Unraveling LGALS1 as a Potential Immune Checkpoint and a Predictor of the Response to Anti-PD1 Therapy in Clear Cell Renal Carcinoma

Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistance

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape across multiple cancer types achieving durable responses for a significant number of patients. Despite their success, many patients still fail to respond to ICIs or develop resistance soon after treatment. We sought to identify early treatment features associated with ICI outcome. We leveraged the MC38 syngeneic tumor model because it has variable response to ICI therapy driven by tumor intrinsic heterogeneity. ICI response was assessed based on the level of immune cell infiltration into the tumor - a well-established clinical hallmark of ICI response. We generated a spatial atlas of 48,636 transcriptome-wide spots across 16 tumors using spatial transcriptomics; given the tumors were difficult to profile, we developed an enhanced transcriptome capture protocol yielding high quality spatial data. In total, we identified 8 tumor cell subsets (e.g., proliferative, inflamed, and vascularized) and 4 stroma subsets (e.g., immune and fibroblast). Each tumor had orthogonal histology and bulk-RNA sequencing data, which served to validate and benchmark observations from the spatial data. Our spatial atlas revealed that increased tumor cell cholesterol regulation, synthesis, and transport were associated with a lack of ICI response. Conversely, inflammation and T cell infiltration were associated with response. We further leveraged spatially aware gene expression analysis, to demonstrate that high cholesterol synthesis by tumor cells was associated with cytotoxic CD8 T cell exclusion. Finally, we demonstrate that bulk RNA-sequencing was able to detect immune correlates of response but lacked the sensitivity to detect cholesterol synthesis as a feature of resistance.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment

BACKGROUND

Pancreatic cancer is one of the most lethal malignancies, characterized by poor prognosis and low survival rates. Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy, often failing to capture the complexity of the disease. The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment. This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.

AIM

To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.

METHODS

This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2, PLAU, and CCNA2. The results were validated in an independent dataset. This study also examined the correlations between the model risk score and various clinical features, components of the immune microenvironment, chemotherapeutic drug sensitivity, and metabolism-related pathways. Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.

RESULTS

The prognostic model demonstrated significant predictive value, with the risk score showing a strong correlation with clinical features: It was significantly associated with tumor grade (G) (b P < 0.01), moderately associated with tumor stage (T) (a P < 0.05), and significantly correlated with residual tumor (R) status (b P < 0.01). There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs. Furthermore, the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.

CONCLUSION

The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives

The advent of PD1/PD-L1 inhibitors has significantly transformed the therapeutic landscape for clear cell renal cell carcinoma (ccRCC). This review provides an in-depth analysis of the biological functions and regulatory mechanisms of PD1 and PD-L1 in ccRCC, emphasizing their role in tumor immune evasion. We comprehensively evaluate the clinical efficacy and safety profiles of PD1/PD-L1 inhibitors, such as Nivolumab and Pembrolizumab, through a critical examination of recent clinical trial data. Furthermore, we discuss the challenges posed by resistance mechanisms to these therapies and potential strategies to overcome them. We also explores the synergistic potential of combination therapies, integrating PD1/PD-L1 inhibitors with other immunotherapies, targeted therapies, and conventional modalities such as chemotherapy and radiotherapy. In addition, we examine emerging predictive biomarkers for response to PD1/PD-L1 blockade and biomarkers indicative of resistance, providing a foundation for personalized therapeutic approaches. Finally, we outline future research directions, highlighting the need for novel therapeutic strategies, deeper mechanistic insights, and the development of individualized treatment regimens. Our work summarizes the latest knowledge and progress in this field, aiming to provide a valuable reference for improving clinical efficacy and guiding future research on the application of PD1/PD-L1 inhibitors in ccRCC.

A novel LGALS1-depended and immune-associated fatty acid metabolism risk model in acute myeloid leukemia stem cells

Leukemia stem cells (LSCs) are recognized as the root cause of leukemia initiation, relapse, and drug resistance. Lipid species are highly abundant and essential component of human cells, which often changed in tumor microenvironment. LSCs remodel lipid metabolism to sustain the stemness. However, there is no useful lipid related biomarker has been approved for clinical practice in AML prediction and treatment. Here, we constructed and verified fatty acid metabolism-related risk score (LFMRS) model based on TCGA database via a series of bioinformatics analysis, univariate COX regression analysis, and multivariate COX regression analysis, and found that the LFMRS model could be an independent risk factor and predict the survival time of AML patients combined with age. Moreover, we revealed that Galectin-1 (LGALS1, the key gene of LFMRS) was highly expressed in LSCs and associated with poor prognosis of AML patients, and LGALS1 repression inhibited AML cell and LSC proliferation, enhanced cell apoptosis, and decreased lipid accumulation in vitro. LGALS1 repression curbed AML progression, lipid accumulation, and CD8+ T and NK cell counts in vivo. Our study sheds light on the roles of LFMRS (especially LGALS1) model in AML, and provides information that may help clinicians improve patient prognosis and develop personalized treatment regimens for AML.

Noninvasively Deciphering the Immunosuppressive Tumor Microenvironment Using Galectin-1 PET to Inform Immunotherapy Responses

Immune checkpoint blockade (ICB) has achieved groundbreaking results in clinical cancer therapy; however, only a subset of patients experience durable benefits. The aim of this study was to explore strategies for predicting tumor responses to optimize the intervention approach using ICB therapy. Methods: We used a bilateral mouse model for proteomics analysis to identify new imaging biomarkers for tumor responses to ICB therapy. A PET radiotracer was synthesized by radiolabeling the identified biomarker-targeting antibody with 124I. The radiotracer was then tested for PET prediction of tumor responses to ICB therapy. Results: We identified galectin-1 (Gal-1), a member of the carbohydrate-binding lectin family, as a potential negative biomarker for ICB efficacy. We established that Gal-1 inhibition promotes a sensitive immune phenotype within the tumor microenvironment (TME) for ICB therapy. To assess the pre-ICB treatment status of the TME, a Gal-1-targeted PET radiotracer, 124I-αGal-1, was developed. PET imaging with 124I-αGal-1 showed the pretreatment immunosuppressive status of the TME before the initiation of therapy, thus enabling the prediction of ICB resistance in advance. Moreover, the use of hydrogel scaffolds loaded with a Gal-1 inhibitor, thiodigalactoside, demonstrated that a single dose of thiodigalactoside-hydrogel significantly potentiated ICB and adoptive cell transfer immunotherapies by remodeling the immunosuppressive TME. Conclusion: Our study underscores the potential of Gal-1-targeted PET imaging as a valuable strategy for early-stage monitoring of tumor responses to ICB therapy. Additionally, Gal-1 inhibition effectively counteracts the immunosuppressive TME, resulting in enhanced immunotherapy efficacy.

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

Background

One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC.

Methods

In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors.

Results

We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.

Experimental validation and pan-cancer analysis identified COL10A1 as a novel oncogene and potential therapeutic target in prostate cancer

BACKGROUND

Type X collagen (COL10) is a homologous trimeric non-fibrillar collagen found in the extracellular matrix of human tissues, and it exhibits a distinctive white appearance. Type X collagen α1 chain (COL10A1) is a specific cleaved fragment of type X collagen. However, the expression, prognostic significance, clinicopathological attributes and immune-related associations of COL10A1 in prostate cancer as well as in pan-cancer contexts remain poorly understood.

METHODS

Using bioinformatic analysis of data from the most recent databases (TCGA, GTEx and GEO databases), we have extensively elucidated the role played by COL10A1 in terms of its expression patterns, prognostic implications, and immune efficacy across a pan-cancer spectrum. Subsequently, the biological functions of COL10A1 in prostate cancer were elucidated by experimental validation.

RESULTS

Our findings have confirmed that COL10A1 was highly expressed in most cancers and was associated with poorer prognosis in cancer patients. Immune correlation analysis of COL10A1 in various cancers showed its significant correlation with Tumor mutational burden (TMB), microsatellite instability (MSI) and immune cell infiltration. In addition, knockdown of COL10A1 in prostate cancer resulted in a substantial reduction in the proliferation, migration, and invasive potential of prostate cancer cells.

CONCLUSION

Our pan-cancer analysis of COL10A1 gene provided novel insights into its pivotal role in cancer initiation, progression, and therapeutic implications, underscoring its potential significance in prognosis and immunotherapeutic interventions for cancer, particularly prostate cancer.

Single-cell characterization of macrophages in uveal melanoma uncovers transcriptionally heterogeneous subsets conferring poor prognosis and aggressive behavior

Uveal melanoma (UM) is the most frequent primary intraocular malignancy with high metastatic potential and poor prognosis. Macrophages represent one of the most abundant infiltrating immune cells with diverse functions in cancers. However, the cellular heterogeneity and functional diversity of macrophages in UM remain largely unexplored. In this study, we analyzed 63,264 single-cell transcriptomes from 11 UM patients and identified four transcriptionally distinct macrophage subsets (termed MΦ-C1 to MΦ-C4). Among them, we found that MΦ-C4 exhibited relatively low expression of both M1 and M2 signature genes, loss of inflammatory pathways and antigen presentation, instead demonstrating enhanced signaling for proliferation, mitochondrial functions and metabolism. We quantified the infiltration abundance of MΦ-C4 from single-cell and bulk transcriptomes across five cohorts and found that increased MΦ-C4 infiltration was relevant to aggressive behaviors and may serve as an independent prognostic indicator for poor outcomes. We propose a novel subtyping scheme based on macrophages by integrating the transcriptional signatures of MΦ-C4 and machine learning to stratify patients into MΦ-C4-enriched or MΦ-C4-depleted subtypes. These two subtypes showed significantly different clinical outcomes and were validated through bulk RNA sequencing and immunofluorescence assays in both public multicenter cohorts and our in-house cohort. Following further translational investigation, our findings highlight a potential therapeutic strategy of targeting macrophage subsets to control metastatic disease and consistently improve the outcome of patients with UM.

Effects of anoxic prognostic model on immune microenvironment in pancreatic cancer

Pancreatic cancer has one of the worst prognoses in the world, which suggests that the tumor microenvironment, which is characterized by hypoxia and immunosuppression, plays a significant role in the prognosis and progression of pancreatic cancer. We identified PLAU, LDHA, and PKM as key genes involved in pancreatic cancer hypoxia through GO/KEGG enrichment related hypoxia pathways and cox regression, established prognostic models, and studied their relationship to immune invasion through bioinformatics using R and related online databases. We verified the high expression of PLAU, LDHA, and PKM in pancreatic cancer cells using qPCR in vitro, and we also discovered that the expression of PLAU, LDHA, and PKM in hypoxic pancreatic cancer cells differed from that in normal cultured pancreatic cancer cells. Finally, we discovered that our prognostic model accurately predicted postrain in pancreatic cancer patients with hypoxia and immune infiltration.

T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies

T cells are potent at eliminating pathogens and playing a crucial role in the adaptive immune response. T cell receptor (TCR) convergence describes T cells that share identical TCRs with the same amino acid sequences but have different DNA sequences due to codon degeneracy. We conducted a systematic investigation of TCR convergence using single-cell immune profiling and bulk TCRβ-sequence (TCR-seq) data obtained from both mouse and human samples and uncovered a strong link between antigen-specificity and convergence. This association was stronger than T cell expansion, a putative indicator of antigen-specific T cells. By using flow-sorted tetramer+ single T cell data, we discovered that convergent T cells were enriched for a neoantigen-specific CD8+ effector phenotype in the tumor microenvironment. Moreover, TCR convergence demonstrated better prediction accuracy for immunotherapy response than the existing TCR repertoire indexes. In conclusion, convergent T cells are likely to be antigen-specific and might be a novel prognostic biomarker for anti-cancer immunotherapy.

miR-21-5p/PRKCE axis implicated in immune infiltration and poor prognosis of kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC or ccRCC) is the most notorious subtype of renal cell carcinoma for its poor prognosis. Mounting evidence has highlighted the key role of PRKCE in the initiation and development of several types of human cancer, including kidney renal clear cell carcinoma (KIRC). However, the mechanism of PRKCE aberrant expression and the specific clinical correlation of PRKCE expression with immune cell infiltration in KIRC remains elusive. Therefore, we analyzed the relationship between PRKCE and KIRC using many databases, including Oncomine, TCGA, GTEx, TIMER, and GEO. We found that PRKCE decreased in KIRC tumor tissue compared to normal tissue. The Kaplan-Meier Plotter analysis and Univariate and Multivariate Cox analyses were used to evaluate the association between PRKCE and clinicopathological variables and prognosis. Low PRKCE expression was associated with poor survival and histologic grade, T stage, pathologic stage, and M stage. Besides, the C-indexes and calibration plots of the nomogram based on multivariate analysis showed an effective predictive performance for KIRC patients. In addition, PRKCE may be positively correlated with inflammation and negatively correlated with proliferation, metastasis, and invasion as identified by CancerSEA. Moreover, overexpression of PRKCE suppressed ACHN and Caki-1 cell proliferation, migration, and invasion in vitro. Additionally, methylation level data acquired from UALCAN, DiseaseMeth, CCLE, LinkedOmics, and MEXPRESS was used to investigate the relationship between PRKCE expression and PRKCE methylation level. Furthermore, upstream potential miRNA predictions were further performed to explore the mechanism of PRKCE decreased expression in KIRC using multiple online databases available on publicly assessable bioinformatics platforms. High PRKCE methylation levels and hsa-miR-21-5p may contribute to PRKCE low expression in KIRC. Finally, an analysis of immune infiltration indicated that PRKCE was associated with immune cell infiltration. Importantly, PRKCE may affect prognosis partially by regulating immune infiltration in KIRC. In summary, PRKCE may serve as a novel prognostic biomarker reflecting immune infiltration level and a novel therapeutic target in KIRC.

Construction and verification of a hypoxia-related nine-gene prognostic model in uveal melanoma based on integrated single-cell and bulk RNA sequencing analyses

Uveal melanoma (UM) is the most common primary intraocular tumor with high metastasis and poor prognosis among adults. Hypoxia participates in the metastasis process in various types of cancers. It is reported that the increased expression of hypoxia inducible factor 1 alpha subunit (HIF1A), a hypoxia-related molecule, is associated with worse prognoses of UM patients. Based on the integrated analysis of single-cell sequencing (scRNA-seq) dataset from Gene Expression Omnibus (GEO) and bulk RNA-seq dataset from the Cancer Genome Atlas (TCGA), we found hypoxia was the key feature in UM progression and identified 47 common hypoxia-related differentially expressed genes (DEGs) for the following research. Univariate cox analysis and LASSO-Cox regression analysis were performed to establish a nine-gene prognostic model. According to this model, UM patients could be divided into high- and low-risk groups, with a significant difference in overall survival and progression free survival between the two groups (P < 0.001). The accuracy of the predictive model was also verified on two other independent datasets. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hypoxia-related DEGs were enriched in immune and cancer related pathways. The proportion of immune infiltration and the expression of immune biomarkers were different between high- and low-risk UM patients, providing potential targets for UM immunotherapy. Hence, our hypoxia-related nine-gene model could efficiently predict the prognosis and guide personalized therapies for UM patients.

The Bioinformatical Identification of Potential Biomarkers in Heart Failure Diagnosis and Treatment

Background

Heart failure (HF) is defined as the inability of the heart's systolic and diastolic function to properly discharge blood flow from the veins to the heart. The goal of our research is to look into the possible mechanism that causes HF.

Methods

The GSE5406 database was used for screening the differentially expressed genes (DEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) network were applied to analyze DEGs. Besides, cell counting Kit-8 (CCK-8) was conducted to observe the knockdown effect of hub genes on cell proliferation.

Results

Finally, 377 upregulated and 461 downregulated DEGs came out, enriched in the extracellular matrix organization and gap junction. According to GSEA results, Hoft cd4 positive alpha beta memory t cell bcg vaccine age 18–45 yo id 7 dy top 100 deg ex vivo up, Sobolev t cell pandemrix age 18–64 yo 7 dy dn, and so on were significantly related to gene set GSE5406. 7 hub genes, such as COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3 and MAPK1, were selected from PPI networks. CCK-8 indicated silencing of STAT3 promoted the proliferation of H9C2 cells and silencing of UBB inhibited the proliferation of H9C2 cells.

Conclusion

Our analysis reveals that COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3, and MAPK1 might promote the progression of HF and become the biomarkers for diagnosis and treatment of HF.

Pan-Cancer Analysis Revealed SRSF9 as a New Biomarker for Prognosis and Immunotherapy

Background. Serine/arginine-rich splicing factor 9 (SRSF9) is one of the members of SRSF gene family and related to the tumorigenesis and the progression of tumor. However, whether SRSF9 has a crucial role across pan-cancer is still unknown. Methods. In this study, we used public databases, such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx), to analyze SRSF9 expression level among tumor and normal cells. Survival analysis, K-M plotter, and PrognoScan were used to analyze the prognosis value of SRSF9, regarding to overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Moreover, we performed the correlation between SRSF9 and clinical characteristics (including the outcome of prognosis), as well as molecular events of tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint gene, tumor microenvironment (TME), immune infiltrating cells, mismatch repair (MMR) genes, m6A genes, DNA methyltransferases, and neoantigen with bioinformatics methods and TISIDB, TIMER, and Sangerbox websites. Results. In general, SRSF9 expression was upregulated in most cancers, such as BLCA, CHOL, and UCEC, which SRSF9 was associated with short survival and severe progression. In COAD, STAD, and UCEC, SRSF9 expression was positively related to both TMB and MSI. In BRCA, BLCA, ESCA, GBM, HNSC, LUSC, LUAD, OV, PRAD, TGCT, THCA, and UCEC, both immune score and stomal score showed a negative relationship with SRSF9 expression. Immune score showed a positive relationship with SRSF9 expression in LGG. SRSF9 expression had a significant and positive correlation with six types of immune infiltration cells in LGG, KIRC, LIHC, PCPG, PRAD, SKCM, THCA, and THYM, except in LUSC. In LIHC, SRSF9 was highly significant correlated with most immune checkpoint genes. For neoantigens, correlation between SRSF9 and the quantity of neoantigens was significantly positive in some cancer types. SRSF9 was also correlated with MMR genes, m6A genes, and DNA methyltransferases. In the 33 cancer types, gene set enrichment analysis (GSEA) demonstrated that SRSF9 was correlated with multiple functions and signaling pathways. Conclusion. These findings demonstrated that SRSF9 may be a new biomarker for the prognosis and immunotherapy in various cancers. As a result, it will be beneficial to provide new therapies for cancer patients, thereby improving the treatment and prognosis of cancer patients.

Targeted Mass Spectrometry Enables Multiplexed Quantification of Immunomodulatory Proteins in Clinical Biospecimens

Immunotherapies are revolutionizing cancer care, producing durable responses and potentially cures in a subset of patients. However, response rates are low for most tumors, grade 3/4 toxicities are not uncommon, and our current understanding of tumor immunobiology is incomplete. While hundreds of immunomodulatory proteins in the tumor microenvironment shape the anti-tumor response, few of them can be reliably quantified. To address this need, we developed a multiplex panel of targeted proteomic assays targeting 52 peptides representing 46 proteins using peptide immunoaffinity enrichment coupled to multiple reaction monitoring-mass spectrometry. We validated the assays in tissue and plasma matrices, where performance figures of merit showed over 3 orders of dynamic range and median inter-day CVs of 5.2% (tissue) and 21% (plasma). A feasibility study in clinical biospecimens showed detection of 48/52 peptides in frozen tissue and 38/52 peptides in plasma. The assays are publicly available as a resource for the research community.

The Stromal and Immune Landscape of Nasopharyngeal Carcinoma and Its Implications for Precision Medicine Targeting the Tumor Microenvironment

The evolution of the tumor microenvironment (TME) is a cancer-dependent and dynamic process. The TME is often a complex ecosystem with immunosuppressive and tumor-promoting functions. Conventional chemotherapy and radiotherapy, primarily focus on inducing tumor apoptosis and hijacking tumor growth, whereas the tumor-protective microenvironment cannot be altered or destructed. Thus, tumor cells can quickly escape from extraneous attack and develop therapeutic resistance, eventually leading to treatment failure. As an Epstein Barr virus (EBV)-associated malignancy, nasopharyngeal carcinoma (NPC) is frequently infiltrated with varied stromal cells, making its microenvironment a highly heterogeneous and suppressive harbor protecting tumor cells from drug penetration, immune attack, and facilitating tumor development. In the last decade, targeted therapy and immunotherapy have emerged as promising options to treat advanced, metastatic, recurrent, and resistant NPC, but lack of understanding of the TME had hindered the therapeutic development and optimization. Single-cell sequencing of NPC-infiltrating cells has recently deciphered stromal composition and functional dynamics in the TME and non-malignant counterpart. In this review, we aim to depict the stromal landscape of NPC in detail based on recent advances, and propose various microenvironment-based approaches for precision therapy.

Targeting galectins in T cell-based immunotherapy within tumor microenvironment

Over the past few years, tumor immunotherapy has emerged as an innovative tumor treatment and owned incomparable advantages over other tumor therapy. With unique complexity and uncertainty, immunotherapy still need helper to apply in the clinic. Galectins, modulated in tumor microenvironment, can regulate the disorders of innate and adaptive immune system resisting tumor growth. Considering the role of galectins in tumor immunosuppression, combination therapy of targeted anti-galectins and immunotherapy may be a promising tumor treatment. This brief review summarizes the expression and immune functions of different galectins in tumor microenvironment and discusses the potential value of anti-galectins in combination with checkpoint inhibitors in tumor immunotherapy.

The effect of CCL5 on the immune cells infiltration and the prognosis of patients with kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is the most representative subtype of renal cancer. Immune infiltration was associated with the survival time of patients with tumors. C-C chemokine ligand 5 (CCL5) can promote the malignant process of tumor and be related to infiltration immune cells in some cancers, but not reported in KIRC. Methods: The expression profile and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. The correlation between the expression level of CCL5 and clinical features in KIRC was analyzed. Gene Set Enrichment Analysis (GSEA) was utilized to explore the functions and pathways of CCL5 in KIRC. Then, the analysis between the survival and immune infiltration cells was carried out, as well as the non-parametric tests between the CCL5 expression and the ratios of immune infiltration cells. Results: The correlations between the expression levels of CCL5 in KIRC and clinical features including survival time, pathological stage, grade, and status of the patient, have been identified. Meanwhile, GSEA analysis has shown relationships between the expression of CCL5 and immune pathways. The immune infiltrated cells were correlated with the prognosis of KIRC, especially regulatory T cells (Tregs), mast cells, and dendritic cells. And Tregs was associated with the CCL5 expression. Conclusion: The increased expression of CCL5 is related to poor prognosis and clinical features. Meanwhile, CCL5 is related to Tregs ratios and CCL5 may act as a typical chemokine to recruit Tregs in KIRC. CCL5 could be used as a biomarker for the prognosis prediction and a potential therapeutic target for patients with KIRC.

Identification of a Glucose Metabolism-related Signature for prediction of Clinical Prognosis in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent and invasive histological subtypes among all renal cell carcinomas (RCC). Cancer cell metabolism, particularly glucose metabolism, has been reported as a hallmark of cancer. However, the characteristics of glucose metabolism-related gene sets in ccRCC have not been systematically profiled.

Methods: In this study, we downloaded a gene expression profile and glucose metabolism-related gene set from TCGA (The Cancer Genome Altas) and MSigDB, respectively, to analyze the characteristics of glucose metabolism-related gene sets in ccRCC. We used a multivariable Cox regression analysis to develop a risk signature, which divided patients into low- and high- risk groups. In addition, a nomogram that combined the risk signature and clinical characteristics was created for predicting the 3- and 5-year overall survival (OS) of ccRCC. The accuracy of the nomogram prediction was evaluated using the area under the receiver operating characteristic curve (AUC) and a calibration plot.

Results: A total of 231 glucose metabolism-related genes were found, and 68 differentially expressed genes (DEGs) were identified. After screening by univariate regression analysis, LASSO regression analysis and multivariable Cox regression analysis, six glucose metabolism-related DEGs (FBP1, GYG2, KAT2A, LGALS1, PFKP, and RGN) were selected to develop a risk signature. There were significant differences in the clinical features (Fuhrman nuclear grade and TNM stage) between the high- and low-risk groups. The multivariable Cox regression indicated that the risk score was independent of the prognostic factors (training set: HR=3.393, 95% CI [2.025, 5.685], p<0.001; validation set: HR=1.933, 95% CI [1.130, 3.308], p=0.016). The AUCs of the nomograms for the 3-year OS in the training and validation sets were 0.808 and 0.819, respectively, and 0.777 and 0.796, respectively, for the 5- year OS.

Conclusion: We demonstrated a novel glucose metabolism-related risk signature for predicting the prognosis of ccRCC. However, additional in vitro and in vivo research is required to validate our findings.