The most common method of spatial metabolomics combined multi-omics analysis is spatial metabolomics combined transcriptomics (35). Transcriptome sequencing (36) yields a large number of differential genes and numerous regulatory networks in tissue samples, but it poses difficulties to identify critical pathways or key genes controlling them. Metabolites are the final embodiment of life activities, and the small changes in phenotypic traits will be amplified exponentially at the metabolic level. Therefore, we can use the metabolome to reflect the altered phenotype (37), but the separate metabolome detection cannot explain the genetic variation that affects the phenotypic changes. In short, spatial metabolomics combined with transcriptomics can be visually drawn from the gene transcript products (RNA) to various metabolites in the inferior metabolic pathways and can obtain the data of the spatial information simultaneously, thus exploring biological problems from two levels of both “cause” and “effect” and verifying each other. Generally speaking, spatial metabolomics combined with transcriptomics helps us to screen out key genes, marker metabolites, and regulatory metabolic pathways from the huge amount of detected data, deeply analyze the growth and development process of biological systems, and explain the complexity and integrity of biological processes. It provides important technical support in the study of tumorigenesis mechanism, metabolic reprogramming, and early diagnosis, and also proposes key ideas to solve the problem of studying the spatial and temporal heterogeneity of tumors.

D.H.Heiland et al. have deeply characterized glioblastoma in human neocortex slice models with the help of spatial transcriptomics, metabolomics and proteomics, and single-cell RNA sequencing (scRNA-seq) (38). By exploring the unique and common transcriptional programs between patients, they have inferred that glioblastoma develops along a defined neural lineage and adapts to inflammatory or metabolic stimuli, which makes them reminiscent of the reactive transformation of mature astrocytes. Metabolomics analysis and mass spectrometry imaging techniques combined analysis supports the hypothesis that tumor heterogeneity is determined by changes in the microenvironment. This suggests that glioblastoma cells adopt a transcriptional program that is similar to the inflammatory transformation of astrocytes, indicating that the overall changes in local native tumors and the tumor microenvironment collectively shape its transcriptional heterogeneity. It points out the forward directions for further studies of the metabolic reprogramming of glioma cells, and also provides critical data for adjusting the currently popular glioblastoma models.

Such problems often exist in actual scientific research processes, such as many genes and proteins are obtained after the differential comparative analysis, but their function seems to be not directly related to the phenotypic differences. Therefore, the significance of spatial metabolomics combined with multi-omics analysis is also reflected here. For the hot topics of the research field, a great number of reports have revealed the direct association of phenotypes with some key metabolites. And for emerging themes of the research fields, combined multi-omics analysis can provide a more panoramic network of molecular associations. Ultimately, among all the various differential omics molecules, the interrelated functional metabolites or pathways are selected for key discussion and research, to provide more directional guidance for the pioneers in this field.

Lipids are important biomolecules that play multiple roles in the cellular functions of mammalian organisms. Driven by the biological significance of lipids, lipidomics (39) is considered a key member of the multi-omics family to understand disease-specific dysfunction of lipid metabolism and lipid species associated with the disease, help to discover biomarkers and targets for monitoring therapeutic strategies, and to provide insights into tumor lipid analysis and pathophysiological mechanisms. Compared with normal cells, the lipid metabolic reprogramming of tumor cells can significantly affect the growth, proliferation, differentiation, apoptosis, and drug resistance of cancer cells, and it has been demonstrated in lung cancer and its subtypes (40). Lipidomics can detect differences in the abundance of lipids in different samples, but the differential spatial distribution of lipids cannot be obtained after the homogenization of tissue samples. Although spatial metabolomics detects few metabolites, it can sensitively obtain information on the spatial distribution of lipids. Spatial metabolomics combined with multi-omics analysis can complement the weaknesses and complement each other.

To more comprehensively reveal the distribution of anticancer drugs in tumor tissues in the process of cancer treatment and the drug’s impact on endogenous lipid metabolites, Yanyan Chen et al. first simulated the three-dimensional multicellular tumor spheroids with a complicated tumor microenvironment as a platform for in vitro research of drugs and endogenous metabolites. After finishing modeling, combined with MALDI-MSI and lipidomics, they studied the drug accumulation time and spatial distribution of hydroxychloroquine (HCQ) and its impact on lipid metabolism in lung cancer multicellular spheroids (41). The results show that using HCQ as treatment can significantly affect the changes in lipid abundance in multicellular spheroids of lung cancer, among which lipids like phosphatidyl ethanolamine (PE), phosphatidylcholine (PC), phosphatidylinositol (PI) can be more focused on. These findings can help us better understand the close relationship between lipid metabolic reprogramming caused by antitumor drugs and the tumor microenvironment (TME).

By using a comprehensive approach of global lipid metabolomics based on MSI as well as spatial metabolomics, Chao Zhao et al. investigated the toxic effects of bisphenol F (BPF) and its potential toxicology mechanisms in breast cancer xenografts and tumor metastasis-related tissues (e.g. liver and kidney) in breast cancer xenografts treated with BPF (42). Moreover, they used lipidomics and spatial metabolomics techniques to exploit and finally successfully apply a novel method for the fingerprint map analysis of global lipids and metabolites in the kidney and liver. The results have shown that exposure to BPF interferes with the metabolome and liposome of the liver and kidney, which results in metabolic reprogramming by activating amino acid biosynthesis and glycolysis metabolism in liver and kidney tissues. It also disrupts the biosynthesis and degradation of lipids, leading to abnormal cell membrane homeostasis and cell function. These observations provide greater insights into the critical role of metabolic reprogramming in the biological effects of BPF-induced tumor growth and proliferation.

Chenglong Sun et al. Have proposes an integrated spatially-resolved multiomics approach to explore cell-specific metabolic reprogramming and interactions in the microenvironment of gastric cancer (43). They have conducted Spatial metabolomics (SM), Spatial Lipidomics (SL) and Spatial Transcriptomics (ST) analysis on frozen cancer tissue sections from patients with gastric adenocarcinoma, and depicted spatially-resolved maps of metabolites, lipids, and gene expression patterns both in tumor cells and normal cells. They also have found that arginine and proline metabolism is significantly reprogrammed in gastric adenocarcinoma: arginine and its synthesis-associated gene ASS1 are both upregulated in tumor sections. And the visualized results have shown that the expression of most lipids in gastric adenocarcinoma tissues is elevated except the expression of Phosphatidylserine (PS) and PI (44). It is notable that an immune desert phenotype has been found in both the stroma and parenchyma of gastric cancer tissues, indicating the lack of T cell tumors, which suggests that the alterations of relevant lipid in tumor tissues can strongly impact immune cell differentiation and activation (45).

Multi-omics analysis combined with spatial metabolomics can improve the integrity of biological information analysis and can visually detect gene, gene transcripts (RNA), various proteins that perform different biological functions, and even the various metabolites in the subordinating metabolic pathways. In conclusion, multi-omics analysis can help us expand our vision of the scientific field better and obtain a deeper understanding of the significance of omics interactions at different levels.

Energy metabolism reprogramming of tumor cells promotes rapid cell growth, proliferation, and differentiation by regulating energy metabolism. And it is considered a unique marker of tumor cells (48), including aerobic glycolysis, glutaminolysis, and upregulation of lipid synthesis. Among them, the famous scientist Otto Heinrich Warburg found the Warburg effect (49) in the 1920s. Even under the condition of sufficient oxygen supply, tumor cells prefer glycolysis rather than mitochondrial oxidative phosphorylation to generate energy, resulting in a high glucose uptake rate and elevated metabolic lactate content. Compared with traditional metabolomics, spatial metabolomics can help scientisits to provide relatively accurate metabolic evidence that drives the regulation of phenotype and to reflect physiological conditions at the spatial level well. Therefore, spatial metabolomics has surpassed traditional diagnostic methods that rely on a few diagnostic markers, and can effectively characterize tumor metabolic disorders (50), achieving precision medicine (51).

Metabolic reprogramming of glycolysis is an important aspect of tumor cells’ adaptation to hypoxia and is one of the marks of malignancy. Lingzhi Wang et al. have applicated together with LC-MS, metabolomics, immunohistochemical (IHC) (52) staining, and other analytical means for the research of the nasopharyngeal carcinoma (NPC) glucose metabolism process in depth. After the statistical analysis, they found that the tumor suppressor gene CYLD increases the stability and nuclear transportation of p53 by deubiquitination, to downregulate the transcription of phosphofructokinase-2/fructose-2, 6-Diphosphatase 3 (PFKFB3) (53), thus inhibiting the glycolysis process in NPC cells. The latest researches have shown that PFKFB3 has been associated with various aspects of cancer, including cancer cell proliferation, vascular invasiveness, drug resistance, and the TME (54). Furthermore, Baoyu He et al. have identified and functionally characterized a novel, metabolically relevant, long, non-coding RNA (lncRNA) LINC00930 (55). The result suggests that LINC00930 promotes the glycolysis process in NPC cells by up-regulating the expression of PFKFB3 in NPC. Therefore, the lack of expression of CYLD enhances the reprogramming of glucose metabolism and tumor progression and is associated with poor prognosis in NPC through PFKFB3 upregulation. These conclusions suggest that CYLD can be used as a clinical biomarker for predicting NPC prognosis, and that PFKFB3 may also be a new therapeutic target for targeted therapy in the future (56).

Early tumor diagnosis is essential to prolong patients’ survival time and improve their quality of life and prognosis (57). The traditional methods of early tumor diagnosis mainly include imaging examination, blood and tumor marker detection, pathological puncture biopsy, and cell smear examination. However, due to tumor heterogeneity, it is difficult for these methods to have both high specificity and sensitivity. After the diagnosis of tumors, accurate and timely classification and grading not only influence the survival time of patients, but also play a decisive role in the selection of treatment options (58).

With the continuous research on the mechanism of tumor metabolic reprogramming, scientists have found that metabolic reprogramming of tumor cells is often driven by a variety of signaling pathways and transcription factors (59). Therefore, screening of key protein markers of tumor-dependent metabolic reprogramming has a promising prospect, which is conducive to the realization of early clinical diagnosis and the development of novel drugs targeting tumor metabolism (60).

According to the latest application of spatial metabolomics in the diagnosis and classification of head and neck tumors, Luojiao Huang et al. have applied spatial metabolomics analysis by using AFADESI-MSI technology to thyroid tumors (61). Firstly, they characterized high-resolution, tumor region-specific metabolites spectra for each pathologic type of thyroid tumors. And then they identified 83 sets of metabolic biomarkers which could visually differentiate benign follicular adenoma (FA) from differentiated thyroid cancers including papillary thyroid carcinoma (PTC) and thyroid follicular cancer (FTC). Finally, a molecular diagnostic strategy was established to distinguish between 65 pathologic thyroid tumors. Overall, the diagnostic models based on the metabolic spectrum of different types of thyroid tumors, have showed a prediction accuracy of 83.3%. For the rarer and more aggressive medullary thyroid cancer (MTC), Andrew Smith et al. have analyzed tissue samples from 7 patients with MTC with the MALDI-MSI technology (62). After performing a proteomic combination analysis, they have identified and selected several potential tumor markers associated with the pathogenesis of MTC. These findings can provide a valuable starting point for further studies on the diagnosis and analysis of thyroid tumors in the preoperative stage.

Tumor-targeted therapy is defined as the use of drugs that target different cancer cell targets and do not cause simultaneous harm to non-cancer cells as conventional chemotherapeutic drugs do (63). The purpose of investigating targeted therapy is to discover and attack specific areas or substances in tumor cells or to detect and prevent specific signaling molecules that are sent within the tumor cells that command the growth of tumor cells. Nowadays, tumor-targeted therapy is widely used in non-small cell lung cancer (NSCLC) (64, 65), triple-negative breast cancer (BC) (66), and other cancers. But deeper research of targeted therapy for head and neck tumors is still in great need.

Recently, amino acid metabolic reprogramming in head and neck tumor is attracting increasing attention, especially in glutamine metabolism. Glutamine, despite being a non-essential amino acid, is the most abundant amino acid in circulation (67). In the normal human body, the content of glutamine in the blood is relatively constant, but when the cells are under extremely vigorous catabolic conditions, the decomposition of glutamine is significantly elevated (68). Toshimitsu Ohashi et al. have combined capillary electrophoresis-mass spectrometry imaging (CE-MS) with metabolomics to comparatively analyze 23 HNSCC patients and 6 patients without cancer. They have proved that glutamine metabolism was markedly up-regulated in HNSCC tissues, and glutamine metabolites, such as glutathione (GSH), were accumulated in HNSCC tissues (69), to mediate the redox balance of HNSCC tumor cells, and to meet the exuberant needs for energy, anabolic carbon sources, and nitrogen sources of tumor cells. The above results indicate that the up-regulation of glutamine metabolism plays an important role in autophagy regulation in HNSCC tumor cells through the homeostasis of reactive oxygen species free radicals (ROS). In the future, we can also continue to explore anti-tumor drugs targeting glutamine metabolism.

Additionally, the reprogramming of lipid metabolism in head and neck tumors is also being further researched by scientists around the world. Normal body cells rely mainly on the free fatty acids from the food to synthesize the lipid components. Due to the vigorous metabolism of tumor cells, they need a large amount of lipid supply. Therefore, tumor cells depend more on de novo fatty acid synthesis to meet their special lipid needs (70), thus undergoing lipid metabolism reprogramming (71). Schmidt J et al. have used the MALDI-MSI technology to search proteins and lipids that are significantly overexpressed in tumor tissues from numerous HNSCC samples (72). They have found that in the visible low-molecular-weight proteins of this laryngeal cancer patient specimen, S100A8 and S100A9 are highly expressed in the tumor tissue region, but not in the surrounding healthy tissue. In addition, they also found that lysophosphatidylcholine (LPC) levels were significantly decreased, even almost completely abolished in the tumor region, while the glycerophospholipid PE-P and PC accumulated significantly. Further research by Kerkhoff C et al. has found LPC-digested lysophospholipase 1 (Human Acyl-protein thioesterase 1, LYPLA1) (73) accumulation in the tumor region, suggesting that the progression of HNSCC in vivo may depend on lysophospholipid (LP) supply. These findings have confirmed that the hypoxia-induced fatty acids uptake and lipid droplet accumulation of tumor cells constitute lipid metabolism reprogramming, which can effectively prevent the toxicity caused by ROS accumulation, thus providing higher survival benefits and treatment resistance for tumor tissues. Furthermore, it is of great significance to the research of HNSCC metabolic mechanisms and provides targets that can be further explored for the treatment of HNSCC, which has a broad clinical application prospect.

As an important component of personalized cancer therapy, novel drugs that target to tumor metabolism have progressed from preclinical studies to clinical trials, and in some cases have shown great efficacy. It is believed that with the advancement of drug metabolism analytical technology, researchers will be able to discover more new drug targets, construct appropriate patient models and accurately identify target patients, so as to provide patients with more drug choices and to better improve patients’ survival rate.