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Introduction

Cancer is a complex disease involving multiple steps for the transition from a normal state to a neoplastic growth state, followed by the generation of malignant cells. The ‘hallmarks of cancer’ concept encompasses 14 elements required for this process in human cancers (1), and metabolic reprogramming is one of these elements (1,2). Several characteristic metabolic mechanisms involving glucose and amino acids (AAs) in cancer cells have been revealed, and new insights continue to emerge (2). Tumor tissues are exposed to stress through insufficient supply of molecular oxygen and nutrients (3). Cancer cells must adapt to these severe environmental conditions and change their mechanisms for energy acquisition and immune evasion to achieve progression (3). A recent review has revealed that metabolic crosstalk between cancer cells and tumor stromal cells, including immune cells and fibroblasts, can promote symbiosis and malignancy (3).

Since the discovery of the Warburg effect, glucose has been regarded as a primary nutrient for cancer cells through aerobic glycolysis (2). However, this adenosine triphosphate (ATP) production pathway is not always predominant because oxidative phosphorylation remains active in many cancer cells (2). Recent studies have shown that cancer cells utilize glutamine and lipids not only as ATP sources but also as precursors of plasma membrane components and lipid droplets (LDs) (4,5). Regarding lipids, long-chain fatty acids (LCFAs) are energy sources and precursors of various phospholipids (5,6). Indeed, endogenously and exogenously supplied fatty acids (FAs) are sources for ATP production in cancer cells via oxidative phosphorylation (5,6). Although cholesterol is not an energy source, it is important for plasma membrane function, steroid hormone generation, and cellular signaling (6).

Several studies have suggested that lipid metabolism has potential as a therapeutic target in cancer treatment (4–6). A recent study further revealed that inhibition of ATP citrate lyase (ACLY), which is responsible for production of acetyl-CoA, a lipogenesis precursor, can overcome cancer immunotherapy resistance (7). Fatty acid synthase (FAS), which is generally responsible for lipogenesis, can contribute to many aspects of cancer progression (8). An intermediate compound of cholesterol biosynthesis, 7-dehydrocholesterol, can act as a natural inhibitor of ferroptosis in multiple cancer cells (9,10). Thus, the development of new therapeutic strategies targeting these various forms of lipid metabolism is ongoing.

Hypoxia is a general condition in tumors that limits the availability of not only molecular oxygen but also glucose, AAs, and/or lipids from the bloodstream depending on the tumor's distance from the vasculature (11,12) (Fig. 1). Deficiency of these elements in tumors is likely accelerated by the high catabolic demand of cancer cells and vascular immaturity. Therefore, adaptation to these harsh conditions is essential for cancer progression. However, the published relationships among hypoxia, glucose deficiency, AA deficiency, and lipid deficiency in cancer cells have been poorly integrated. A therapeutic strategy targeting these harsh tissue conditions may be beneficial because they are expected to be characteristic of tumors, and such therapy is thus likely to mitigate the toxic adverse effects associated with other therapeutic approaches. The objective of the present review is to summarize the latest understanding on this topic, with a particular focus on lipid metabolism. The review mainly aims to provide a comprehensive perspective on how cancer cells adapt to severe tumor conditions and to discuss possible future research directions and therapeutic applications from the perspective of lipid metabolism.

Figure 1. Gradient of blood component availability in tumor tissues based on distance from the bloodstream. Ovals of the same color represent cancer cell populations with identical hypoxia status. The necrosis area indicates regions where cancer cells are non-viable due to severe O2 and nutrient deprivation. The tumor tissues within open rectangular area represents regions abundant in HIF. HIF, hypoxia-inducible factor.

Adaptive response mechanisms to glucose deprivation

Relationship to hypoxia

Generally, cancer cells adapt to hypoxia and glucose deficiency without relying on lipid metabolism, primarily through the hypoxia-inducible factor (HIF) pathway (11,12) and the unfolded protein response (UPR) (13) (Fig. 2). The UPR involves endoplasmic reticulum (ER)-associated degradation of incorrectly folded proteins produced under the above stress conditions, preventing accumulation of toxic misfolded proteins followed by ER stress. However, expression of HIF1α, required for adaptation to hypoxia, is impaired in cancer cells cultured under low-glucose conditions (14). The combination of hypoxia and glucose deficiency (HGD) is cytotoxic to cancer cells. Indeed, recent studies have shown that HGD induces cancer cell death in association with CRE-binding protein (15) and overproduction of poly (ADP-ribose) polymer (16). However, cancer cells can synergistically respond to HGD and activate genes required for adaptation to this harsh condition (17).

Figure 2. Relationship between HIFs and UPR under HGD. Cancer cells adapt to HGD stress through the UPR, mediated by (1) multiple ER stress sensor molecules and transcriptional activation driven by (2) the HIF-ARNT complex. HIF expression can be both activated and suppressed via (3,4) mTOR. This process is regulated through (7,8) the LIMS-Akt and EZH2-PDH3 pathways (5) HIF-independent transcriptional activation also contributes to adaptation under HGD. HIFs cooperate with (6) XBP1 and (9) Grp78 to activate downstream genes essential for the adaptive response. Molecules involved in HGD-mimicking conditions are shown in magenta. Arrows and T-bars indicate activating and suppressive processes, respectively. ERAD, ER-associated degradation.

The common adaptive response mechanism in cancer cells exposed to HGD may involve both HIF pathway activation (18–20) and the UPR (13,15,16,19,20). Hypoxia induces the UPR through activation of the ER stress sensor molecules activating transcription factor 6, inositol-requiring protein 1 (IRE1), and PKR-like ER kinase (PERK) (1 in Fig. 2) (13,19), which helps cancer cells tolerate low-oxygen conditions. These mechanisms can be induced under hypoxia and/or glucose deficiency. The HIF pathway is mediated by hypoxia-inducible transcription factors HIF1α and HIF2α, which are mainly induced in mildly to moderately hypoxic tumor regions (Figs. 1 and 2), along with the constitutively expressed aryl hydrocarbon receptor nuclear translocator (ARNT) (2 in Fig. 2) (12).

Mammalian target of rapamycin (mTOR) plays a role in adaptation to HGD. mTOR complex 1 (mTORC1) promotes the translation of proteins, including HIFα proteins, to maintain energy homeostasis (3 in Fig. 2) (19,20). However, suppression of mTORC1 signaling can also be important for adapting to hypoxia by enabling appropriate mRNA translation and activating the UPR (19,20). mTOR can be inhibited through the HIF1α-REDD1 and HIF1α-BNIP3 pathways under hypoxia, suggesting a negative feedback loop between HIFα and mTOR (route 4 in Fig. 2) (19). Thus, HIFα, the UPR, and mTOR are interconnected under HGD (13,19).

Several studies have revealed the effects of HGD on cancer cell survival. Resistance to apoptosis induced under HGD is mediated by the serine/threonine kinase proviral integration site for Moloney murine leukemia virus 1 (PIM-1) in some cancer cells (21). Meanwhile, proline oxidase (POX) expression is increased under HGD in an AMP-activated protein kinase (AMPK)-dependent manner (22). Expression of both PIM-1 and POX is HIF-independent (5 in Fig. 2) (21,22). Survival of cancer cells exposed to HGD may be dependent on POX because proline oxidation results in ATP production under HGD (22). Thus, POX is a potential target of cancer therapy. HIF1α and the UPR cooperate to enhance the stemness of breast cancer cells via HIF1α binding to XBP1 under HGD (6 in Fig. 2) (23). HIF1α-driven expression of LIMS1 not only facilitates glucose uptake but also enhances HIF1α translation via the AKT-mTOR pathway in pancreatic cancer cells exposed to HGD (route 7 in Fig. 2), resulting in a cell survival advantage (24).

Recent studies have shown that GD generates an HGD-mimicking condition because even under normoxia, expression of HIF1α can be induced by GD to augment cancer cell survival (25–27). This induction of HIF1α expression occurs through EZH2-dependent suppression of PHD3 expression (8 in Fig. 2) (25). This simple GD-driven pseudo-HGD condition with HIF1α expression through inhibition of its degradation augments the aggressiveness of lung adenocarcinoma cells (25). GD under normoxia also increases HIF1α expression via ER stress-inducible molecular chaperone GRP78 in pancreatic cancer cells to augment chemoresistance (9 in Fig. 2) (26). The GRP78-HIF1α complex binds to the regulatory region of the HIF1A gene to promote transcription. Thus, GRP78 can induce HIF1α expression at the mRNA level (26). In summary, GD can enhance tumor hypoxia by upregulating HIF1α expression at both the protein and mRNA levels. Meanwhile, lysophosphatidic acid receptors were shown to contribute to chemoresistance in pancreatic cancer cell line PANC-1 under HGD (28). Expression of immune checkpoint receptor PD-1 and TIGIT can be synergistically increased in esophageal cancer cells under HGD and is responsible for immune tolerance (29). The roles of HIFs and the UPR were not examined in these two studies (28,29).

Relationship to lipid metabolism

Glucose uptake and subsequent catabolism are activated in cancer cells. Recent studies have shown that GD influences lipid metabolism in cancer cells to support their survival, although its relationship to hypoxia has not yet been established. Indeed, some glioma cells have these characteristics and become susceptible to GD in culture (30). GD in cancer cells is associated with multiple lipid metabolism pathways, as summarized in Table I and Fig. 2. GD together with serum deficiency was also reported to drive the use of extracellular glutamine and lactate in glycerophospholipid synthesis for biomembrane generation via oxaloacetate-phosphoenolpyruvate conversion (Fig. 3A) in lung cancer cells (31). Mitochondrial phosphoenolpyruvate carboxykinase (PEPCK-M) plays key roles in this process (Fig. 3A) (31). Pharmacological activation of AMPK, a cellular energy sensor molecule, activates fatty acid oxidation (FAO) to acquire ATP in Akt-transformed cells under GD (30) (1 in Fig. 3B). Collectively, these studies have revealed novel mechanisms for cancer cell adaptation to severe nutrient deficiency.

Figure 3. Localization of lipid metabolism pathways affected by GD (stop symbol) in cancer cells. Cancer cells adapt to GD through various lipid metabolism pathways. (A) Phospholipids can be synthesized de novo from glutamine and lactate in mitochondria under GD. The metabolic enzyme PEPCK-M plays a crucial role in this process by producing phosphoenolpyruvate via the TCA cycle. (B) AMPK can activate FAO (1). FAs generated through phospholipid lipolysis (2), LD breakdown (5,6), or uptake from the extracellular space (3) serve as energy sources via the FABP-FAO pathway (4). Lipogenesis mediated by FAS can be suppressed through glycogen digestion (7), while prostanoid synthesis may be activated (8). Exogenous monounsaturated FAs can enhance FA desaturation (9). The subcellular localizations of FA desaturation (cytoplasm, ER) are based on data from the Human Protein Atlas (https://www.proteinatlas.org), while the nuclear membrane localization of prostanoid synthesis is based on the information from a previous study (40). Dotted arrows and T-bars with numbers represent active processes in response to GD. Cellular events and biomolecules involved are shown in black, while enzymes and molecules critical for metabolic processes are highlighted in magenta. See the text for further details on these processes.

Table I. Reported relationships between glucose deprivation and lipid metabolism pathways associated with cancer progression.

Table I.

Reported relationships between glucose deprivation and lipid metabolism pathways associated with cancer progression.

Adaptive lipid metabolism	Key factor and mechanism responsible for adaptive lipid metabolism [reference]	Examined cancer cell (histological type) [reference]
FAO	AMPK activation (30), PLD1 (32), MEK5-ERK5 pathway activation (33), FABP7 (34), mTOR activation (35), lipophagy (35), CHKα2 (36), chaperone-mediated lipophagy (36), PIM1-GSK3β-PPARα pathway inactivation (37), PGM1 (38), FAS (38), CAF (42), oleic acid (42), autophagosome maturation (42), cancer stemness (42)	LN18, LN229 (brain) (30), MDA-MB-231 (breast) (32), MCF-7 (breast) (32), RCC4 (renal) (32), HCT116 (colon) (32), OCI-AML3 (lymphocyte) (33), BCL-P2 (lymphocyte) (33), HepG2 (liver) (33), HuH-7 (liver) (33), Primary cells (brain) (34), LN229 (brain) (35), GaMg (brain) (35), U87MG (brain) (35), A172 (brain) (35), HuH7 (liver) (36), U87 (brain) (36), GP06 (brain) (36), GP08 (brain) (36), PC3 (prostate) (37), PC3LN4 (prostate) (37), DU145 (prostate) (37), BGC-823 (gastric) (38), MKN-28 (gastric) (38), NCI-H460 (lung) (42)
Glycerophospholipid synthesis	PEPCK-M (31), glyceroneogenesis (31)	A549 (lung) (31), H23 (lung) (31)
Membrane lipid degradation	Autophagy (32), PLD1 (32)	MDA-MB-231 (32), MCF-7 (breast) (32), RCC4 (renal) (32), HCT116 (colon) (32)
FA transport	FABP7 (34)	Primary cells (brain) (34)
FA uptake	CD36 (33), LRP1 (33)	OCI-AML3 (lymphocyte) (33), BCL-P2 (lymphocyte) (33), HepG2 (liver) (33), HuH-7 (liver) (33)
LD catabolism	mTOR activation (35), lipophagy (35) CHKα2 (36), chaperone-mediated lipophagy (36), PIM1-GSK3β-PPARα pathway activation (37)	LN229 (brain) (35), GaMg (brain) (35), U87MG (brain) (35), A172 (brain) (35), HuH7 (liver) (36), U87 (brain) (36), GP06 (brain) (36), GP08 (brain) (36), PC3 (prostate) (37), PC3LN4 (prostate) (37), DU145 (prostate) (37)
Lipogenesis	PGM1 (38), glycogenolysis (38), FAS (38)	BGC-823 (gastric) (38), MKN-28 (gastric) (38)
Alteration of membrane lipid composition	Glycerophospholipids (31), phospholipids (phosphatidylethanolamine, cardiolipin, etc.) (39), cholesterol (39)	A549 (lung) (31), H23 (lung) (31), Caco-2 (colon) (39)
Prostaglandin E2 synthesis	UPR (41), AMPK activation (41), COX-2 (41)	HT29 (colon) (41), RG/C2 (colon) (41), AA/C1/SB/10C (colon) (41), SW480 (colon) (41)
Stearoyl-CoA desaturase expression	CAF (42), oleic acid supply (42), autophagy activation (42), F-actin polymerization-YAP nuclear translocation pathway (42), cancer stemness (42)	NCI-H460 (lung) (42)

[i] AMPK, AMP-activated protein kinase; PLD1, phospholipase D1; PIM1, proviral integration site for Moloney murine leukemia virus 1; GSK3β, glycogen synthase kinase 3β; PPARα, peroxisome proliferator-activated receptor α; PEPCK-M, phosphoenolpyruvate carboxykinase-M; FABP7, fatty acid binding protein 7; CD36, cluster of differentiation 36; LRP1, lipoprotein receptor-related protein 1; CHKα2, choline kinase α2; PGM1, phosphoglucomutase 1; FAS, fatty acid synthase; UPR, unfolded protein response; COX-2, cyclooxygenase-2; CAF, cancer-associated fibroblast.

Membrane phospholipids can be substrates for phospholipase D1-mediated autophagy in multiple cancer cell types under GD (32). FAs generated during this process (2 in Fig. 3B) can be used for FAO to sustain cell survival (32). In hepatocellular carcinoma and leukemia cells, GD activates MEK-ERK5 signaling to increase FA uptake (3 in Fig. 3B), followed by ATP generation through existing FAO activity (33). The importance of FAO under GD was also demonstrated in drug-resistant slow-cycling glioblastoma subpopulations (34). The same study showed that FA transport (4 in Fig. 3B) by fatty acid-binding protein (FABP)-7 is crucial for survival of mitochondria (oxidative phosphorylation)-active glioma cells (34). LD catabolism by lipophagy, followed by existing FAO activity (route 5 in Fig. 3B), also contributes to the survival of glucose-starved glioblastoma cells (35,36). This involves hyperactivation of mTOR (35) (Fig. 3B). Meanwhile, choline kinase 2 (CHKα2) is responsible for phosphorylation of LD-associated perilipins, finally resulting in chaperone-mediated autophagy-driven degradation of LDs to generate FAs (35) (route 5 in Fig. 3B). FAO under GD is also important for prostate cancer cells, in which LD accumulation via the PIM1-GSK3β-PPARα axis can be activated under nutrient stress conditions for FA generation (37) (route 6 in Fig. 3B).

Glycogenolysis serves as an alternative pathway for cellular glucose supply under GD. Phosphoglucomutase PGM1, a key enzyme for glycogenolysis followed by glycolysis, contributes to the viability of gastric cancer cells exposed to GD by suppressing lipogenesis (38) (7 in Fig. 3B). FAS can compensate for the reduced cell viability caused by PGM1 inhibition under GD conditions (38). Thus, combined inhibition of interconversion of the phosphate position within glucose molecules and FAS may be promising for gastric cancer treatment (38). A study using colon cancer cell line Caco-2 revealed that the composition of phospholipids, cholesterol, and unsaturated FAs in the cell membrane is considerably altered under GD, leading to metabolic adaptations that augment cell survival (39). GD also increases prostaglandin E2 synthesis at the nuclear membrane (40) (8 in Fig. 3B) by repressing 15-hydroxyprostaglandin dehydrogenase expression in colon cancer cells, resulting in a survival advantage for these cells (41). The stemness of lung cancer cells exposed to GD may be enhanced by oleic acid supplied by surrounding cancer-associated fibroblasts (CAFs), followed by stearoyl-CoA desaturase expression (9 in Fig. 3B), nuclear translocation of polymerized actin, and yes-associated protein (42). This process can be inhibited by treatment with an FAO inhibitor (42).

In summary, this section has revealed that various cancer cells generally circumvent GD through FAO (Table I). Additionally, the biosynthesis of glycerophospholipids and prostaglandins can contribute to their adaption to GD.

Adaptive response mechanisms to amino acid deprivation through lipid metabolism and their correlation with hypoxia

In addition to their role as protein components, AAs, such as glucose, can serve as major energy sources. Numerous studies have demonstrated the effects of AA deficiencies on cancer phenotypes, similar to those observed in GD. However, there is limited knowledge on their correlations with hypoxia and lipid metabolism. This section will primarily address this issue. Studies have generally demonstrated that AA deficiencies are associated with multiple lipid metabolism pathways (Table II) and hypoxia in cancer cells.

Table II. Reported relationships between amino acid deficiencies and lipid metabolism pathways in cancer cells.

Table II.

Reported relationships between amino acid deficiencies and lipid metabolism pathways in cancer cells.

Deprived amino acid	Responsive lipid metabolism	Key factor and mechanism responsible for adaptive lipid metabolism [reference]	Examined cancer cell line (histological type) [reference]
Glutamine	Suppression of lipogenesis	Loss of reductive carboxylationa (44), LD-lipophagy, followed by suppression of cholesterol synthesis (53)	PRC3 (kidney) (44), 786-O (kidney) (44), UMRC2 (kidney) (44), HepG2 (liver) (53), HuH6 (liver) (53), Huh7 (liver) (53)
	LPO	Impairment of glutathione synthesisa (46), ferroptosisa (46)	RCC-4 (kidney) (46), 786-O (kidney) (46)
	FAO activation	Sestrin2-mTORCs pathway activation (47), CHTM1 (48), HRD1 (51), CPT2 degradation (51), PI3K-C2γ pathway inactivation (52), glutamine auxotroph (52), AMPK-CHKα2-LD-ATGL pathway activation (50)	H358 (lung) (47), H1299 (lung) (47), H460 (Lung) (47), MCF-7 (breast) (48), RKO (colon) (48), UACC-62 (skin) (48), MDA-MB-231 (breast) (51), Capan1 (pancreas) (52), MiaPaca2 (pancreas) (52), Panc1 (pancreas) (52), H322 (lung) (50), H358 (lung) (50)
	Lipogenesis	CHTM1 (48), PKC-CREB-PGC-1α pathway activation (48), SREBP1-ACC1-LD pathway pathway activation (49), SREBP1-glutamine synthesis activation (49),	MCF-7 (breast) (48), RKO (colon) (48), UACC-62 (skin) (48), HepG2 (liver) (49)
	Lipolysis	AMPK-CHKα2-LD-ATGL pathway activation (50)	H322 (lung) (50), H358 (lung) (50)
	Maintenance of cellular lipid balance	TRIAP-1-p53 interaction (54), phospholipids and sterols homeostasis (54)	HCT-116 (colon) (54)
Serine (Glycine)	LPO inhibition	Glutathione synthesis (59), p53-p21 pathway activation (59)	HCT116 (colon) (59), RKO (colon) (59)
	FAO activation	Pyruvate transfer to the TCA cycle (59)	HCT116 (colon) (59), RKO (colon) (59)
	FAO suppression	Impairment of ceramide synthesisa (58), mitochondrial dysfunctiona (58)	HCT116 (colon) (58), HT29 (colon) (58)
	Deoxyceramide synthesis	Serine to alanine substitution within ceramide moleculea (60, 61)	HCT116 (colon) (60, 61), MOLT-4 (lymphocyte) (61), HEK293T (kidney) (61)
	Accumulation of cellular sphingosine	SK1 degradation (61), autonomous serine synthesis (61)	HCT116 (colon) (61), MOLT-4 (lymphocyte) (61), HEK293T (kidney) (61)
Cysteine	Suppression of per-oxidized lipid accumulation	Activation of FA metabolism (64), GPX4 expression (64), glutathione synthesis (67,68), NRF2-CBS pathway (67), macropinocytosis of albumin (68), iron storage (68, 69), ATM-MTF1-ferritin/FPN1 pathway (69), CISD3 (70), glutaminolysis (70), conversion of peroxidized lipid to lipid alcohol (71), anti-oxidant tryptophan metabolites (71)	HL60 (bone marrow) (64), MOLM13 (bone marrow) (64), SKOV3 (ovary) (67), OVCA429 (ovary) (67), HT-1080 (fibroblast) (68,69), A375 (skin) (68), T98G (brain) (68), U2OS (bone) (68), PaTu8988T (pancreas) (68), GS187 (brain) (68), MDA-MB-231 (breast) (69), RCC4 (kidney) (69), HEK293T (kidney) (69), HL60 (bone marrow) (70), 786-O (kidney) (71), AsPC-1 (pancreas) (71), CFPAC-1 (pancreas) (71), PANC-1 (pancreas) (71), U251 (brain) (71), Be2C, (neuroblast) (71)
	Lipid peroxidation	Suppression of glutathione synthesisa (46,64), ferroptosisa (46,64)	RCC-4 (kidney) (46), 786-O (kidney) (46), HL60 (bone marrow) (64), MOLM13 (bone marrow) (64)
Arginine	Suppression of FAO	Arginine auxotroph (73), impairment of mitochondria functiona (73), cytotoxic autophagya (73)	MDA-MB-231 (breast) (73), T47-D (breast) (73)
		LD accumulation (73)
	Lipid peroxidation	Arginine auxotroph (74), inhibition of mTOR-SREBP1-SCD5 pathwaya (74), ferroptosisa (74)	H1299 (lung) (74), HCC827 (lung) (74)
	Suppression of lipogenesis	Arginine auxotroph (75,76), activation of MEK-ERK-cMyc-ASS1 pathway (75), reduction of ACLY (76), ACC1 (76), and FAS (76) synthesis	SKLMS-1 (vulva) (75), A2058 (skin) (76), SK-Mel-2 (skin) (76)
Methionine	Phospholipid metabolism	Increase of cellular glycerophospholipids (78,79).	B16 (mouse skin) (78), HepG2 (liver) (79)
		Decrease of choline (78) and phosphatidylcholine (79).
	Impairment of cholesterol biosynthesis, facilitation of cholesterol excretion	Suppression of SREBP2-FOXM1 axisa (80), inhibition of S-adenosylmethionine synthesisa (80)	In-house human glioma initiating cell lines (brain) (80)
	Lipid peroxidation	Suppression of glutathione synthesisa (65), ferroptosisa (65)	MG1-4 (mouse brain) (65), TS543 (brain) (65), KNS42 (brain) (65)

a Toxic events for cancer progression. LPO, lipid peroxidation; CHTM1, coiled-coil helix tumor and metabolism 1; HRD1, HMG-CoA reductase degradation protein 1; CPT2, carnitine palmitoyltransferase 2; PI3K-C2γ, phosphoinositide 3-kinase-C2γ; PKC, protein kinase C; CREB, CRE-binding protein; PGC-1α, peroxisome proliferator-activated receptor γ coactivator-1α; SREBP1, sterol regulatory element binding protein-1; ACC1, acetyl-CoA carboxylase 1; AMPK, AMP-activated protein kinase; CHKα2, choline kinase α2; ATGL, adipose triglyceride lipase; TRIAP-1, TP53-regulated inhibitor of apoptosis 1; SK1, sphingosine kinase 1; GPX4, glutathione peroxidase 4; NRF2, nuclear factor-erythroid 2-related factor-2; CBS, cystathionine β-synthase; ATM, ataxia-telangiectasia mutated; MTF1, metal regulatory transcription factor 1; FPN1, ferroportin; CISD3, CDGSH iron sulfur domain 3; SCD5, stearoyl-CoA desaturase 5; c-Myc, MYC proto-oncogene, bHLH transcription factor; ASS1, argininosuccinate synthetase 1; ACLY, ATP citrate lyase; FAS, fatty acid synthase; SREBP2, sterol regulatory element binding protein 2; FOXM1, forkhead box M1.

Glutamine deprivation

Glutamine is another primary nutrient for cancer cells in addition to glucose. The levels of non-essential AAs, including glutamine, in pancreatic cancer tissues are considerably lower than those in adjacent benign tissues (43). In cells with an inactivated von Hippel Lindau (VHL) gene under hypoxia, glutamine is used to generate α-ketoglutarate, which is then used to produce the citrate required for de novo LCFA synthesis through reductive carboxylation (44,45) (route 1 in Fig. 4A) and the glutathione (GSH) synthesis (2 in Fig. 4A) necessary for suppression of lipid peroxide accumulation, which is responsible for ferroptosis (46). Thus, clear cell renal cell carcinoma (ccRCC) cells lacking functional VHL are susceptible to glutamine deficiency (GlnD) caused by glutaminase inhibition (44) or glutamine withdrawal from the culture medium (45) (Table II).

Figure 4. Localization of metabolic networks in cancer cells affected by (A) GlnD, (B) SD, (C) CD and MD, and (D) AD. (A) GlnD induces both anabolism and catabolism of LD. FAs generated through lipolysis can serve as energy sources. Cellular cholesterol levels may be suppressed in response to GlnD to maintain lipid homeostasis. (B) SD induces GSH synthesis from glycine while suppressing purine synthesis via p53. Toxic deoxysphingolipids may be produced under SD, but this toxicity can be mitigated by autonomously synthesized serine, associated with elevated PHGDH expression. 3-PG generated during this process can serve as an energy source via FAO. (C) CD activates anti-ferroptosis mechanisms by promoting GSH synthesis and inhibiting the accumulation of toxic peroxidized lipids. MD induces gene demethylation and contributes to resistance to MD stress through glycerophospholipid synthesis. (D) AD promotes autonomous arginine synthesis through overexpression of the ASS1 gene. This adaptation mechanism involves inhibition of lipogenesis and activation of glycolysis and glutaminolysis, rather than FAO. Arrows and T-bars represent activating and suppressive processes, respectively. Dotted symbols with numbers designate active processes in response to AA deficiency (stop symbols). See the text for additional details. Metabolic enzymes are highlighted in magenta.

Cancer cells can adapt to stressful GlnD conditions through multiple metabolic mechanisms. A recent study using lung cancer cell lines showed that differential regulation of mTORC1 and mTORC2 via Sestrin2, followed by FAO, contributes to cell survival under GlnD (47) (Table II). Under both GD and GlnD, mitochondrial protein coiled-coil helix tumor and metabolism 1 can facilitate LCFA synthesis and FAO via LD formation in multiple cancer cells (48) (routes 3 and 4 in Fig. 4A and Table II). GlnD likely activates the SREBF1 gene, which encodes the lipogenic enzyme SREBP1 (49). SREBF1 activation, mediated by O-linked N-acetylglucosaminylated transcription factor specificity protein 1 (Sp1) and followed by acetyl-CoA carboxylase (ACC) expression, contributes to LD biosynthesis in various cancer cells (49) (route 3 in Fig. 4A). Similar to the previously described glioblastoma cell response to GD (35), CHKα2 plays a key role in LD lipolysis in lung cancer cells exposed to GlnD (50) (route 4 in Fig. 4A and Table II). In this context, neutral lipolysis and lipophagy likely contribute to LD catabolism (route 4 in Fig. 4A). Notably, the same study demonstrated that phosphorylation of CHKα2 at S279, which is involved in neutral lipolysis, is associated with a worse prognosis in patients with non-small-cell lung cancer (50).

HMG-CoA reductase degradation protein 1 (HRD1) was found to suppress the proliferation of breast cancer cell line MDA-MB-231 through inhibition of FAO (51). HRD1 can interact and ubiquitinate FA transporter protein carnitine palmitoyltransferase 2 (CPT2) (5 in Fig. 4A), resulting in blockade of mitochondrial LCFA transport through its degradation (51). Under GlnD, this mechanism can be abolished to activate FAO, resulting in resistance to glutaminase inhibitors. Thus, simultaneous inhibition of FAO and glutamate production may be therapeutically promising, and high HRD1 expression potentially predicts better efficacy of glutaminase inhibitors (51).

PI3K-C2γ expression is inactivated to activate mTOR in cancer cells from pancreatic tumor tissues. These cells depend on exogenous glutamine to activate lipogenesis (52) (6 in Fig. 4A). Thus, treatment with mTOR and glutaminase inhibitors may be therapeutically beneficial. GlnD induces lipophagy-driven LD degradation to increase cellular cholesterol levels in hepatocellular carcinoma cells (53) (7 in Fig. 4A). Cholesterol inhibits SREBP2 maturation, leading to decreased cholesterol synthesis, which helps maintain redox balance and provides a survival advantage to cancer cells under GlnD conditions (53). Interaction between p53 and TP53-regulated inhibitor of apoptosis 1 (TRIAP1) may also be important for survival of colon cancer cells under GlnD (54) (Table II). TRIAP1 affects lipid homeostasis through multiple lipids, including glycerolipids, sphingolipids, and cholesterols, in HCT116 cells (Table II). The TRIAP1-p53 interaction is important for glutamine metabolism. Indeed, under GlnD, p53 can compensate for TRIAP1 function to overcome metabolic stress (54). Collectively, GlnD can couple to multiple lipid metabolism pathways (Table II). Cancer cells likely acquire ATP through the activation of FAO via LD metabolism under this nutrient stress (Fig. 4A). Maintaining cellular lipid homeostasis is also important (Fig. 4A).

GlnD associated with hypoxia is known to trigger expression of UPR-executor transcription factor ATF4 (19,20) in glioblastoma cells (55). This UPR-mediated stress response is responsible for resistance to temozolomide treatment (55). Hypoxia with GlnD can also promote cancer progression through epigenetic mechanisms. In melanoma tissue, the glutamine concentration in the hypoxic tumor core region is considerably lower than that in the tumor periphery (56). Hypoxia with GlnD can cause hypermethylation of histones through inhibition of demethylation and promote cancer cell dedifferentiation, resulting in resistance to BRAF inhibitor treatment (56). The implications for lipid metabolism involvement in these hypoxia- and GlnD-associated features observed in tumor tissues remain to be determined.

Serine and glycine deprivation

Serine and glycine can be enzymatically interconverted through a single reaction mediated by serine hydroxymethyltransferase, involving the conversion of tetrahydrofolate to its methylated form (1 in Fig. 4B), and they contribute to common metabolic pathways such as GSH synthesis (57,58) (2 in Fig. 4B). Thus, studies have tended to consider the effects of these AAs together (Fig. 4B, Table II).

Studies using colon cancer cell line HCT116 showed that serine/glycine deficiency (SGD) can suppress FAO through impaired mitochondrial function (58). Specifically, the ceramide level is decreased in serine-depleted cells (3 in Fig. 4B), leading to mitochondrial dysfunction (58). Thus, restriction of the serine supply is promising in treatment of p53-deficient cancers. However, SGD causes metabolic reprogramming with increased oxidative phosphorylation activity (59). p53-dependent synthesis of GSH with suppression of purine synthesis (4 in Fig. 4B), followed by scavenging of reactive oxygen species, can contribute to resistance of cancer cells against this metabolic stress (59). This process is accompanied by an increase in pyruvate transfer via 3-phosphoglycerate (3-PG) to the mitochondria, which activates FAO (route 5 in Fig. 4B). Furthermore, SGD facilitates the biosynthesis of toxic deoxysphingolipid, in which serine residues are substituted with alanine residues (6 in Fig. 4B), in cancer cells (60). Thus, dietary restriction and pharmacological targeting of the serine supply pathway can lead to tumor regression. This effect was prominent for growth of alanine-rich spheroid cancer cells (60). A more recent study further demonstrated that deoxysphingolipid (deoxysphinganine) generation is non-toxic and important for adaptation of cancer cells to SGD (61). This response mechanism involves accumulation of sphingosine in cancer cells due to the proteasomal degradation of sphingosine kinase 1 (7 in Fig. 4B), leading to increased expression of phosphoglycerate dehydrogenase (PHGDH) and subsequent de novo serine synthesis (route 8 in Fig. 4B) to overcome SGD (61). In summary, cancer cells can adapt to serine deficiency through inhibition of lipid peroxidation, activation of FAO, and modulation of sphingolipid biosynthesis.

Regarding the relationship between SGD and hypoxia, glioblastoma cells were reported to activate the AMPK-HIF1α pathway and induce a pseudo-hypoxia condition under SGD to bypass this metabolic stress (62). The effects of lipid metabolism involvement in this adaptation mechanism remain to be elucidated.

Cysteine deprivation

The major source of cellular cysteine is dietary cystine, which is metabolically associated with glutamine (63). Cysteine is also produced by de novo synthesis from methionine (63). The major effect of cysteine deficiency (CD) on cancer cells is impaired GSH synthesis, resulting in ferroptosis through accumulation of toxic peroxidized phospholipids (Fig. 4C, Table II) (46,64). Thus, CD may be therapeutically beneficial (46,64–66).

The mechanisms underlying how cancer cells circumvent CD fall into a single category (Table II): ferroptosis resistance through suppression of peroxidized lipid accumulation. Multiple pathways may be involved in this process in various cancer cells (Table II). For example, acute myeloid leukemia cells are auxotrophic for cysteine and can acquire CD resistance through overexpression of glutathione peroxidase 4 (1 in Fig. 4C) and microsomal glutathione-S-transferase 1, which may be associated with activation of FA metabolism (64) (Table II). In VHL-defective ccRCC cells, generation of peroxidized lipids is suppressed by FAO inhibition and GSH synthesis (46). In ovarian cancer cells, transcription factor NRF2 enhances cystathionine β-synthase (CBS) gene expression and activates autonomous synthesis of cysteine (route 2 in Fig. 4C), resulting in resistance to CD-induced ferroptosis through GSH synthesis (route 3 in Fig. 4C) (67). A recent study showed that albumin can be an extracellular source of cysteine under CD (68). Albumin incorporated into cancer cells via macro-pinocytosis in association with mTOR inhibition undergoes lysosomal degradation (68). Cysteine released into the cytoplasm also contributes to GSH synthesis (4 and route 3 in Fig. 4C), thereby preventing ferroptosis of cancer cells (68). This mechanism is especially important under spheroid culture conditions associated with tumor-like stress conditions (68).

Deprivation of cellular free iron ions is an alternative mechanism of ferroptosis resistance. Expression of genes involved in cellular iron storage can be increased via the ATM-MTF1 axis under CD. As a result, the balance between iron storage and release is expected to shift toward storage (5 in Fig. 4C), leading to ferroptosis resistance in multiple cancer cells (69). Expression of iron-sulfur cluster protein CISD3 is also increased in cancer cells and contributes to iron storage under CD, followed by ferroptosis resistance (70).

Tryptophane metabolites contribute to ferroptosis resistance under CD through a detoxification mechanism. Specifically, serotonin and 3-hydroxy-anthranilic acid produced by kynureninase act as radical trapping agents and reduce peroxidized lipids to non-toxic lipid alcohols (71) (6 in Fig. 4C). Overall, cancer cells utilize multiple molecular defense mechanisms against CD-driven ferroptosis (Table II).

In the context of CD and hypoxia, CD-induced death of MDA-MB-231 cells can be alleviated by hypoxia through inhibition of ATF4 expression (72). Thus, hypoxia may reduce CD-mediated cytotoxicity in certain cancer cells.

Arginine deprivation

Cancer cells are also sensitive to arginine deficiency (AD), and this vulnerability is often associated with lipid metabolism (Table II). Expression of argininosuccinate synthetase 1 (ASS1), an enzyme in the urea cycle (Fig. 4D) that also catalyzes arginine biosynthesis, is low in >60% of clinical breast cancer samples (73). Thus, many cancer cells are arginine auxotrophs (Table II). AD induces cytotoxic autophagy in breast cancer cells, leading to cell death due to impaired mitochondrial function (Table II). In such cases, FAO suppression is followed by a metabolic shift to increase LDs (73) (route 1 in Fig. 4D). Meanwhile, ASS1-high non-small-cell lung cancer cells can confer ferroptosis resistance through monounsaturated FA synthesis. To facilitate this process, ASS1-driven synthesis of arginine activates the mTOR-SREBP1-SCD5 pathway to cause unsaturation of autonomously synthesized FAs, leading to inhibition of peroxidized lipid generation (74). This mechanism provides additional insight into why arginine auxotroph lung cancer cells are susceptible to AD-induced cell death. These observations suggest that arginine-deficient diets may be a therapeutic strategy through induction of ferroptosis. However, cancer cells can circumvent this stress condition. For example, AD can activate the MEK-ERK signaling pathway (route 2 in Fig. 4D) to induce c-Myc-Max transcription factor-driven ASS1 gene activation (3 in Fig. 4D) in ASS1-negative vulvar leiomyosarcoma (SKLMS-1) cells (75) (Table II). This cellular response causes adaptive metabolic reprogramming, including reduced ACLY expression, followed by suppression of de novo FA synthesis (75) (4 in Fig. 4D). Moreover, reports have described resistance to pharmacological AD in melanoma cells with ASS1 overexpression. In these cells, de novo lipogenesis is also inhibited through suppression of ACLY (Fig. 4D), acetyl-CoA carboxylase 1 (ACC1 in Fig. 4D), and FAS (Fig. 4D) to promote the Warburg effect (76). In this scenario, c-Myc contributes to AD resistance not only through ASS1 expression but also by enhancing glycolysis and glutaminolysis (5 in Fig. 4D) via the expression of glucose transporter 1 and glutaminase, respectively. Collectively, cancer cells can evade the effects of AD by autonomously synthesizing arginine, with the Warburg effect, rather than FAO, likely playing a key role in supporting cell survival under AD.

Under hypoxia, pharmacological AD inhibits expression of HIFs, inducible nitric oxide synthase, and ASS1 in HCT116 cells to inhibit tumor growth (77). This effect is associated with UPR induction. ASS1-deficient bladder cancer cell line UMUC3 shows greater sensitivity to hypoxia under AD (77). Thus, hypoxic cancer cells are vulnerable to AD.

Methionine deprivation

Deprivation of methionine, an essential AA, from culture medium likely affects lipid metabolism in cancer cells (Fig. 4C, Table II). Methionine deprivation (MD) is linked to GSH synthesis through the methionine cycle and cysteine synthesis pathway (routes 2 and 3 in Fig. 4C). Consequently, MD can impair GSH synthesis (65), leading to ferroptosis, similar to the effect of CD. However, acute myeloid leukemia cells are cysteine auxotrophs as indicated by the fact that methionine supplementation does not rescue them from CD-driven ferroptosis (64). MD also affects cellular lipids by increasing phosphorylethanolamine and decreasing choline (Table II), thereby contributing to a synergistic therapeutic effect with chloroethylnitrosourea treatment (78). Meanwhile, methionine is a precursor of S-adenosylmethionine (7 in Fig. 4C), a key methyl donor for biomolecules. Thus, MD affects methylation-driven lipid metabolism pathways. For example, MD causes S-adenosylmethionine deficiency in HepG2 cells and enhances glycerophosphocholine synthesis through demethylation of CpGs within the PNPLA7 promoter region (79) (8 in Fig. 4C). This adaptation mechanism potentially promotes cancer progression via activation of mitochondrial oxidative phosphorylation (79). MD may also be therapeutically promising for glioma-initiating cells because it impairs cholesterol synthesis and increases cholesterol excretion, thereby suppressing key glioma-initiating cell functions (80).

Adaptive response mechanisms to simultaneous deprivation of O2 and lipids

Cancer cells can utilize FAs obtained through lipogenesis and uptake from extracellular spaces under glucose and AA deficiency. However, the exogenous supply of lipids is restricted in cancer cells within poorly vascularized tumor tissues. This section will discuss recently revealed adaptive response mechanisms to restricted supply of both lipids and molecular oxygen, although the observations are currently limited to certain cancer types.

Adaptive responses to LCFA starvation and hypoxia in cancer cells

In general, tumor tissues are poorly vascularized. Thus, many blood components, including lipids, are poorly supplied. Impairment of lipogenesis may also enhance lipid insufficiency in cancer cells exposed to hypoxia with limited exogenous lipid supply. The effect of simultaneous deprivation of lipids and molecular oxygen on cancer cells was first demonstrated by experiments examining how serum starvation and hypoxia (SSH) affects their phenotype in relation to mTOR activity (81). The same study using mouse embryonic fibroblast cells and various cancer cells, including kidney cancer cells, showed that mTOR activation under SSH augments ER stress, resulting in UPR-mediated cell death (Table III) (81). This is likely due to abnormal ER expansion induced by increased protein synthesis and insufficient unsaturated LCFA supply because cell death was alleviated by supplementation of albumin-conjugated oleic acid (81).

Table III. Reported effects of hypoxia and lipid starvation on cancer cell response.

Table III.

Reported effects of hypoxia and lipid starvation on cancer cell response.

Deprived lipids	Related cellular events [reference]	Examined cancer cells (histological origin) [reference]
Exogenous FA	ER expansiona (81), UPR-induced cell deatha (81)	MCF7 (breast) (81), RCC10 (kidney) (81), U251 (brain) (81), RT4 (bladder) (81), A498 (kidney) (81)
	UPR (93), ICAM-1-driven resistance to apoptosis (95)	OVSAYO (ovary) (93,95), OVISE (ovary) (93,95)
	Neutral lipase-mediated LD catabolism (88), oleate-mediated suppression of lipotoxicity (88)	A498 (kidney) (88)
	Lipophagy-mediated LD catabolism (98)	OVSAYO (ovary) (98), OVISE (ovary) (98)
	EMT (96)	OVSAYO (ovary) (96), OVISE (ovary) (96)
Endogenous FA	Impairment of NAD+ productiona (82), induction of lipid auxotroph through inhibition of lipogenesis (82)	Hela (uterus) (82)
Cholesterol	Secretion of procoagulant extracellular vesicles (99)	OVSAYO (ovary) (99), OVISE (ovary) (99)
	SREBP1-driven lipogenic gene expression (100), apoptosis (100), impairment of spheroid growth (100)	U87 (brain) (100), U251 (brain) (100)
	SREBP2-driven ACSS2 gene expression (101), palmitate synthesis (101), phospholipid synthesis (101)	BT474 (breast) (101), DU145 (prostate) (101)

a Toxic events for cancer progression. ER, endoplasmic reticulum; FA, fatty acid; UPR, unfolded protein response; ICAM-1, intercellular adhesion molecule-1; LD, lipid droplet; EMT, epithelial-mesenchymal transition; NAD, nicotinamide adenine dinucleotide; SREBP, sterol regulatory element binding protein.

Hypoxia and delipidated serum cell culture conditions, characterized by poor supply of molecular oxygen as an electron acceptor and poor supply of extracellular lipids, was also shown to reduce regeneration of NAD+, a cofactor required for lipogenic citrate production in cancer cells, followed by cell proliferation (82). Thus, cancer cells exposed to SSH are likely to become lipid auxotrophs (Table III). However, NAD+-independent metabolism of exogenous acetate to acetyl-CoA followed by lipogenesis in cancer cells can rescue this auxotrophy (82).

ccRCC is a histological subtype of most kidney cancers (70–80%). Most of these cancer cells lack VHL function, resulting in constitutive HIF expression. Consequently, these cancer cells exhibit hypoxia-mimicking phenotypes (83,84). The phenotypes of ccRCC cells, such as ER homeostasis (83), motility (85), invasiveness (85), and anti-apoptosis (84), are highly dependent on LD biogenesis rather than LD catabolism (83–85). The kidney is a well-perfused organ (85), and studies on this cancer type under true hypoxic conditions are limited because ccRCC cells express HIF even in normoxic environments. However, hypoxia is a general tumor condition. Indeed, hypoxic regions exist within the normal renal medulla (86) and renal tumors (87), and adaptation of cancer cells to SSH may contribute to kidney cancer progression (Table III).

The HIF2α-perilipin 2 pathway contributes to ER homeostasis-mediated survival of ccRCC cells under SSH (1 in Fig. 5 and Table III) (83). LD catabolism also contributes to ccRCC cell phenotypes under LCFA starvation and hypoxia. LDs in cancer cells undergo hormone-sensitive lipase-driven degradation under SSH to maintain the cellular unsaturated FA (oleic acid) level, thereby suppressing the effect of toxic saturated FAs synthesized under hypoxia (2 in Fig. 5 and Table III) (88). Thus, the roles of LDs in malignancy are context-dependent.

Figure 5. Overview of lipid starvation and hypoxia-driven cancer cell phenotypes. Cancer cells adapt to lipid (LCFA and cholesterol) deprivation and hypoxia through various resistance mechanisms. These malignant phenotypes are mediated by lipolysis and lipogenesis. Transcription factors such as HIFs, ARNT, SREBPs, NFκB, and Sp1, along with cofactors like GILZ, Sam68, and hTERT, play key roles in these processes. Handshake symbols between hypoxia and orange or blue lightning symbols represent LCFA starvation + hypoxia and cholesterol starvation + hypoxia, respectively. Plain arrows, dashed arrows, and T-bars indicate activation processes, protein expression, and inhibition processes, respectively. See the text for further details on the numbered items. Orange and blue symbols correspond to processes induced by LCFA and cholesterol starvation, respectively. Bold orange and blue arrows represent phenotype expressions resulting from LCFA and cholesterol starvation, respectively. EMT, epithelial-mesenchymal transition; PLIN2, perilipin 2.

Ovarian clear cell carcinoma (OCCC) has morphological and biological similarities to ccRCC (89). As described above, ccRCC cells exhibit hypoxia-mimicking phenotypes. This characteristic may also be true for OCCC cells because the HIF pathway is more active in this cancer subtype than in other histological subtypes of epithelial ovarian cancer (90–92).

The effects of SSH on gene expression and phenotypes of OCCC cells have been examined, revealing that multiple genes can be synergistically activated in certain OCCC cells exposed to SSH stress. We first discovered this phenomenon for the FVII gene (93) (3 in Fig. 5), which is responsible for initiation of the physiological blood coagulation cascade and a potential contributor to cancer-associated thromboembolism (Fig. 5 and Table III) (94). Unlike typical hypoxia response genes, such as VEGF, this transcriptional activation involves an Sp1-HIF2α interaction on the FVII gene promoter (Fig. 5) (93). Subsequent studies revealed that multiple genes, including ICAM1 (95) and CD69 (96), exhibit the same SSH-driven transcriptional activation (Fig. 5) and have much higher synergism than the FVII gene (95,96). We found that unlike hypoxia alone and serum starvation alone, SSH has UPR involvement (Table III) (93). However, the UPR does not contribute to the synergistic expression of FVII and ICAM1 (93,95).

Albumin serves as a major LCFA transporter in the blood and is important for LCFA uptake by cells (97). Addition of albumin-LCFA complex was found to abolish the SSH-driven ICAM1 and CD69 expression, suggesting that LCFA starvation is responsible for the synergistic transcriptional activation under hypoxia (95,96) (4 in Fig. 5). Indeed, addition of low-density lipoproteins, including LCFAs and cholesterol as their esterified form, also abolished the SSH-driven ICAM1 expression (4 in Fig. 5), whereas addition of cholesterol alone did not (98). mTOR is involved in this ICAM1 expression via NFκB (5 in Fig. 5) (95). Further studies revealed that lipophagy is induced under SSH in OCCC cells and is responsible for the synergistic ICAM1 and CD69 expression (6 in Fig. 5) (96,98). Lipophagy enhances binding of pro-inflammatory transcription factor NFκB to the ICAM1 promoter region via Sam68 and hTERT (7 in Fig. 5) (98). Currently, ICAM-1 is considered to suppress the lipotoxicity-mediated apoptosis induced by lipophagy-driven LD degradation (8 and 9 in Fig. 5 and Table III) (98). Meanwhile, CD69 causes epithelial-mesenchymal transition in a fibronectin-dependent manner (Fig. 5 and Table III), leading to OCCC cell survival in vitro and in vivo (96).

Adaptive response mechanisms to cholesterol starvation and hypoxia in cancer cells

The effect of SSH on synergistic transcriptional activation in OCCC cells is mediated not only by LCFA starvation but also by cholesterol deficiency. The synergistic FVII gene expression under SSH is mediated through activation of the mTOR-SREBP1 axis (10 in Fig. 5) under cholesterol deprivation (11 in Fig. 5) (99). SREBP1 and HIF1α-ARNT complex indirectly promotes FVII expression through transcriptional activation of glucocorticoid-induced leucine zipper (GILZ) protein (12 in Fig. 5) to generate procoagulant extracellular vesicles, which are potentially responsible for cancer-associated thromboembolism (Fig. 5 and Table III) (94,99). It is noteworthy that GILZ suppresses SSH-driven ICAM1 expression because this anti-inflammatory transcriptional regulator binds and inhibits NFkB (13 in Fig. 5) (99).

Glioblastoma cells have been found to exhibit synergistic gene expression mediated by SREBP under both hypoxia and lipoprotein-deficient medium conditions (100). These SSH conditions synergistically enhance transcriptional activation of the lipogenic stearoyl-CoA desaturase, FABP-3, and FABP-4 genes (14 in Fig. 5) to suppress apoptosis and promote spheroid growth (Fig. 5 and Table III). An SREBP-dependent gene signature involving these genes predicts a poor survival rate for patients with glioblastoma (100). Lipogenesis via acetate also supports the survival of cancer cells under SSH, similar to the effect observed with exogenous acetate supply in HeLa cells (82,101). In this process, SREBP2 activates ACSS2 gene expression to enhance acetyl-CoA production (15 in Fig. 5 and Table III) (101). Additionally, HIFs may contribute to this mechanism because ARNT can upregulate ACSS expression under SSH (Fig. 5 and Table III) (101).

Conclusion and perspectives

In recent years, a wealth of knowledge regarding cancer cell metabolism has been accumulated, leading to the proposal of various therapeutic approaches. However, the current understanding of the combined effects of hypoxia and nutrient deprivation on cancer cell phenotypes remains insufficient, and further research is needed to enhance clinical applications. The conclusion of this review is that cancer cells can adapt to severe oxygenation and nutrient supply conditions through diverse lipid metabolism pathways. Activation of these pathways results in increased malignant phenotypes, including stemness, drug resistance, immune tolerance, and resistance to apoptosis. Thus, in addition to the accumulated information on metabolic reprogramming in cancer, we propose that a detailed understanding of the adaptation mechanisms to both hypoxia and various nutrient starvation conditions provides a platform for exploring promising therapeutic strategies targeting finely reprogrammed metabolisms in cancer. For instance, the suppression of lipid peroxidation correlates with an insufficient supply of multiple AAs, including cysteine, suggesting a potential pro-ferroptosis strategy for treating cancers prone to cysteine starvation. Furthermore, cancer cells rely on multiple lipid metabolism pathways to adapt to GlnD. Thus, combination therapy targeting glutamine supply and lipid metabolism may be promising. Additionally, therapeutic approaches targeting both severe hypoxia and tissue nutrient insufficiency may be beneficial because these harsh tissue conditions are characteristic of tumors. Specific targeting of these tissue conditions is expected to avoid the unwanted adverse effects associated with therapeutic strategies that target hypoxia alone.

Given the contents of the present review, knowledge of the effects of simultaneous deprivation of molecular oxygen and AAs on lipid metabolism in cancer cells has been scarce. Therefore, we propose three new research directions: identification of unaddressed effects caused by depletion of both AAs and lipids, which accelerate malignancy; exploration of cancer types that are highly dependent on lipid metabolism when molecular oxygen and/or nutrients are in poor supply; and identification of biomarkers for such cancer cells and types, which may be vulnerable to lipid metabolism-targeted therapy. Therefore, this preclinical research field currently leaves the door open for greater understanding of tumor biology.

In summary, we have presented a new viewpoint that cancer cells can bypass both hypoxia and nutrient-poor tumor conditions through various types of lipid metabolism. We expect the information in this review to offer insights into how cancer cells adapt to harsh hypoxia and nutrient-deficient conditions, which partially mimic complex tumor microenvironments. Combined with previous studies and future experimental investigations, these efforts are likely to improve the development of new treatment approaches for aggressive cancers, including the identification of novel diagnostic markers and generation of new therapeutic strategies targeting metabolic pathways characteristic to cancer cells exposed to simultaneous insufficiency of molecular oxygen, AAs, and/or lipids.

Acknowledgements

The authors would like to thank Dr Alison Sherwin for editing a draft of this manuscript.

Funding

Funding: No funding was received.

Availability of data and materials

Not applicable.

Authors' contributions

SK conceived and wrote the manuscript. YM contributed to drafting the manuscript. Data authentication is not applicable. Both authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

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Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

References