epithelial cell line L132 as measured by MTT assay
(performed in triplicate). Values of replicates are % of cell death.
Concen-rep-rep-rep-Mean%
tration*licatelicatelicateofLive
(μg/ml)123replicatesSDSEMcell
25039.5142.5244.0342.022.301.3357.98
10032.9334.4433.6933.690.750.4466.31
5030.6028.9430.5230.020.940.5469.98
2527.9627.8127.1327.630.440.2572.37
12.525.6225.5525.4025.520.120.0774.48
6.2523.1320.8718.6120.872.261.3179.13
3.12513.3411.0811.8312.081.150.6687.92
1.562 6.56 7.31 9.57 7.811.570.9192.19
0.781 8.06 4.30 3.54 5.302.421.4094.70

Summary Results: Cytotoxicity of Apple Stem Cell Extracts.

TABLE 10
IC50 values of the apple stem cell extracts on the on the target
cell lines as determined by MTT assay.
Target Cell
LineIC50
A54912.58
NCI-H52010.21
L132127.46

Apple stem cell extracts killed lung cancer cells lines A549 and NCI-H520 at relatively low doses: IC50s were 12.58 and 10.21 μg/ml respectively as compared to 127.46 μg/ml for the lung epithelial cell line L132. Near complete anti-tumor activity was seen at a dose of 250 μg/ml in both the lung cancer cell lines. This same dose spared more than one half of the L132 cells. See Tables 7-10. The data revealed that apple stem cell extract is cytotoxic to lung cancer cells while sparing lung epithelial cells. FIG. 6 shows a graphical representation of cytotoxicity activity of apple stem cell extracts on lung tumor cell lines A549, NCIH520 and on L132 lung epithelial cell line (marked “Normal”). The γ-axis is the mean % of cells killed by the indicated treatment compared to unexposed cells. The difference in cytotoxicity levels was statistically significant at p≤05.

Example 9

The experiment of Example 7 was repeated substituting other plant materials for ASC. Plant stem cell materials included Dandelion Root Extract (DRE), Aloe Vera Juice (AVJ), Apple Fiber Powder (AFP), Ginkgo Leaf Extract (GLE), Lingonberry Stem Cells (LSC), Orchid Stem Cells (OSC) as described in Examples 1 and 2. The concentrations of plant materials used were nominally 250, 100, 50, 25, 6.25, 3.125, 1.562, and 0.781 μg/mL. These materials were tested only for cells the human lung epithelial cell line L132 (as a proxy for normal epithelial cells) and for cells of the human lung adenocarcinoma cell line A549 (as a proxy for lung cancer cells).

A549 cells lung cancer cell line cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Cancer Cell Line A549 Cells.

TABLE 11
Triplicate results of cell death of DRE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-DRE-Live
treated A549% of cell deathMeanSDSEMcell
25080.4376.4074.8477.232.891.6722.77
10067.6075.2663.7768.885.853.3831.12
5065.3262.9459.9462.732.701.5637.27
2556.8357.9748.1454.315.383.1145.69
6.2555.5949.6949.1751.483.572.0648.52
3.12551.7648.4545.3448.523.211.8551.48
1.56243.6944.0036.0241.244.522.6158.76
0.78137.4726.1919.5727.749.055.2372.26

AVJ-Treated Lung Cancer Cell line A549 Cells.

TABLE 12
Triplicate results of cell death of AVJ-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AVJ-treatedLive
A549% of cell deathMeanSDSEMcell
25076.8178.1675.8876.951.140.6623.05
10076.4075.2673.7175.121.350.7824.88
5065.3266.1559.9463.803.371.9536.20
2550.1048.4556.6351.734.322.5048.27
6.2547.5246.3846.1746.690.720.4253.31
3.12539.8638.6143.7940.752.701.5659.25
1.56232.4019.7730.5427.576.823.9472.43
0.78120.5015.6332.1922.778.514.9277.23

AFP-Treated Lung Cancer Cell line A549 Cells.

TABLE 13
Triplicate results of cell death of AFP-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AFP-treatedLive
A549% of cell deathMeanSDSEMcell
25086.1387.9986.6586.920.960.5613.08
10079.5081.0682.0980.881.300.7519.12
5073.6072.4671.3372.461.140.6627.54
2568.0167.7066.9867.560.530.3132.44
6.2560.8762.1160.7761.250.750.4338.75
3.12549.4851.7650.7250.661.140.6649.34
1.56240.0641.7247.0042.933.622.0957.07
0.78139.2337.7836.8537.961.200.6962.04

GLE-treated Lung Cancer Cell line A549 Cells.

TABLE 14
Triplicate results of cell death of GLE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-GLE-treatedLive
A549% of cell deathMeanSDSEMcell
25088.4291.4990.4490.121.560.909.88
10084.3983.7783.1683.770.610.3516.23
5079.4781.5876.7579.272.421.4020.73
2573.6072.5471.4072.511.100.6327.49
6.2562.8963.6859.9162.161.991.1537.84
3.12550.1854.4751.8452.162.171.2547.84
1.56246.9344.3043.3344.851.861.0755.15
0.78139.5639.3940.9639.970.870.5060.03

LSC-treated lung cancer cell lines A549 cells.

TABLE 15
Triplicate results of cell death of LSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
LSC treated A549% of cell deathMeanSDSEMcell
25077.5478.8578.2078.200.650.3821.80
10077.1476.0476.5976.590.550.3223.41
5066.4268.5266.8267.251.120.6532.75
2559.8067.2264.1663.733.732.1536.27
6.2550.5348.8248.0749.141.260.7350.86
3.12541.1443.6042.7242.491.240.7257.51
1.56239.4739.7440.6139.940.600.3460.06
0.78138.5531.8336.7935.723.482.0164.28

OSC-treated Lung Cancer Cell line A549 Cells.

TABLE 16
Triplicate results of cell death of OSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
OSC-treated A549% of cell deathMeanSDSEMcell
25070.8465.5771.4969.303.251.8730.70
10048.8150.9157.2852.334.412.5547.67
5046.5949.6053.3349.843.381.9550.16
2538.7740.8136.5838.722.111.2261.28
6.2535.7440.7941.0539.193.001.7360.81
3.12534.5533.6837.0235.081.731.0064.92
1.56233.8633.4427.6331.643.482.0168.36
0.78121.3220.0034.8225.388.214.7474.62

L132 cells (“normal” lung epithelial cell line) cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 17
Triplicate results of cell death of DRE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration% of %
(μg/mL)cellLive
DRE-treated L132deathMeanSDSEMcell
25086.6686.6186.6686.640.030.0213.36
10076.2977.3976.8476.840.550.3223.16
5065.9268.1767.0167.031.130.6532.97
2555.5458.9557.1957.231.700.9842.77
6.2545.1749.7347.3747.422.281.3252.58
3.12534.8040.5037.5437.612.851.6562.39
1.56224.4231.2827.7227.813.431.9872.19
0.78114.0522.0617.8918.004.012.3182.00

AVJ-Treated Lung Epithelial Cell Line L132 cells.

TABLE 18
Triplicate results of cell death of AVJ-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration % of %
(μg/mL)cellLive
AVJ-treated L132deathMeanSDSEMcell
25057.0355.9353.6255.531.741.0044.47
10050.9949.7847.0449.272.031.1750.73
5044.9543.6340.4543.012.311.3456.99
2538.9137.4933.8636.752.601.5063.25
6.2532.8831.3427.2830.502.891.6769.50
3.12526.8425.1920.6924.243.181.8475.76
1.56220.8019.0514.1117.983.472.0082.02
0.78114.7612.90 7.5211.733.762.1788.27

AFP-Treated Lung Epithelial Cell Line L132 cells.

TABLE 19
Triplicate results of cell death of AFP-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
AFP-treated L132% of cell deathMeanSDSEMcell
25056.1555.4357.1956.260.880.5143.74
10049.9548.2447.6448.611.200.6951.39
5043.7441.0538.0940.962.831.6359.04
2537.5433.8628.5433.324.532.6166.68
6.2531.3426.6718.9925.676.243.6074.33
3.12525.1419.489.4418.027.954.5981.98
1.56218.9412.2910.8714.034.312.4985.97
0.78112.73 5.10 6.81 8.214.002.3191.79

GLE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 20
Triplicate results of cell death of GLE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
GLE-treated L132% of cell deathMeanSDSEMcell
25084.4283.2083.0883.570.740.4316.43
10080.0579.2978.5979.310.730.4220.69
5072.7571.5974.1072.811.260.7227.19
2580.0581.8679.9980.631.060.6119.37
6.2568.2670.1368.2668.881.080.6231.12
3.12560.6263.0760.6261.441.410.8238.56
1.56248.0748.7748.8348.560.420.2451.44
0.78146.2745.5746.6746.170.560.3253.83

LSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 21
Triplicate results of cell death of LSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
LSC-treated L132% of cell deathMeanSDSEMcell
25086.4185.8285.7686.000.350.2014.00
10081.2181.2779.9980.820.720.4219.18
5075.9674.7473.5174.741.230.7125.26
2574.7472.7571.4772.991.650.9527.01
6.2570.1368.3268.2668.901.060.6131.10
3.12554.0358.0553.4455.172.511.4544.83
1.56253.9751.9851.9852.641.150.6647.36
0.78146.7945.6244.9245.78 0.940.54 54.22

OSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 22
Triplicate results of cell death of OSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration %
(μg/mL)Live
OSC-treated L132% of cell deathMeanSDSEMcell
25061.8462.3760.4461.551.000.5738.45
10054.1453.4452.1053.231.040.6046.77
5042.9442.3040.3241.851.370.7958.15
2535.9434.4833.3134.581.320.7665.42
6.2533.9632.6732.0332.890.980.5767.11
3.12527.4826.2026.7226.800.650.3773.20
1.562 9.80 7.29 7.35 8.151.430.8391.85
0.781 7.29 8.98 8.05 8.110.850.4991.89

Calculated values.

TABLE 23
Calculated IC50 doses (ug/mL) and therapeutic ratios
(IC50 for L132 cells/IC50 for A549 cells) for each
treatment material. Values greater than one indicate
that a material would be more selective in killing cancer
cells than normal cells. ASC results imported from
Example 8. These studies indicate that at least
some of the materials may be effective anti-cancer agents.
ASC has outstanding selectivity compared to other materials.
ASCDREAVJAFPGLELSCOSC
A549 12.589.82211.4811.9811.1 13.733.9 
IC50
L132 127.4656.88 62.6682.6577.6369.26715.38
IC50
Ther.10.15.8 5.56.97.0 0.70.5
Ratio

US11872258B2. Oral cavity treatments (2024-01-16). None [US]. Inventors: Maryam Rahimi [US].
Patent 2024
14-3-3 Proteins 43-63 61-26 A549 Cells Action Potentials Adenocarcinoma of Lung Aloe Aloe vera Antineoplastic Agents Biological Assay Biological Factors Biological Processes Bromides Cardiac Arrest Cell Death Cell Extracts Cell Lines Cell Proliferation Cells Cell Survival Cytotoxin diphenyl DNA Replication Epistropheus Epithelial Cells Fibrosis Formazans Genetic Selection Ginkgo biloba Ginkgo biloba extract Homo sapiens Lingonberry Lung Lung Cancer Lung Neoplasms Malignant Neoplasms Mitochondria Mitochondrial Inheritance Neoplasms Neoplastic Stem Cells Oral Cavity PEG SD-01 Penicillins Pharmaceutical Preparations Phosphates Plant Cells Plant Leaves Plant Roots Plants Powder Psychological Inhibition Saline Solution SD 31 SD 62 SEM-76 Squamous Cell Carcinoma Stem, Plant Stem Cells Streptomycin Succinate Dehydrogenase Sulfoxide, Dimethyl Taraxacum Tetrazolium Salts
2
Micropeptide SEQ ID NO: 1 Regulates Cancer Cell Proliferation
Not available on PMC !

Example 3

Lung cancer cell line A549 and squamous cell carcinoma cell line H10 expressing inducible SEQ ID NO: 1-HA vector were established as described previously. SEQ ID NO: 1 expression was detected by qPCR (FIG. 7A) and by Western Blot (FIG. 7B). Immunostaining using a custom-made antibody against SEQ ID NO: 1 reveals a predominant cytoplasmic localization with a filamentous pattern. This data demonstrates that the micropeptide can also be expressed and detectable in these cell lines.

To evaluate the effects of SEQ ID NO: 1 on proliferation, A549 and H10 cells transduced with SEQ ID NO: 1-HA vector or control vector were monitored for 14 days. Growth curves show that cells overexpressing micropeptide SEQ ID NO: 1 have a consistently lower growth rate compared to the control (FIG. 8A). This effect in proliferation is also accompanied by an increase in cells arrested in G1 phase (FIG. 8B). Collectively with the data shown before in the pancreatic cell line BxPC-3, there is a strong evidence of the role of the micropeptide of SEQ ID NO: 1 in decreasing cell proliferation in several cancer types (pancreas, lung and squamous cell carcinoma).

US11858972B2. Micropeptides and uses thereof (2024-01-02). FUNDACIÓ PRIVADA INSTITUT D'INVESTIGACIÓ ONCOLÒGICA DE VALL HEBRON [ES]. Inventors: Maria Abad Méndez [ES], Olga Boix Sánchez [ES], Emanuela Greco [ES], Iñaki Merino Valverde [ES].
Patent 2024
Adenocarcinoma of Lung Cell Cycle Arrest Cell Lines Cell Proliferation Cells Cloning Vectors Cytoplasmic Filaments G1 Phase Immunoglobulins Lung Lung Cancer Malignant Neoplasms Pancreas Squamous Cell Carcinoma Western Blot
3
Evaluating Cytotoxicity of Compounds in Cancer Cells
Not available on PMC !

Example 5

The human skin squamous carcinoma line (HSC) (Japanese Collection of Research Bioresources Cell Bank (JCRB)) was cultured in Dulbecco's minimal essential media (DMEM) containing 20% fetal bovine serum (FBS), (Thermofisher, Waltham, MA). Cells were plated at a density of 5,000 cells per well into a 96-well plate and allowed to attach for 24 h in a 37° C. humidified incubator with a 5% CO2 atmosphere. Media was replaced with that containing test agents or vehicle (0.1% dimethylsulphoxide) in 1% FBS and cells incubated for a further 72 h. Cell viability was assessed using CellTiter-Glo® (Promega). Non-linear regression analysis was performed using GraphPad PRISM®. FIG. 5 shows a representative experiment where the EC50 of COMPOUND A, was 5.2 nM and that of COMPOUND G was 9.2 nM.

US11865113B2. Methods of treating a patient afflicted with non-alcoholic steatohepatitis (NASH) (2024-01-09). Lipidio Pharmaceuticals Inc. [US]. Inventors: Nigel R. A. Beeley [US], J. Gordon Foulkes [US], Kieran George Mooney [US], Charles Rodney Greenaway Evans [GB], Keith Arthur Johnson [US], Howard G. Welgus [US], Celia P. Jenkinson [US].
Patent 2024
Atmosphere Carcinoma Cells Cell Survival Culture Media Cytotoxin Fetal Bovine Serum Homo sapiens inhibitors Japanese prisma Promega Skin Squamous Cell Carcinoma Squamous Epithelial Cells Sulfoxide, Dimethyl
4
Retreatment with Immunotherapy in Metastatic Cervical Cancer
From June 2019 to April 2021, patients with metastatic cervical cancer who received ICI retreatment at the Cancer Center, Union Hospital, Huazhong University of Science and Technology, Wuhan, China, were enrolled in this study. The inclusion criteria were as follows: (1) pathologically confirmed squamous cell carcinoma, adenocarcinoma, or adenosquamous carcinoma of the cervix; (2) metastatic cervical cancer; (3) achieved complete response (CR), partial response (PR), or stable disease (SD) as the best clinical response to first-course immunotherapy; (4) received at least two cycles of retreatment with triplet combination therapy including PD-1 inhibitor, chemotherapy, and antiangiogenic agent; (5) had at least one measurable lesion according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1; and (6) Eastern Cooperative Oncology Group performance score of 1 or less. Patients who did not have the follow-up data were excluded from the analyses. Baseline clinicopathological data, including age, histology, initial stage, metastatic sites, primary surgery, lines of prior systemic treatment, and immunotherapy regimens, were retrieved from medical records.
This retrospective study was conducted in accordance with the principles embodied in the 1964 Declaration of Helsinki and was approved by the Ethics Committee of the Union Hospital of the Huazhong University of Science and Technology (20220023). Informed consent was obtained from all the participants or their legal guardians if the participants cannot write.
Li G., Cheng M., Hong K, & Jiang Y. (2023). Clinical Efficacy and Safety of Immunotherapy Retreatment in Metastatic Cervical Cancer: A Retrospective Study. OncoTargets and Therapy, 16, 157-163.
Publication 2023
Adenocarcinoma Angiogenesis Inhibitors Cervical Cancer Combined Modality Therapy Ethics Committees, Clinical Immunotherapy Legal Guardians Malignant Neoplasms Neck Neoplasm Metastasis Neoplasms Operative Surgical Procedures Patients Pharmacotherapy Programmed Cell Death Protein 1 Inhibitor Retreatments Squamous Cell Carcinoma Treatment Protocols Triplets Vitelliform Macular Dystrophy
5
Silencing hsa_circ_0000276 in Cervical Cancer
Cells were cultured in complete medium, composed of 10% fetal bovine serum (Sangong Biotech, Shanghai, China), 1% penicillin–streptomycin solution, and DMEM high-glucose medium, in a 5% CO2 incubator at 37 °C. The normal cervical epithelial cells HcerEpic and HPV16-positive human cervical squamous carcinoma SiHa and CaSki cells were grown in a culture dish, and the liquid was changed once every 2 days. Cells were transfected with short hairpin RNAs (shRNAs) designed to target hsa_circ_0000276. Cells were cultured in six-well plates, and when they reached approximately 70% confluence, they were transfected with shRNA or shNC (GenePharma, Shanghai, China) using Lipofectamine 2000 (Invitrogen, Waltham, USA) and collected after 24–48 h. Transfection efficiency in cells was verified using polymerase chain reaction (PCR).
Zhang H., Wang X., Li Y., Bai Y., Li Q., Wang S., Wei Y., Li J., Wen S, & Zhao W. (2023). The hsa_circ_0000276-ceRNA regulatory network and immune infiltration in cervical cancer. BMC Cancer, 23, 222.
Publication 2023
Cells Epithelial Cells Fetal Bovine Serum Glucose Homo sapiens Human papillomavirus 16 Hyperostosis, Diffuse Idiopathic Skeletal lipofectamine 2000 Neck Penicillins Short Hairpin RNA Squamous Cell Carcinoma Streptomycin Transfection

Top products related to «Squamous Cell Carcinoma»

1
Fetal bovine serum (fbs) by Thermo Fisher Scientific
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
2
Dulbecco modified eagle medium (dmem) by Thermo Fisher Scientific
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DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.
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Penicillin by Thermo Fisher Scientific
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Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
4
Streptomycin by Thermo Fisher Scientific
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Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.
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Penicillin streptomycin by Thermo Fisher Scientific
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Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.
6
Rpmi 1640 medium by Thermo Fisher Scientific
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RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.
7
Rpmi 1640 by Thermo Fisher Scientific
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RPMI 1640 is a common cell culture medium used for the in vitro cultivation of a variety of cells, including human and animal cells. It provides a balanced salt solution and a source of essential nutrients and growth factors to support cell growth and proliferation.
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L glutamine by Thermo Fisher Scientific
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L-glutamine is an amino acid that is commonly used as a dietary supplement and in cell culture media. It serves as a source of nitrogen and supports cellular growth and metabolism.
9
Fetal bovine serum (fbs) by Merck Group
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FBS, or Fetal Bovine Serum, is a commonly used cell culture supplement. It is derived from the blood of bovine fetuses and provides essential growth factors, hormones, and other nutrients to support the growth and proliferation of a wide range of cell types in vitro.
10
Cal27 by American Type Culture Collection
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The CAL27 is a cell line derived from a human squamous cell carcinoma of the tongue. It is a commonly used in vitro model for oral cancer research.

More about "Squamous Cell Carcinoma"

Squamous cell carcinoma (SCC) is a type of cancer that originates from squamous cells, which are flat cells that line many of the body's organs.
This form of cancer can occur in various parts of the body, such as the skin, lungs, esophagus, and cervix.
SCCs are typically slow-growing and can often be treated effectively if caught early.
Key subtopics related to SCC include: - Epidemiology and risk factors: SCC is the second most common type of skin cancer, and it is often associated with exposure to UV radiation, smoking, and certain viral infections. - Pathogenesis: SCC is characterized by the abnormal growth and proliferation of squamous cells, which can lead to the formation of tumors. - Diagnosis and staging: SCC is typically diagnosed through a biopsy, and staging is based on factors such as the size and depth of the tumor, as well as the presence of metastases. - Treatment options: Treatment for SCC may include surgery, radiation therapy, chemotherapy, and targeted therapies, depending on the stage and location of the cancer. - Cell culture models: SCC research often utilizes cell lines such as CAL27, which are derived from SCC tumors and can be cultured in media like DMEM, RPMI 1640, and supplements like FBS, L-glutamine, penicillin, and streptomycin.
Understanding the characteristics and treatment options for squamous cell carcinoma is crucial for improving patient outcomes and advancing medical research in this field.
The use of AI-powered platforms like PubCompare.ai can help researchers optimize their SCC research protocols and improve the reproducibility and accuracy of their findings.