Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics

Table 3 Standard curve linear equation

Class	Equation	r	LLOQ	ULOQ
BA	y = 5.89032 x + 0.00349	0.99952	0.001	1
CAR	y = 10.63664 x - 9.00378e-4	0.99178	0.005	5
CE	y = 4.15246 x - 0.00124	0.99310	0.01	5
Cer	y = 2.90771 x + 0.00126	0.99628	0.002	2
CerP	y = 0.14978 x + 7.83392e-5	0.99815	0.005	2
Cho	y = 4.27736 x + 0.02240	0.99768	0.005	5
CoQ	y = 3.64824 x + 1.73992	0.99749	0.002	2
DG	y = 7.54751 x + 0.04692	0.99412	0.002	2
Eicosanoid	y = 61.65750 x + 0.08742	0.99661	0.001	1
FFA	y = 22.05858 x - 0.21591	0.99297	0.02	10
HexCer	y = 1.40675 x - 2.86002e-5	0.99152	0.002	2
LPC	y = 2.87925 x - 0.00565	0.99009	0.01	5
LPC-O	y = 2.87925 x - 0.00565	0.99009	0.01	5
LPE	y = 0.43538 x - 0.00138	0.99562	0.02	5
LPE-P	y = 0.43538 x - 0.00138	0.99562	0.02	5
LPG	y = 0.62714 x - 1.52689e-5	0.99848	0.005	2
LPI	y = 0.06717 x + 0.00722	0.99669	0.002	2
LPS	y = 0.10692 x + 2.42176e-5	0.99127	0.01	2
PC	y = 1.39542 x - 6.09826e-4	0.99651	0.002	10
PC-O	y = 1.39542 x - 6.09826e-4	0.99651	0.002	10
PE	y = 14.19927 x - 0.02853	0.99327	0.01	5
PE-P	y = 1.01408 x - 0.00815	0.99613	0.02	5
PG	y = 6.97289 x + 0.01424	0.99033	0.01	10
PI	y = 3.05988 x - 0.15491	0.99717	0.02	5
PS	y = 6.52668 x - 0.37092	0.99437	0.01	10
SM	y = 0.68841 x - 5.22709e-4	0.99110	0.002	10
SPH	y = 1.80406 x + 4.28477e-4	0.99513	0.005	2
TG	y = 1.33356 x + 5.11722e-4	0.99125	0.005	10

Class: lipid classification; Equation: linear equation; r: the correlation coefficient; LLOQ (nmol/mL): lower limit of quantification; ULOQ (nmol/mL): upper limit of quantification

ISSN: 1476-511X