Immune signature of T follicular helper cells predicts clinical prognostic and therapeutic impact in lung squamous cell carcinoma

Xu, Feng; Zhang, Hongpan; Chen, Jiexin; Lin, Ling; Chen, Yongsong

doi:10.1016/j.intimp.2019.105932

Cited by 64 publications

(47 citation statements)

References 33 publications

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“…30 We used the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and subclass mapping to predict clinical responses to immune checkpoint inhibitors as previously described. 31,32 As chemotherapy is commonly used to treat NSCLC, we used the R package "pRRophetic" to estimate the chemotherapeutic response determined by the half maximal inhibitory concentration (IC50) of each LUSC patient on the Genomics of Drug Sensitivity in Cancer (GDSC) website. 33…”

Section: Immunotherapeutic and Chemotherapeutic Response Predictionmentioning

confidence: 99%

A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

Lin

et al. 2020

OncoImmunology

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106

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The tumor-suppressor gene tumor protein p53 (TP53) is one of the most commonly mutated genes in human lung cancer, and TP53 mutations are associated with a worsened prognosis and causes resistance to cancer therapy. RNA sequencing and TP53 mutation data were downloaded to determine specific TP53associated signature based on differentially expressed genes between patients with lung squamous cell carcinoma (LUSC) with wild type (TP53 WT) and mutated (TP53 MUT) TP53. We investigated the predictive value of this signature on the immune microenvironment, tumor mutational burden (TMB), and likelihood of response to immunotherapy and chemotherapy. In total, 1,556 differentially expressed genes were identified based on TP53 mutation status. Three genes (KLK6, MUC22 and CSN1S1) identified by univariate and multivariate Cox regression analyses, comprised the prognostic signature which was an independent and specific prognostic marker of overall survival in patients with LUSC. A nomogram was also established to validate this signature for clinical use. Moreover, the high-risk group was characterized by increased infiltration of monocytes and macrophages M1, and decreased T cells CD8 and T cells follicular helper. Highrisk group exhibited a higher TMB, and was much more sensitive to immunotherapy and chemotherapy. KLK6 and CSN1S1 expression and the prognostic prediction values were further validated in clinical samples. The derived TP53-associated signature is a specific and independent prognostic biomarker for LUSC patients, and could provide potential prognostic biomarker or therapeutic targets for the development of novel immunotherapies and chemotherapies.

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Section: Immunotherapeutic and Chemotherapeutic Response Predictionmentioning

confidence: 99%

A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

Lin

et al. 2020

OncoImmunology

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106

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“…Many researchers used CIBERSORT for estimating the in ltration of immune cells, and there is no differences between using CIBERSORT on microarray datasets (Zhou et al 2018;Stahl et al 2019;Xu et al 2019) and RNA sequencing datasets (Yazdani et al 2018;Chaumette et al 2020;Diaz-Mejia et al 2019), even on both microarray datasets and RNA sequencing datasets (Wang et al 2020). Meanwhile, it has been reported that CIBERSORT has been used in several previous studies to analyzed the TIICs in the renal cell carcinoma, which have identi ed that CD8+ T cells were associated with prolonged overall survival and the potential biomarker relaed to CD8+ T cells, respectively (Zhang et al 2019;Lin et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Gene expression-based immune infiltration analyses of renal cancer and their associations with survival outcome

Chen

Yin

et al. 2021

Preprint

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Background: Renal cancer is a common malignant tumor with an increasing incidence rate. Methods: In this study, based on the gene expression profiles, we analyzed the compositions of tumor-infiltrating immune cells (TIICs) in renal cancer and paracancerous samples using CIBERSORT. The proportions of 22 TIICs subsets in 122 paired renal carcinoma and paracancerous samples, and 224 Wilms tumor (WT) samples varied between intragroup and intergroup. Results: After analyzed the difference of TIICs composition between renal cancer and paired paracancerous samples, we found that M0 macrophages and CD8 T cells were significantly elevated, while naive B cells were significantly decreased in renal cancer samples compared with paracancerous samples. Survival analysis showed that high overall TIICs proportion , the low proportion of resting mast cells and the high proportion of activated memory CD4 T cells were associated with poor prognosis of renal cancer patients. In addition, 3 clusters were identified by hierarchical clustering analysis, and they presented a distinct prognosis. Cluster 1 had superior survival outcomes, while cluster 2 had an inferior survival outcome. Conclusions: Our study indicated that overall TIICs proportion, certain TIICs subset proportion, including resting mast cells and activated memory CD4 T cells, and distinct cluster patterns were associated with the prognosis of renal cancer, which was significant for the clinical surveillance and treatment of renal cancer.

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“…This gene can also act as an oncogenic factor in breast cancer [34]. Moreover, its biological roles in liver cancer [35], head and neck squamous cell carcinoma [13], and lung cancer [36] were determined. Finally, IGF2BP3 plays significant biological roles in thyroid cancer [37], breast cancer [38], gastric cancer [39], and colorectal cancer [40].…”

Section: Discussionmentioning

confidence: 99%

Multiomics profile and prognostic gene signature of m6A regulators in uterine corpus endometrial carcinoma

et al. 2020

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Uterine corpus endometrial carcinoma (UCEC) is the most common type of gynecologic malignancy worldwide. Despite advances in the treatments of UCEC, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer pathogenesis and progression. Thus, we aimed to identify the landscape of m6A regulators and build a prognostic gene signature in UCEC. In this study, we first analyzed copy number variations (CNVs), single nucleotide variations (SNVs) and gene expression profiles as well as matched clinical information of UCEC patients from The Cancer Genome Atlas (TCGA) database. Next, we determined that CNVs in m6A regulatory genes had a significant negative impact on patient survival. The mRNA expression levels of a total of 16 m6A regulators were significantly correlated with different CNV patterns. Using univariate Cox regression analysis, IGF2BP1, KIAA1429, IGF2BP3, YTHDF3, and IGF2BP2 were found to be closely associated with UCEC patient survival outcomes. Based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression models, we built a 3-gene (IGF2BP3, KIAA1429 and IGF2BP1) signature of m6A regulators with prognostic value in UCEC that could effectively predict patient prognosis (log-rank test p-value < 0.0001). In addition, risk scores were significantly different between patients stratified by tumor stage, SNV, and CNV. Multivariate Cox regression analysis suggested that risk score might be an independent prognostic indicator for the overall survival of patients with UCEC (p-value < 0.05). Gene enrichment analysis indicated that high IGF2BP1 gene expression is associated with cytoplasmic stress granules. KIAA1429 gene expression is associated with cellular nucleic acid metabolism. The expression of the IGF2BP3 gene is associated with RNA binding processes. In conclusion, we determined that genetic alterations in m6A regulatory genes could be effective and reliable biomarkers for UCEC prognosis prediction.

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Immune signature of T follicular helper cells predicts clinical prognostic and therapeutic impact in lung squamous cell carcinoma

Cited by 64 publications

References 33 publications

A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

Gene expression-based immune infiltration analyses of renal cancer and their associations with survival outcome

Multiomics profile and prognostic gene signature of m6A regulators in uterine corpus endometrial carcinoma

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