a375 atcc Search Results


99
ATCC human melanoma cell line a375
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
Human Melanoma Cell Line A375, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/human melanoma cell line a375/product/ATCC
Average 99 stars, based on 1 article reviews
human melanoma cell line a375 - by Bioz Stars, 2026-05
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97
ATCC human ocular choroidal melanoma cell line
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
Human Ocular Choroidal Melanoma Cell Line, supplied by ATCC, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/human ocular choroidal melanoma cell line/product/ATCC
Average 97 stars, based on 1 article reviews
human ocular choroidal melanoma cell line - by Bioz Stars, 2026-05
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99
ATCC human melanoma cells
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
Human Melanoma Cells, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/human melanoma cells/product/ATCC
Average 99 stars, based on 1 article reviews
human melanoma cells - by Bioz Stars, 2026-05
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95
ATCC a375 m2
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
A375 M2, supplied by ATCC, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/a375 m2/product/ATCC
Average 95 stars, based on 1 article reviews
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a375p  (ATCC)
95
ATCC a375p
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
A375p, supplied by ATCC, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/a375p/product/ATCC
Average 95 stars, based on 1 article reviews
a375p - by Bioz Stars, 2026-05
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94
ATCC cancer cell lines a375 s2
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
Cancer Cell Lines A375 S2, supplied by ATCC, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/cancer cell lines a375 s2/product/ATCC
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cancer cell lines a375 s2 - by Bioz Stars, 2026-05
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a375  (ATCC)
92
ATCC a375
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
A375, supplied by ATCC, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/a375/product/ATCC
Average 92 stars, based on 1 article reviews
a375 - by Bioz Stars, 2026-05
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93
ATCC a375 ma1 cells
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
A375 Ma1 Cells, supplied by ATCC, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/a375 ma1 cells/product/ATCC
Average 93 stars, based on 1 article reviews
a375 ma1 cells - by Bioz Stars, 2026-05
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90
ATCC 375 cell line
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
375 Cell Line, supplied by ATCC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/375 cell line/product/ATCC
Average 90 stars, based on 1 article reviews
375 cell line - by Bioz Stars, 2026-05
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93
ATCC kras mutant cell lines
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
Kras Mutant Cell Lines, supplied by ATCC, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/kras mutant cell lines/product/ATCC
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kras mutant cell lines - by Bioz Stars, 2026-05
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92
ATCC brafv600e nrasq61k
Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of <t>A375</t> cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.
Brafv600e Nrasq61k, supplied by ATCC, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/brafv600e nrasq61k/product/ATCC
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brafv600e nrasq61k - by Bioz Stars, 2026-05
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Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of A375 cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.

Journal: Journal for Immunotherapy of Cancer

Article Title: AGPAT3 reshapes tumor cell vulnerability to IFNγ-mediated ferroptosis and enhances immunotherapy efficacy through lipid remodeling

doi: 10.1136/jitc-2025-013305

Figure Lengend Snippet: Prediction of ICI outcomes using the FD.sig model with IFN-γ as a key factor. ( A ) Flowchart of constructing FD.model for ICI prediction response. ( B ) The volcano plot of FD.score between tumor and normal tissues in TCGA data; ( C ) Survival analysis of FD.score in TCGA-SKCM. ( D ) ROC curves of eight machine-learning methods in testing set. ( E ) AUCs of eight methods in training and testing sets. ( F ) Comparison of AUCs between FD.sig and other signatures in training and testing sets. ( G ) Comparison of AUCs between FD.sig and other signatures in each cohort. ( H ) The importance ranking of genes in FD.model. ( I ) Comparison of IFNG expression on T cells between responder, non-responder and treatment-naïve in GSE115978 . ( J–K ) Cell viability of A375 cells and H1299 cells treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 0–72 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( L–M ) Relative ROS level of A375 ( L ) and H1299 cells ( M ) treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. AUC, area under the receiver operating characteristic curve; BLCA, bladder urothelial carcinoma; BRCA, breast cancer; CRAD, colorectal adenocarcinoma; FD.model, ferroptosis-based machine learning model; FD.sig, ferroptosis-driver signature; HNSC, head and neck squamous carcinoma; ICI, immune checkpoint inhibitor; IFN, interferon; 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; NR, non-responders; OS, overall survival; PRAD, prostate adenocarcinoma; R, responders; ROC, receiver operating characteristic; ROS, reactive oxygen species; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma.

Article Snippet: Human melanoma cell line A375 was purchased from FuHeng Biology (Shanghai, China), while the human lung cancer cell line H1299, mouse melanoma cell line B16F10 (B16), and mouse lung cancer cell line lewis lung carcinoma (LLC) were purchased from American Type Culture Collection (ATCC).

Techniques: Comparison, Expressing, Incubation, Single Cell, RNA Sequencing

IFN-γ induces the sensitivity of ferroptosis through lipid remodeling. ( A–B ) Ratios of different lipids in A375 cells and H1299 cells after IFN-γ incubation for different times, IFN-γ: 60 mg/mL. ( C ) Differential analysis of free fatty acids compared the control group with IFN-γ incubation groups in A375 cells and H1299 cells, IFN-γ: 60 mg/mL. ( D ) Ratios of ether lipids and ester lipids in H1299 cells after IFN-γ incubation, IFN-γ: 60 mg/mL. ( E–F ) Heatmap of ether-PC and ether-PE in A375 ( E ) and H1299 cells ( F ) after IFN-γ incubation, IFN-γ: 60 mg/mL. FA, fatty acyls; GL, glycerolipids; GP, glycerophospholipids; IFN, interferon; MUFA, monounsaturated fatty acid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PR, prenol lipids; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; SP, sphingolipids; ST, sterol lipids.

Journal: Journal for Immunotherapy of Cancer

Article Title: AGPAT3 reshapes tumor cell vulnerability to IFNγ-mediated ferroptosis and enhances immunotherapy efficacy through lipid remodeling

doi: 10.1136/jitc-2025-013305

Figure Lengend Snippet: IFN-γ induces the sensitivity of ferroptosis through lipid remodeling. ( A–B ) Ratios of different lipids in A375 cells and H1299 cells after IFN-γ incubation for different times, IFN-γ: 60 mg/mL. ( C ) Differential analysis of free fatty acids compared the control group with IFN-γ incubation groups in A375 cells and H1299 cells, IFN-γ: 60 mg/mL. ( D ) Ratios of ether lipids and ester lipids in H1299 cells after IFN-γ incubation, IFN-γ: 60 mg/mL. ( E–F ) Heatmap of ether-PC and ether-PE in A375 ( E ) and H1299 cells ( F ) after IFN-γ incubation, IFN-γ: 60 mg/mL. FA, fatty acyls; GL, glycerolipids; GP, glycerophospholipids; IFN, interferon; MUFA, monounsaturated fatty acid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PR, prenol lipids; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; SP, sphingolipids; ST, sterol lipids.

Article Snippet: Human melanoma cell line A375 was purchased from FuHeng Biology (Shanghai, China), while the human lung cancer cell line H1299, mouse melanoma cell line B16F10 (B16), and mouse lung cancer cell line lewis lung carcinoma (LLC) were purchased from American Type Culture Collection (ATCC).

Techniques: Incubation, Control

IFN-γ induces the sensitivity of ferroptosis through ether lipids. ( A, C ) Volcano plot of ether-PC and ether-PE compared the control group with IFN-γ incubation groups in A375 cells and H1299 cells, IFN-γ: 60 mg/mL. ( B, D ) Intersection Venn diagram of differential lipids in A375 cells and H1299 cells. ( E–J ) Cell viability of A375 and H1299 cells treated with RSL3/RSL3+Fer-1 for 12 hours after incubation with indicated phospholipids, including PC 18_18:1 ( E ), PE 18_18:1 ( F ), PC 18_20:4 ( G ), PE 18_20:4 ( H ), PC 18_22:6 ( I ), PE 18_22:6 ( J ), phospholipids: 25 µM, Fer-1: 1 µm. ( K ) Comparison of lipid metabolism pathways with transcriptome data among groups with different IFN-γ incubation times in A375 cells and H1299 cells, IFN-γ: 60 mg/mL. IFN, interferon; PC, phosphatidylcholine; PE, phosphatidylethanolamine.

Journal: Journal for Immunotherapy of Cancer

Article Title: AGPAT3 reshapes tumor cell vulnerability to IFNγ-mediated ferroptosis and enhances immunotherapy efficacy through lipid remodeling

doi: 10.1136/jitc-2025-013305

Figure Lengend Snippet: IFN-γ induces the sensitivity of ferroptosis through ether lipids. ( A, C ) Volcano plot of ether-PC and ether-PE compared the control group with IFN-γ incubation groups in A375 cells and H1299 cells, IFN-γ: 60 mg/mL. ( B, D ) Intersection Venn diagram of differential lipids in A375 cells and H1299 cells. ( E–J ) Cell viability of A375 and H1299 cells treated with RSL3/RSL3+Fer-1 for 12 hours after incubation with indicated phospholipids, including PC 18_18:1 ( E ), PE 18_18:1 ( F ), PC 18_20:4 ( G ), PE 18_20:4 ( H ), PC 18_22:6 ( I ), PE 18_22:6 ( J ), phospholipids: 25 µM, Fer-1: 1 µm. ( K ) Comparison of lipid metabolism pathways with transcriptome data among groups with different IFN-γ incubation times in A375 cells and H1299 cells, IFN-γ: 60 mg/mL. IFN, interferon; PC, phosphatidylcholine; PE, phosphatidylethanolamine.

Article Snippet: Human melanoma cell line A375 was purchased from FuHeng Biology (Shanghai, China), while the human lung cancer cell line H1299, mouse melanoma cell line B16F10 (B16), and mouse lung cancer cell line lewis lung carcinoma (LLC) were purchased from American Type Culture Collection (ATCC).

Techniques: Control, Incubation, Comparison

IFN-γ alters the ether lipid metabolism via the IRF1-AGPAT3 axis. ( A ) Schematic summarizing the PUFA-ePLs biosynthesis pathway and their contribution to ferroptosis susceptibility. ( B ) Heatmap of different groups on the expression of specific genes in A375 cells and H1299 cells. ( C ) Representative western blot for AGPS, AGPAT3 and GNPAT in A375 cells and H1299 cells after IFN-γ incubation, IFN-γ: 60 mg/mL. ( D ) Cell viability of different groups treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( E ) Relative ROS level of different groups treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. ( F ) Heatmap of different groups on the expression of IFN-γ downstream transcription factors in A375 cells and H1299 cells. ( G–H ) IRF1 binding sites at the AGPAT3 promoter region in lung cancer cell lines from GSE186168 ChIP-seq data. ( I–J ) The peak of IRF1 binding at the Agpat3 promoter region becomes higher after IFN-γ incubation from GSE201881 ( I ) and GSE141606 ( J ) ChIP-seq data. ( K ) IRF1 binding sites at the AGPAT3 promoter region in A375 from CUT&Tag data. ChIP-seq, chromatin Immunoprecipitation sequencing; CUT&Tag, Cleavage Under Targets and Tagmentation; IFN, interferon; PUFA-ePLs, polyunsaturated ether phospholipids; ROS, reactive oxygen species.

Journal: Journal for Immunotherapy of Cancer

Article Title: AGPAT3 reshapes tumor cell vulnerability to IFNγ-mediated ferroptosis and enhances immunotherapy efficacy through lipid remodeling

doi: 10.1136/jitc-2025-013305

Figure Lengend Snippet: IFN-γ alters the ether lipid metabolism via the IRF1-AGPAT3 axis. ( A ) Schematic summarizing the PUFA-ePLs biosynthesis pathway and their contribution to ferroptosis susceptibility. ( B ) Heatmap of different groups on the expression of specific genes in A375 cells and H1299 cells. ( C ) Representative western blot for AGPS, AGPAT3 and GNPAT in A375 cells and H1299 cells after IFN-γ incubation, IFN-γ: 60 mg/mL. ( D ) Cell viability of different groups treated with different concentrations of RSL3/RSL3+Fer-1 for 24 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 100 mg/mL, Fer-1: 1 µM. ( E ) Relative ROS level of different groups treated with RSL3/RSL3+Fer-1 for 3 hours after IFN-γ incubation for 48 hours (n=3), IFN-γ: 60 mg/mL, RSL3: 500 nM, Fer-1: 1 µM. ( F ) Heatmap of different groups on the expression of IFN-γ downstream transcription factors in A375 cells and H1299 cells. ( G–H ) IRF1 binding sites at the AGPAT3 promoter region in lung cancer cell lines from GSE186168 ChIP-seq data. ( I–J ) The peak of IRF1 binding at the Agpat3 promoter region becomes higher after IFN-γ incubation from GSE201881 ( I ) and GSE141606 ( J ) ChIP-seq data. ( K ) IRF1 binding sites at the AGPAT3 promoter region in A375 from CUT&Tag data. ChIP-seq, chromatin Immunoprecipitation sequencing; CUT&Tag, Cleavage Under Targets and Tagmentation; IFN, interferon; PUFA-ePLs, polyunsaturated ether phospholipids; ROS, reactive oxygen species.

Article Snippet: Human melanoma cell line A375 was purchased from FuHeng Biology (Shanghai, China), while the human lung cancer cell line H1299, mouse melanoma cell line B16F10 (B16), and mouse lung cancer cell line lewis lung carcinoma (LLC) were purchased from American Type Culture Collection (ATCC).

Techniques: Expressing, Western Blot, Incubation, Binding Assay, ChIP-sequencing

IFN-γ alters the ether lipid metabolism via the IRF1-AGPAT3 axis. ( A–B ) Changes in ratio of ether lipids and eater lipids in A375 and H1299 cells in NC, sh-AGPAT3 and sh-AGPAT3+IFN-γ group, IFN-γ: 60 mg/mL. ( C–D ) Changes in ratio of different ether lipids in A375 and H1299 cells in NC, sh-AGPAT3 and sh-AGPAT3+IFN-γ group, IFN-γ: 60 mg/mL. ( E–F ) Heatmap of ether-PC and ether-PE in A375 ( E ) and H1299 cells ( F ) in different groups, IFN-γ: 60 mg/mL. IFN, interferon; NC, normal control; PC, phosphatidylcholine; PE, phosphatidylethanolamine.

Journal: Journal for Immunotherapy of Cancer

Article Title: AGPAT3 reshapes tumor cell vulnerability to IFNγ-mediated ferroptosis and enhances immunotherapy efficacy through lipid remodeling

doi: 10.1136/jitc-2025-013305

Figure Lengend Snippet: IFN-γ alters the ether lipid metabolism via the IRF1-AGPAT3 axis. ( A–B ) Changes in ratio of ether lipids and eater lipids in A375 and H1299 cells in NC, sh-AGPAT3 and sh-AGPAT3+IFN-γ group, IFN-γ: 60 mg/mL. ( C–D ) Changes in ratio of different ether lipids in A375 and H1299 cells in NC, sh-AGPAT3 and sh-AGPAT3+IFN-γ group, IFN-γ: 60 mg/mL. ( E–F ) Heatmap of ether-PC and ether-PE in A375 ( E ) and H1299 cells ( F ) in different groups, IFN-γ: 60 mg/mL. IFN, interferon; NC, normal control; PC, phosphatidylcholine; PE, phosphatidylethanolamine.

Article Snippet: Human melanoma cell line A375 was purchased from FuHeng Biology (Shanghai, China), while the human lung cancer cell line H1299, mouse melanoma cell line B16F10 (B16), and mouse lung cancer cell line lewis lung carcinoma (LLC) were purchased from American Type Culture Collection (ATCC).

Techniques: Control