Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

Ren, Xueting; Cui, Hanxiao; Wu, Jianhua; Zhou, Ruina; Wang, Nan; Liu, Dandan; Xie, Xin; Zhang, Hao; Liu, Di; Ma, Xiuli; Dang, Chengxue; Kang, Huafeng; Lin, Shuai

doi:10.1002/cam4.4755

Cited by 13 publications

(7 citation statements)

References 80 publications

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“…31 Recent findings corroborate the prognostic prowess of a 16-gene signature involving MAP2K6 in breast cancer patients, further emphasizing its significance. 32 Additionally, SDC1 emerges as an independent prognostic risk factor associated with poor overall survival in breast cancer, 33 reinforcing the implications of the current findings. Collectively, these observations underscore the substantial value of the MAP2K6 gene in the model that was constructed in the current study.…”

Section: Discussionsupporting

confidence: 71%

Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms

Hu,

Qin,

Zhang

et al. 2024

J Int Med Res

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Objective To construct a prognostic model of a breast cancer-related oxidative stress-related gene (OSRG) signature using machine learning algorithms. Methods The OSRGs of breast cancer were constructed by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The Cancer Genome Atlas (TCGA) was used to analyse the gene expression and prognostic value. The Human Protein Atlas was used to analyse the protein expression of hub genes. Receiver operating characteristic analysis, calibration curve and decision curve analysis were used to predict the stability of this model. Results The area under the curve of 1-, 3- and 5-year overall survival were 0.751, 0.707 and 0.645 in the TCGA training dataset; and 0.692, 0.678 and 0.602 in the TCGA testing dataset, respectively. Calibration plot showed good agreement between predicted probabilities and observed outcomes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) pathway analysis indicated that multiple cancer-related pathways were highly enriched in the high-risk group. Immune infiltration analysis showed immune cells and their functions may play a key role in the development and mechanism of breast cancer. Conclusions This new OSRG signature was associated with the immune infiltration and it might be useful in predicting the prognosis in patients with breast cancer.

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Section: Discussionsupporting

confidence: 71%

Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms

Hu,

Qin,

Zhang

et al. 2024

J Int Med Res

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“…The Nup107-160 complex potentially facilitates the nuclear translocation of phosphorylated ERK to control the malignant transformation of senescent cells [45,46]. NUP43 overexpression contributes to the advancement of multiple malignant cancers, including gastric cancer, breast cancer, and ovarian cancer [47][48][49]. Nevertheless, the role of NUP43 in CRC has not been reported.…”

Section: Discussionmentioning

confidence: 99%

NUP43 promotes PD-L1/nPD-L1/PD-L1 feedback loop via TM4SF1/JAK/STAT3 pathway in colorectal cancer progression and metastatsis

Wu,

Sun,

Nai

et al. 2024

Cell Death Discov.

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Programmed cell death-ligand 1 (PD-L1) has a significant role in tumor progression and metastasis, facilitating tumor cell evasion from immune surveillance. PD-L1 can be detected in the tumor cell nucleus and exert an oncogenic effect by nuclear translocation. Colorectal cancer (CRC) progression and liver metastasis (CCLM) are among the most lethal diseases worldwide, but the mechanism of PD-L1 nuclear translocation in CRC and CCLM remains to be fully understood. In this study, using CRISPR-Cas9-based genome-wide screening combined with RNA-seq, we found that the oncogenic factor NUP43 impacted the process of PD-L1 nuclear translocation by regulating the expression level of the PD-L1 chaperone protein IPO5. Subsequent investigation revealed that this process could stimulate the expression of tumor-promoting factor TM4SF1 and further activate the JAK/STAT3 signaling pathway, which ultimately enhanced the transcription of PD-L1, thus establishing a PD-L1-nPD-L1-PD-L1 feedback loop that ultimately promoted CRC progression and CCLM. In conclusion, our study reveals a novel role for nPD-L1 in CRC, identifies the PD-L1-nPD-L1-PD-L1 feedback loop in CRC, and provides a therapeutic strategy for CRC patients.

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“…In addition, RCL1 has shown strong potential for predicting overall and disease-free survival of HCC patients [ 46 ], while FANCI has been identified as a reliable marker of hepatitis B virus-associated HCC [ 47 ]. Previous studies have indicated that RCL1 could independently predict breast cancer prognosis [ 48 ] and copy number variants of RCL1 are associated with a range of neuropsychiatric phenotypes [ 49 ]. Above all, our study showed that SORBS2, FANCI, DHRS1, and IGFALS can be mutated in HCC, and the effects of these mutations should be explored in future work.…”

Section: Discussionmentioning

confidence: 99%

Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

Lin

Gao

et al. 2022

Cancers

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Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.

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Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

Cited by 13 publications

References 80 publications

Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms

Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms

NUP43 promotes PD-L1/nPD-L1/PD-L1 feedback loop via TM4SF1/JAK/STAT3 pathway in colorectal cancer progression and metastatsis

Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

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