Identified metabolic related genes to construct a prognostic model of Breast cancer patients

doi:10.21203/rs.2.21431/v1

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Identified metabolic related genes to construct a prognostic model of Breast cancer patients

https://doi.org/10.21203/rs.2.21431/v1

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Background: More and more evidence confirms that there are many metabolic disorders in the tumor. The occurrence and development of breast cancer (BC) is closely related to metabolism.

Methods: A metabolic related genes table was obtained by the Kyoto Encyclopedia of Genes and Genomes (KEGG) related metabolic pathway. The edgeR package was used to identify differentially expressed genes (DEGs) of The Cancer Genome Atlas (TCGA) breast cancer. We established a prognostic model by univariate Cox regression analysis and lasso-penalized Cox regression. The validation prognostic model was built through the Group on Earth Observations (GEO) database. Use the nomogram and Receiver Operating Characteristic (ROC) curve to verify the accuracy of models.

Result: We identified 178 DEGs and 14 prognostic-related genes to construct a prognostic model. In the TCGA prognostic model and the GEO validation prognostic model, patients were divided into high riskscore group and low riskscore group, the high riskscore group had worse prognosis.

Conclusion: We constructed a prognostic model of metabolic related genes and verified the feasibility and accuracy of the model. It is hoped that the model can provide a basis and biomarker for breast cancer related metabolic therapy and prognosis.

Cancer Biology

TCGA

breast cancer

metabolism

prognostic model

Table 1 Primer sequences for qPCR

Primer	Seqence 5’-3’
TSTA3 forward	GCTGGACGCAGGTGCAA
TSTA3 reverse	AGAGGATGATGGGCTCCACT
MTHFD2 forward	GATCCTGGTTGGCGAGAATC
MTHFD2 reverse	TCTGGAAGAGGCAACTGAACA
SEPHS2 forward	GGAAAGGAGGACCTGCAACCA
SEPHS2 reverse	ACCAGGAATCTGCCGCAAAAG
P4HA1 forward	ACCACAGCACAGTACAGAGTAT
P4HA1 reverse	GGAAACATCCAGTCCTGTGAG
INPP1 forward	TGGAGAACAAGTTTCCAGGCTT
INPP1 reverse	CTGTTTCTTCCTCTGTTGAACACAA
GAPDH forward	CAACGAATTTGGCTACAGCA
GAPDH reverse	AGGGGTCTACATGGCAACTG

Table 2. The Univariate Cox regression analysis of TCGA BC patients

gene	Univariate analysis
gene	HR	HR.95L	HR.95H	P value
TYMP	0.790	0.681	0.898	0.020
QPRT	1.927	1.607	2.347	0.007
TSTA3	1.709	1.301	1.916	0.019
MTHFD2	1.714	1.504	1.924	0.006
IDO1	0.767	0.839	0.596	0.028
UGP2	1.616	1.401	2.032	0.034
INPP1	0.840	0.781	0.892	0.036
P4HA1	1.505	1.300	1.910	0.033
HGD	1.030	1.002	1.059	0.038
PLA2G2D	0.899	0.831	0.971	0.007
IL4I1	0.643	0.802	0.586	0.010
SEPHS2	1.905	1.400	2.109	0.032
ALDH2	0.841	0.744	0.899	0.041
LYPLA1	1.709	1.301	2.017	0.033

Table 3. Univariate and Multivariate Cox regression analysis of TCGA prognostic model

Risk Factors	Overall Survival (OS)
	Univariate analysis		Multivariate analysis
	OR (95% CI)	P value	OR (95% CI)	P value
age	1.731 (1.317-2.647)	0.001	1.531 (1.217-2.031)	0.021
AJCC stage	2.048 (1.603-2.616)	<0.001	2.285 (1.443-3.619)	<0.001
T stage	1.379 (1.100-1.729)	0.034	0.800 (0.596-1.076)	0.140
N stage	1.578 (1.300-1.900)	0.026	0.959 (0.700-1.316)	0.780
Risk Score	3.095 (2.145-4.465)	<0.001	2.519 (1.761-3.603)	<0.001

Table 4. Univariate and Multivariate Cox regression analysis of GSE88770 validated prognostic model

Risk Factors	Overall Survival (OS)
	Univariate analysis		Multivariate analysis
	OR (95% CI)	P value	OR (95% CI)	P value
Grade	0.482 (0.210-0.879)	0.047	0.456 (0.175-1.136)	0.091
N stage	0.751 (0.468-2.616)	0.207	0.833 (0.515-1.347)	0.456
Ki-67	1.483 (1.056-1.789)	0.095	1.398 (0.808-2.417)	0.231
Risk Score	2.286 (1.059-4.465)	0.036	2.371 (1.079-5.207)	0.032

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Identified metabolic related genes to construct a prognostic model of Breast cancer patients

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