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Objective To investigate the impact of PD1-TCF1+CD8+ stem-like memory T cells on immunotherapy prognosis and tertiary lymphoid structure in tumors. Methods Pathological tissue sections were collected from 33 patients treated with immunotherapy, 18 cases of NSCLC and 15 cases of ESCC. The expression of PD1-TCF1+CD8+T cells was detected through quantitative analysis by multiplex immunofluorescence. Survival curves were described by the Kaplan-Meier method. Pearson's correlation test was used for correlation analysis. Results The high levels of PD1-TCF1+CD8+T cells had a better PFS in NSCLC and ESCC cohorts. In the NSCLC cohort, high levels of PD1-TCF1+CD8+ T cells were significantly and positively correlated with the number (P=0.0151) and size (P=0.0007) of TLSs. Conclusion In patients with NSCLC and ESCCs, high PD1-TCF1+CD8+ stem-like memory T cell expression indicates improved prognosis and immune response and is associated with the formation of TLSs in the tumor microenvironment of NSCLC.

Objective To explore the effect of peripheral blood markers on the efficacy and prognosis of patients with advanced esophageal cancer treated with immune checkpoint inhibitors (ICIs). Methods The case data of 61 patients with advanced esophageal cancer who met the inclusion criteria were collected. Data on clinical indicators and peripheral blood markers as well as objective response rate (ORR) and progression-free-survival (PFS) were obtained. Results The median PFS of the included patients was 7.10 months (95%CI: 5.12-9.07). The ORR of patients with baseline lactate dehydrogenase (LDH) < 201 was better than that of patients with LDH≥201 (P < 0.05). Univariate analysis showed that baseline LDH0 < 201, neutrophil to lymphocyte ratio (NLR) < 3.9, platelet-to-lymphocyte ratio (PLR) < 240.3, and LDH1 < 249.0 two weeks after ICI treatment were significantly associated with significant improvement in PFS (P < 0.05). In multivariate analysis, patients with NLR0 < 3.9 had longer PFS (P < 0.05). Conclusion LDH0 < 201, NLR0 < 3.9, PLR0 < 240.3, and LDH1 < 249.0 are positively correlated with the prognosis of patients with advanced esophageal cancer treated with ICIs.

Lung squamous cell carcinoma (LUSC) is associated with a worse prognosis than other histological subtypes of non-small cell lung cancer. Due to the vital role of CD8+ T cells in anti-tumor immunity, the characterization of CD8+ T cell infiltration-related (CTLIR) gene signature in LUSC is worthy of in-depth exploration. In our study, tumor tissues of LUSC patients from Renmin Hospital of Wuhan University were stained by multiplex immunohistochemistry to evaluate the density of infiltrated CD8+ T cells and explore the correlation with immunotherapy response. We found that the proportion of LUSC patients who responded to immunotherapy was higher in the high density of CD8+ T cell infiltration group than in the low density of CD8+ T cell infiltration group. Subsequently, we collected bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) database. The abundance of infiltrating immune cells in LUSC patients was analyzed by using CIBERSORT algorithm, and weighted correlation network analysis was performed to identify the co-expressed gene modules related to CD8+ T cells. We then developed a prognostic gene signature based on CD8+ T cell co-expressed genes and calculated the CTLIR risk score, which stratified LUSC patients into high-risk and low-risk groups. With univariate and multivariate analyses, the gene signature was identified as an independent prognostic factor in LUSC patients. The overall survival of LUSC patients in the high-risk group was significantly shorter than that of the low-risk group in the TCGA cohort, which was validated in Gene Expression Omnibus datasets. We analyzed immune cell infiltration in the tumor microenviroment and found fewer CD8+ T cells and more regulatory T cell infiltration in the high-risk group, which is characterized as an immunosuppressive phenotype. Furthermore, the LUSC patients in the high-risk group were predicted to have a better response to immunotherapy than those in the low-risk group when treated with PD-1 and CTLA4 inhibitors. In conclusion, we performed a comprehensive molecular analysis of the CTLIR gene signature in LUSC and constructed a risk model for LUSC patients to predict prognosis and immunotherapy response. [ABSTRACT FROM AUTHOR]

Accumulating data show that N6-methyladenosine (m6A) methyltransferase METTL3 and long noncoding RNA MALAT1 act pivotal roles in multiple malignancies including prostate cancer (PCa). However, the role and molecular mechanism underlying METTL3-mediated m6A modification of MALAT1 in PCa remain undocumented. The association of METTL3 and MALAT1 expression with clinicopathological characteristics and prognosis in patients with PCa was analyzed by quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, and public The Cancer Genome Atlas (TCGA) dataset. The in vitro and in vivo experiments were executed to investigate the role of METTL3 in PCa. m6A dot blot, methylated RNA immunoprecipitation (MeRIP), RIP, and qRT-PCR assays were employed to observe METTL3-mediated m6A modification of MALAT1. The effects of METTL3 on MALAT1-mediated PI3K/AKT pathway were assessed by Western blot analysis. As a result, we found that METTL3 was significantly upregulated in PCa tissues and high expression of METTL3 was associated with Gleason score and tumor recurrence in patients with PCa. Knockdown of METTL3 markedly repressed growth and invasion of PCa cells in vitro and in vivo, whereas ectopic expression of METTL3 showed the opposite effects. Moreover, knockdown of METTL3 decreased the total m6A levels of PCa cells as well as the MALAT1 m6A levels, leading to reduced MALAT1 expression. Overexpression of MALAT1 reversed METTL3 knockdown-induced antitumor effects and PI3K/AKT signaling inactivation. MALAT1 harbored a positive correlation with METTL3 expression and tumor recurrence in PCa. In conclusion, our findings demonstrate that METTL3-mediated m6A modification of lncRNA MALAT1 promotes growth and invasion of PCa cells by activating PI3K/AKT signaling. [ABSTRACT FROM AUTHOR]

Background: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms., Methods: Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms. Associations between hub genes and signaling pathways, disease genes, immune cell infiltration, drug sensitivity, and prognostic predictions were explored via multi-omics analysis. Hub gene expression was validated through HPA and CCLE. Multiple algorithms pinpointed Cancer-Associated Fibroblasts genes (CAFs), with ten machine-learning methods generating CAFs scores for prognosis. Model gene expression was validated at the single-cell level using the TISCH database., Results: We identified 201 upregulated and 935 downregulated DEGs. Three hub genes, namely CDH6, EGFLAM, and RASGRF2, were unveiled. These genes are implicated in diverse disease-related signaling pathways. Additionally, they exhibited significant correlations with disease-associated gene expression, immune cell infiltration, and drug sensitivity. Exploration of the HPA and CCLE databases exposed substantial expression variations across patients and cell lines for these genes. Subsequently, we identified CAFs-associated genes and established a robust prognostic model. The analysis in the TISCH database showed that the genes in this model were highly expressed in CAFs., Conclusions: The results unveil an association between CDH6, EGFLAM, and RASGRF2 and the immunotherapeutic response in gastric cancer. These genes hold potential as predictive biomarkers for gastric cancer immunotherapy resistance and prognostic assessment.

Lung squamous cell carcinoma (LUSC) is associated with a worse prognosis than other histological subtypes of non-small cell lung cancer. Due to the vital role of CD8 + T cells in anti-tumor immunity, the characterization of CD8 + T cell infiltration-related (CTLIR) gene signature in LUSC is worthy of in-depth exploration. In our study, tumor tissues of LUSC patients from Renmin Hospital of Wuhan University were stained by multiplex immunohistochemistry to evaluate the density of infiltrated CD8 + T cells and explore the correlation with immunotherapy response. We found that the proportion of LUSC patients who responded to immunotherapy was higher in the high density of CD8 + T cell infiltration group than in the low density of CD8 + T cell infiltration group. Subsequently, we collected bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) database. The abundance of infiltrating immune cells in LUSC patients was analyzed by using CIBERSORT algorithm, and weighted correlation network analysis was performed to identify the co-expressed gene modules related to CD8 + T cells. We then developed a prognostic gene signature based on CD8 + T cell co-expressed genes and calculated the CTLIR risk score, which stratified LUSC patients into high-risk and low-risk groups. With univariate and multivariate analyses, the gene signature was identified as an independent prognostic factor in LUSC patients. The overall survival of LUSC patients in the high-risk group was significantly shorter than that of the low-risk group in the TCGA cohort, which was validated in Gene Expression Omnibus datasets. We analyzed immune cell infiltration in the tumor microenviroment and found fewer CD8 + T cells and more regulatory T cell infiltration in the high-risk group, which is characterized as an immunosuppressive phenotype. Furthermore, the LUSC patients in the high-risk group were predicted to have a better response to immunotherapy than those in the low-risk group when treated with PD-1 and CTLA4 inhibitors. In conclusion, we performed a comprehensive molecular analysis of the CTLIR gene signature in LUSC and constructed a risk model for LUSC patients to predict prognosis and immunotherapy response., (© 2023. The Author(s).)