Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.

Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Combined Inhibition of UBE2C and PLK1 Reduce Cell Proliferation and Arrest Cell Cycle by Affecting ACLY in Pan-Cancer.

Various studies have shown that the cell-cycle-related regulatory proteins UBE2C, PLK1, and BIRC5 promote cell proliferation and migration in different types of cancer. However, there is a lack of in-depth and systematic research on the mechanism of these three as therapeutic targets. In this study, we found a positive correlation between the expression of UBE2C and PLK1/BIRC5 in the Cancer Genome Atlas (TCGA) database, revealing a potential combination therapy candidate for pan-cancer. Quantitative real-time PCR (qRT-PCR), Western blotting (WB), cell phenotype detection, and RNA-seq techniques were used to evidence the effectiveness of the combination candidate. We found that combined interference of UBE2C with PLK1 and UBE2C with BIRC5 affected metabolic pathways by significantly downregulating the mRNA expression of IDH1 and ACLY, which was related to the synthesis of acetyl-CoA. By combining the PLK1 inhibitor volasertib and the ACLY inhibitor bempedoic acid, it showed a higher synergistic inhibition of cell viability and higher synergy scores in seven cell lines, compared with those of other combination treatments. Our study reveals the potential mechanisms through which cell-cycle-related genes regulate metabolism and proposes a potential combined targeted therapy for patients with higher PLK1 and ACLY expression in pan-cancer.

Cucurbitacin I Reverses Tumor-Associated Macrophage Polarization to Affect Cancer Cell Metastasis.

The tumor microenvironment plays a critical role in tumor progression and immune regulation. As one of the most important components of the tumor microenvironment, macrophages have become a new therapeutic target for inhibiting tumor progression. Despite the well-documented anticancer activity of cucurbitacin I, its effect on macrophages remains unclear. In this study, we established a coculture system of macrophages and cancer cells under hypoxic conditions to simulate the tumor-promoting environment mediated by M2-like macrophages. We determined whether cucurbitacin I modulates M2-like polarization in macrophages in vitro and conducted RNA sequencing to identify gene expression changes induced by cucurbitacin I in macrophages. The results indicated a remarkable inhibition of the M2-like polarization phenotype in macrophages following treatment with cucurbitacin I, which was accompanied by the significant downregulation of heme oxygenase-1. Moreover, we found that cucurbitacin I-treated macrophages reduced the migration of cancer cells by inhibiting the M2 polarization in vitro. These findings highlight the potential of cucurbitacin I as a therapeutic agent that targets M2-like macrophages to inhibit cancer cell metastasis. Our study provides novel insights into the intricate interplay among macrophage polarization, cucurbitacin I, and heme oxygenase-1, thereby opening new avenues for cancer treatment.

Intercellular Molecular Crosstalk Networks within Invasive and Immunosuppressive Tumor Microenvironment Subtypes Associated with Clinical Outcomes in Four Cancer Types.

Heterogeneity is a critical basis for understanding how the tumor microenvironment (TME) contributes to tumor progression. However, an understanding of the specific characteristics and functions of TME subtypes (subTMEs) in the progression of cancer is required for further investigations into single-cell resolutions. Here, we analyzed single-cell RNA sequencing data of 250 clinical samples with more than 200,000 cells analyzed in each cancer datum. Based on the construction of an intercellular infiltration model and unsupervised clustering analysis, four, three, three, and four subTMEs were revealed in breast, colorectal, esophageal, and pancreatic cancer, respectively. Among the subTMEs, the immune-suppressive subTME (subTME-IS) and matrix remodeling with malignant cells subTME (subTME-MRM) were highly enriched in tumors, whereas the immune cell infiltration subTME (subTME-ICI) and precancerous state of epithelial cells subTME (subTME-PSE) were less in tumors, compared with paracancerous tissues. We detected and compared genes encoding cytokines, chemokines, cytotoxic mediators, PD1, and PD-L1. The results showed that these genes were specifically overexpressed in different cell types, and, compared with normal tissues, they were upregulated in tumor-derived cells. In addition, compared with other subTMEs, the expression levels of PDCD1 and TGFB1 were higher in subTME-IS. The Cox proportional risk regression model was further constructed to identify possible prognostic markers in each subTME across four cancer types. Cell-cell interaction analysis revealed the distinguishing features in molecular pairs among different subTMEs. Notably, ligand-receptor gene pairs, including COL1A1-SDC1, COL6A2-SDC1, COL6A3-SDC1, and COL4A1-ITGA2 between stromal and tumor cells, associated with tumor invasion phenotypes, poor patient prognoses, and tumor advanced progression, were revealed in subTME-MRM. C5AR1-RPS19, LGALS9-HAVCR2, and SPP1-PTGER4 between macrophages and CD8+ T cells, associated with CD8+ T-cell dysfunction, immunosuppressive status, and tumor advanced progression, were revealed in subTME-IS. The spatial co-location information of cellular and molecular interactions was further verified by spatial transcriptome data from colorectal cancer clinical samples. Overall, our study revealed the heterogeneity within the TME, highlighting the potential pro-invasion and pro-immunosuppressive functions and cellular infiltration characteristics of specific subTMEs, and also identified the key cellular and molecular interactions that might be associated with the survival, invasion, immune escape, and classification of cancer patients across four cancer types.

Diagnostic value of 3D Optical Coherence Tomography Multimode Images in the Diagnosis of Acute Central Serous Chorioretinopathy.

BACKGROUND: Spectral-Domain Optical Coherence Tomography (SD-OCT) provides non-invasive, high-speed, high-resolution, three-dimensional cross-section imaging of the macula. OBJECTIVES: This study aimed to investigate the diagnostic value of the multimodal imaging technique of three-dimension (3D) optical coherence tomography (OCT) (3D-OCT) for the diagnosis and characterization of acute central serous chorioretinopathy (CSC). METHODS: In this prospective clinical study 3D-OCT examinations of 82 cases with acute CSC were performed on the macular area, and the image characteristics were analyzed. Our study included a total of 87 eyes from 82 cases of CSC patients, 67 males and 15 females (mean age ± standard deviation (SD): 42.89 ±7.80 years old; age range: 27 to 56 years old. The 3D-OCT images were evaluated for the presence of subretinal fluid, subretinal space, fluctuation of the internal limiting membrane (ILM), folds of retinal pigment epithelial (RPE), retinal pigment epithelium detachment (PED), and flat irregular PED. The foveal thickness was measured using the manual caliper of OCT software. RESULTS: The OCT B-scan images showed 87 (100%) eyes had exudative retinal detachment (ERD), 38 (44%) had flat irregular PED, 36 (41%) had PED, 8 (9%) had subretinal turbidity structure, 2 (2%) had subretinal dot-like precipitates, 1 (1%) had focal choroidal excavation (FCE), and 1 (1%) eye had fluctuation of internal limiting membrane (FI). In the ILM-RPE thickness map, all eyes had a round or round like regular uniform domes. Fifty-seven (66%) domes were limited in the examination area and 30 (44%) domes were beyond the scope of this examination and only a partial section of the dome could be observed. In the en-face image, all eyes had a round or round-like black figure that corresponded with domes in the ILM-RPE thickness map. In RPE surface, 76 (87%) eyes had a shallow plate depression, 71(82%) had small focal uplift, and 1 (1%) eye had a focal concave feature. CONCLUSIONS: In the OCT ILM-RPE thickness, en-face image, and RPE surface maps, acute CSC exhibited specific imaging characteristics that can be helpful for reliable diagnosis and differential diagnosis of CSC.

Single-cell and spatial analyses reveal the association between gene expression of glutamine synthetase with the immunosuppressive phenotype of APOE+CTSZ+TAM in cancers.

An immunosuppressive state is regulated by various factors in the tumor microenvironment (TME), including, but not limited to, metabolic plasticity of immunosuppressive cells and cytokines secreted by these cells. We used single-cell RNA-sequencing (scRNA-seq) data and applied single-cell flux estimation analysis to characterize the link between metabolism and cellular function within the hypoxic TME of colorectal (CRC) and lung cancer. In terms of metabolic heterogeneity, we found myeloid cells potentially inclined to accumulate glutamine but tumor cells inclined to accumulate glutamate. In particular, we uncovered a tumor-associated macrophage (TAM) subpopulation, APOE+CTSZ+TAM, that was present in high proportions in tumor samples and exhibited immunosuppressive characteristics through upregulating the expression of anti-inflammatory genes. The proportion of APOE+CTSZ+TAM and regulatory T cells (Treg) were positively correlated across CRC scRNA-seq samples. APOE+CTSZ+TAM potentially interacted with Treg via CXCL16-CCR6 signals, as seen by ligand-receptor interactions analysis. Notably, glutamate-to-glutamine metabolic flux score and glutamine synthetase (GLUL) expression were uniquely higher in APOE+CTSZ+TAM, compared with other cell types within the TME. GLUL expression in macrophages was positively correlated with anti-inflammatory score and was higher in high-grade and invasive tumor samples. Moreover, spatial transcriptome and multiplex immunofluorescence staining of samples showed that APOE+CTSZ+TAM and Treg potentially colocalized in the tissue sections from CRC clinical samples. These results highlight the specific role and metabolic characteristic of the APOE+CTSZ+TAM subpopulation and provide a new perspective for macrophage subcluster-targeted therapeutic interventions or metabolic checkpoint-based cancer therapies.

KAT2A/E2F1 Promotes Cell Proliferation and Migration via Upregulating the Expression of UBE2C in Pan-Cancer.

Various studies have shown that lysine acetyltransferase 2A (KAT2A), E2F transcription factor 1 (E2F1), and ubiquitin conjugating enzyme E2 C (UBE2C) genes regulated the proliferation and migration of tumor cells through regulating the cell cycle. However, there is a lack of in-depth and systematic research on their mechanisms of action. This study analyzed The Cancer Genome Atlas (TCGA) to screen potential candidate genes and the regulation network of KAT2A and E2F1 complex in pan-cancer. Quantitative real-time PCR (qRT-PCR) and Western blotting (WB), cell phenotype detection, immunofluorescence co-localization, chromatin immunoprecipitation assay (ChIP), and RNA-Seq techniques were used to explore the functional of a candidate gene, UBE2C. We found that the expression of these three genes was significantly higher in more than 10 tumor types compared to normal tissue. Moreover, UBE2C was mainly expressed in tumor cells, which highlighted the impacts of UBE2C as a specific therapeutic strategy. Moreover, KAT2A and E2F1 could promote cell proliferation and the migration of cancer cells by enhancing the expression of UBE2C. Mechanically, KAT2A was found to cooperate with E2F1 and be recruited by E2F1 to the UBE2C promoter for elevating the expression of UBE2C by increasing the acetylation level of H3K9.

scWizard: A web-based automated tool for classifying and annotating single cells and downstream analysis of single-cell RNA-seq data in cancers.

The emerging number of single-cell RNA-seq (scRNA-Seq) datasets allows the characterization of cell types across various cancer types. However, there is still lack of effective tools to integrate the various analysis of single-cells, especially for making fine annotation on subtype cells within the tumor microenvironment (TME). We developed scWizard, a point-and-click tool packaging automated process including our developed cell annotation method based on deep neural network learning and 11 downstream analyses methods. scWizard used 113,976 cells across 13 cancer types as a built-in reference dataset for training the hierarchical model enabling to automatedly classify and annotate 7 major cell types and 47 cell subtypes in the TME. scWizard provides a built-in pre-training set for user's flexible choice, and gives a higher accuracy for annotation subtypes of tumor-derived T-lymphocytes/natural killer cells (T/NK) and myeloid cells from different cancer types compared with the existing five methods. scWizard has good robustness in three independent cancer datasets, with an accuracy of 0.98 in annotating major cell types, 0.85 in annotating myeloid cell subtypes and 0.79 in annotating T/NK cell subtypes, indicting the wide applicability of scWizard in different cell types of cancers. Finally, the automatic analysis and visualization function of scWizard are presented by using the intrahepatic cholangiocarcinoma (ICC) scRNA-Seq dataset as a case. scWizard focuses on decoding TME and covers various analysis flows for cancer scRNA-Seq study, and provides an easy-to-use tool and a user-friendly interface for researchers widely, to further accelerate the biological discovery of cancer research.

PRR11 promotes cell proliferation by regulating PTTG1 through interacting with E2F1 transcription factor in pan-cancer.

The upregulated proline rich 11 (PRR11) plays a critical role in cancer progression. The relevant biological functions of PRR11 in pan-cancer development are not well understood. In the current study, we found that PRR11 was upregulated in 19 cancer types compared with that of normal tissues and high-expressed PRR11 was a predictor of poor prognosis in 10 cancer types by bioinformatics. Then we showed that interfering PRR11 on three cancer cell lines could greatly inhibit cell proliferation and migration and arrest cells to S phase in vivo. Based on RNA-seq, downregulation of PRR11 expression could extremely suppress the expression of PTTG1 and the cell cycle pathway identified by a differentially expressed gene analysis and an enrichment analysis. The expression of PRR11 and PTTG1 was positively correlated in TCGA and independent GEO data sets. Importantly, we revealed that the PRR11 could express itself in the nucleus and interact with E2F1 on the PTTG1 promoter region to increase the expression of PTTG1. Further results indicated that the expression of PTTG1 was also associated with poor prognosis in 10 cancer types, while downregulation of PTTG1 expression could inhibit cancer cell proliferation and migration. Therefore, we found that PRR11 served as an oncogene in pan-cancer and could influence the cell cycle progression through regulating the expression of PTTG1 by interacting with the transcription factor E2F1.

A score of DNA damage repair pathway with the predictive ability for chemotherapy and immunotherapy is strongly associated with immune signaling pathway in pan-cancer.

DNA damage repair (DDR) is critical in maintaining normal cellular function and genome integrity and is associated with cancer risk, progression, and therapeutic response. However, there is still a lack of a thorough understanding of the effects of DDR genes' expression level in cancer progression and therapeutic resistance. Therefore, we defined a tumor-related DDR score (TR-DDR score), utilizing the expression levels of 20 genes, to quantify the tumor signature of DNA damage repair pathways in tumors and explore the possible function and mechanism for the score among different cancers. The TR-DDR score has remarkably predictive power for tumor tissues. It is a more accurate indicator for the response of chemotherapy or immunotherapy combined with the tumor-infiltrating lymphocyte (TIL) and G2M checkpoint score than the pre-existing predictors (CD8 or PD-L1). This study points out that the TR-DDR score generally has positive correlations with patients of advanced-stage, genome-instability, and cell proliferation signature, while negative correlations with inflammatory response, apoptosis, and p53 pathway signature. In the context of tumor immune response, the TR-DDR score strongly positively correlates with the number of T cells (CD4+ activated memory cells, CD8+ cells, T regs, Tfh) and macrophages M1 polarization. In addition, by difference analysis and correlation analysis, COL2A1, MAGEA4, FCRL4, and ZIC1 are screened out as the potential modulating factors for the TR-DDR score. In summary, we light on a new biomarker for DNA damage repair pathways and explore its possible mechanism to guide therapeutic strategies and drug response prediction.

A pilot study of combined optical coherence tomography and diffusion tensor imaging method for evaluating microstructural change in the visual pathway of pituitary adenoma patients.

BACKGROUND: RNFL thickness measured by optical coherence tomography (OCT) and visual pathway measured by diffusion tensor imaging (DTI) can be used to predict visual field recovery, respectively. However, the relationship between RNFL thickness and visual pathway injury in patients with pituitary adenoma (PA) remains unclear. This study aims to evaluate the combining DTI and OCT methods in observing the microstructural change in the visual pathway in patients with PA. METHODS: Twenty-nine patients who were diagnosed with PA were included in the study group, and 29 healthy subjects were included as the control group. OCT detected the thickness of circumpapillary retinal nerve fiber layer (CP-RNFL) and ganglion cell layer (GCL). DTI measured the values of fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Correlation between CP-RNFL and GCL thickness and FA and ADC values was analyzed in the study group. RESULTS: Compared with the control group, the FA values of the bilateral optic nerve, chiasma, bilateral optic tract, and left optic radiation in the study group were reduced, and the ADC values of the bilateral optic nerve and optic chiasma were increased. Correlation analysis showed that the FA value of the optic chiasma was positively correlated with the average thickness of RNFL, the CP-RNFL thickness in the nasal and temporal retinal quadrants in both eyes, as well as the thickness of macular ring GCL in the nasal, supra, and inferior quadrants. The FA values of the optic nerve, optic chiasma, optic tract, and optic radiation were positively correlated with CP-RNFL thickness in the nasal and temporal quadrants. CONCLUSION: Combined DTI and OCT can provide a comprehensive understanding of the microscopic changes in the structure and function of the whole visual pathway in patients with PA.

Improving Cancer Immunotherapy: Exploring and Targeting Metabolism in Hypoxia Microenvironment.

Although immunotherapy has achieved good results in various cancer types, a large proportion of patients are limited from the benefits. Hypoxia and metabolic reprogramming are the common and critical factors that impact immunotherapy response. Here, we present current research on the metabolism reprogramming induced by hypoxia on antitumor immunity and discuss the recent progression among preclinical and clinical trials exploring the therapeutic effects combining targeting hypoxia and metabolism with immunotherapy. By evaluating the little clinical translation of the combined therapy, we provide insight into "understanding and regulating cellular metabolic plasticity under the current tumor microenvironment (TME)," which is essential to explore the strategy for boosting immune responses by targeting the metabolism of tumor cells leading to harsh TMEs. Therefore, we highlight the potential value of advanced single-cell technology in revealing the metabolic heterogeneity and corresponding phenotype of each cell subtype in the current hypoxic lesion from the clinical patients, which can uncover potential metabolic targets and therapeutic windows to enhance immunotherapy.

Analyses of Long-Term Epidemic Trends and Evolution Characteristics of Haplotype Subtypes Reveal the Dynamic Selection on SARS-CoV-2.

The scale of SARS-CoV-2 infection and death is so enormous that further study of the molecular and evolutionary characteristics of SARS-CoV-2 will help us better understand and respond to SARS-CoV-2 outbreaks. The present study analyzed the epidemic and evolutionary characteristics of haplotype subtypes or regions based on 1.8 million high-quality SARS-CoV-2 genomic data. The estimated ratio of the rates of non-synonymous to synonymous changes (Ka/Ks) in North America and the United States were always more than 1.0, while the Ka/Ks in other continents and countries showed a sharp decline, then a slow increase to 1.0, and a dramatic increase over time. H1 (B.1) with the highest substitution rate has become the most dominant haplotype subtype since March 2020 and has evolved into multiple haplotype subtypes with smaller substitution rates. Many evolutionary characteristics of early SARS-CoV-2, such as H3 being the only early haplotype subtype that existed for the shortest time, the global prevalence of H1 and H1-5 (B.1.1) within a month after being detected, and many high divergent genome sequences early in February 2020, indicate the missing of early SARS-CoV-2 genomic data. SARS-CoV-2 experienced dynamic selection from December 2019 to August 2021 and has been under strong positive selection since May 2021. Its transmissibility and the ability of immune escape may be greatly enhanced over time. This will bring greater challenges to the control of the pandemic.

Single-Cell Transcriptome Analysis Reveals RGS1 as a New Marker and Promoting Factor for T-Cell Exhaustion in Multiple Cancers.

T-cell exhaustion is one of the main reasons of tumor immune escape. Using single-cell transcriptome data of CD8+ T cells in multiple cancers, we identified different cell types, in which Pre_exhaust and exhausted T cells participated in negative regulation of immune system process. By analyzing the coexpression network patterns and differentially expressed genes of Pre_exhaust, exhausted, and effector T cells, we identified 35 genes related to T-cell exhaustion, whose high GSVA scores were associated with significantly poor prognosis in various cancers. In the differentially expressed genes, RGS1 showed the greatest fold change in Pre_exhaust and exhausted cells of three cancers compared with effector T cells, and high expression of RGS1 was also associated with poor prognosis in various cancers. Additionally, RGS1 protein was upregulated significantly in tumor tissues in the immunohistochemistry verification. Furthermore, RGS1 displayed positive correlation with the 35 genes, especially highly correlated with PDCD1, CTLA4, HAVCR2, and TNFRSF9 in CD8+ T cells and cancer tissues, indicating the important roles of RGS1 in CD8+ T-cell exhaustion. Considering the GTP-hydrolysis activity of RGS1 and significantly high mRNA and protein expression in cancer tissues, we speculated that RGS1 potentially mediate the T-cell retention to lead to the persistent antigen stimulation, resulting in T-cell exhaustion. In conclusion, our findings suggest that RGS1 is a new marker and promoting factor for CD8+ T-cell exhaustion and provide theoretical basis for research and immunotherapy of exhausted cells.

Characterizing Intercellular Communication of Pan-Cancer Reveals SPP1+ Tumor-Associated Macrophage Expanded in Hypoxia and Promoting Cancer Malignancy Through Single-Cell RNA-Seq Data.

Hypoxia is a characteristic of tumor microenvironment (TME) and is a major contributor to tumor progression. Yet, subtype identification of tumor-associated non-malignant cells at single-cell resolution and how they influence cancer progression under hypoxia TME remain largely unexplored. Here, we used RNA-seq data of 424,194 single cells from 108 patients to identify the subtypes of cancer cells, stromal cells, and immune cells; to evaluate their hypoxia score; and also to uncover potential interaction signals between these cells in vivo across six cancer types. We identified SPP1+ tumor-associated macrophage (TAM) subpopulation potentially enhanced epithelial-mesenchymal transition (EMT) by interaction with cancer cells through paracrine pattern. We prioritized SPP1 as a TAM-secreted factor to act on cancer cells and found a significant enhanced migration phenotype and invasion ability in A549 lung cancer cells induced by recombinant protein SPP1. Besides, prognostic analysis indicated that a higher expression of SPP1 was found to be related to worse clinical outcome in six cancer types. SPP1 expression was higher in hypoxia-high macrophages based on single-cell data, which was further validated by an in vitro experiment that SPP1 was upregulated in macrophages under hypoxia-cultured compared with normoxic conditions. Additionally, a differential analysis demonstrated that hypoxia potentially influences extracellular matrix remodeling, glycolysis, and interleukin-10 signal activation in various cancer types. Our work illuminates the clearer underlying mechanism in the intricate interaction between different cell subtypes within hypoxia TME and proposes the guidelines for the development of therapeutic targets specifically for patients with high proportion of SPP1+ TAMs in hypoxic lesions.

ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research.

The comprehensive and integrative analysis of RNA-seq data, in different molecular layers from diverse samples, holds promise to address the full-scale complexity of biological systems. Recent advances in gene set variant analysis (GSVA) are providing exciting opportunities for revealing the specific biological processes of cancer samples. However, it is still urgently needed to develop a tool, which combines GSVA and different molecular characteristic analysis, as well as prognostic characteristics of cancer patients to reveal the biological processes of disease comprehensively. Here, we develop ARMT, an automatic tool for RNA-Seq data analysis. ARMT is an efficient and integrative tool with user-friendly interface to analyze related molecular characters of single gene and gene set comprehensively based on transcriptome and genomic data, which builds the bridge for deeper information between genes and pathways, to further accelerate scientific findings. ARMT can be installed easily from https://github.com/Dulab2020/ARMT.

Multi-Omics Perspective Reveals the Different Patterns of Tumor Immune Microenvironment Based on Programmed Death Ligand 1 (PD-L1) Expression and Predictor of Responses to Immune Checkpoint Blockade across Pan-Cancer.

Immune checkpoint inhibitor (ICI) therapies have shown great promise in cancer treatment. However, the intra-heterogeneity is a major barrier to reasonably classifying the potential benefited patients. Comprehensive heterogeneity analysis is needed to solve these clinical issues. In this study, the samples from pan-cancer and independent breast cancer datasets were divided into four tumor immune microenvironment (TIME) subtypes based on tumor programmed death ligand 1 (PD-L1) expression level and tumor-infiltrating lymphocyte (TIL) state. As the combination of the TIL Z score and PD-L1 expression showed superior prediction of response to ICI in multiple data sets compared to other methods, we used the TIL Z score and PD-L1 to classify samples. Therefore, samples were divided by combined TIL Z score and PD-L1 to identify four TIME subtypes, including type I (3.24%), type II (43.24%), type III (6.76%), and type IV (46.76%). Type I was associated with favorable prognosis with more T and DC cells, while type III had the poorest condition and composed a higher level of activated mast cells. Furthermore, TIME subtypes exhibited a distinct genetic and transcriptional feature: type III was observed to have the highest mutation rate (77.92%), while co-mutations patterns were characteristic in type I, and the PD-L1 positive subgroup showed higher carbohydrates, lipids, and xenobiotics metabolism compared to others. Overall, we developed a robust method to classify TIME and analyze the divergence of prognosis, immune cell composition, genomics, and transcriptomics patterns among TIME subtypes, which potentially provides insight for classification of TIME and a referrable theoretical basis for the screening benefited groups in the ICI immunotherapy.

Roles of Proteoglycans and Glycosaminoglycans in Cancer Development and Progression.

The extracellular matrix (ECM) spatiotemporally controls cell fate; however, dysregulation of ECM remodeling can lead to tumorigenesis and cancer development by providing favorable conditions for tumor cells. Proteoglycans (PGs) and glycosaminoglycans (GAGs) are the major macromolecules composing ECM. They influence both cell behavior and matrix properties through direct and indirect interactions with various cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes, and glycoproteins within the ECM. The classical features of PGs/GAGs play well-known roles in cancer angiogenesis, proliferation, invasion, and metastasis. Several lines of evidence suggest that PGs/GAGs critically affect broader aspects in cancer initiation and the progression process, including regulation of cell metabolism, serving as a sensor of ECM's mechanical properties, affecting immune supervision, and participating in therapeutic resistance to various forms of treatment. These functions may be implemented through the characteristics of PGs/GAGs as molecular bridges linking ECM and cells in cell-specific and context-specific manners within the tumor microenvironment (TME). In this review, we intend to present a comprehensive illustration of the ways in which PGs/GAGs participate in and regulate several aspects of tumorigenesis; we put forward a perspective regarding their effects as biomarkers or targets for diagnoses and therapeutic interventions.

Characterization of Glycolysis-Associated Molecules in the Tumor Microenvironment Revealed by Pan-Cancer Tissues and Lung Cancer Single Cell Data.

Altered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. There is however still a lack of molecular characterization glycolysis and comprehensive studies related to tumor glycolysis in the pan-cancer landscape. Here, we applied a gene expression signature to quantify glycolysis in 9229 tumors across 25 cancer types and 7875 human lung cancer single cells and verified the robustness of signature using defined glycolysis samples from previous studies. We classified tumors and cells into glycolysis score-high and -low groups, demonstrated their prognostic associations, and identified genome and transcriptome molecular features associated with glycolysis activity. We observed that glycolysis score-high tumors were associated with worse prognosis across cancer types. High glycolysis tumors exhibited specific driver genes altered by copy number aberrations (CNAs) in most cancer types. Tricarboxylic acid (TCA) cycle, DNA replication, tumor proliferation and other cancer hallmarks were more active in glycolysis-high tumors. Glycolysis signature was strongly correlated with hypoxia signature in all 25 cancer tissues (r > 0.7) and cancer single cells (r > 0.8). In addition, HSPA8 and P4HA1 were screened out as the potential modulating factors to glycolysis as their expression were highly correlated with glycolysis score and glycolysis genes, which enables future efforts for therapeutic options to block the glycolysis and control tumor progression. Our study provides a comprehensive molecular-level understanding of glycolysis with a large sample data and demonstrates the hypoxia pressure, growth signals, oncogene mutation and other potential signals could activate glycolysis, thereby to regulate cell cycle, energy material synthesis, cell proliferation and cancer progression.