Immune infiltration of tumors is closely associated with clinical outcome in renal cell carcinoma (RCC). Tumor‐infiltrating immune cells (TIICs) regulate cancer progression and are appealing therapeutic targets. The purpose of this study was to determine the composition of TIICs in RCC and further reveal the independent prognostic values of TIICs. CIBERSORT, an established algorithm, was applied to estimate the proportions of 22 immune cell types based on gene expression profiles of 891 tumors. Cox regression was used to evaluate the association of TIICs and immune checkpoint modulators with overall survival (OS). We found that CD8+ T cells were associated with prolonged OS (hazard ratio [HR] = 0.09, 95% confidence interval [CI].01‐.53; P = 0.03) in chromophobe carcinoma (KICH). A higher proportion of regulatory T cells was associated with a worse outcome (HR = 1.59, 95% CI 1.23‐.06; P < 0.01) in renal clear cell carcinoma (KIRC). In renal papillary cell carcinoma (KIRP), M1 macrophages were associated with a favorable outcome (HR = .43, 95% CI .25‐.72; P < 0.01), while M2 macrophages indicated a worse outcome (HR = 2.55, 95% CI 1.45‐4.47; P < 0.01). Moreover, the immunomodulator molecules CTLA4 and LAG3 were associated with a poor prognosis in KIRC, and IDO1 and PD‐L2 were associated with a poor prognosis in KIRP. This study indicates TIICs are important determinants of prognosis in RCC meanwhile reveals potential targets and biomarkers for immunotherapy development.

Messenger RNA vaccines are considered to be a promising strategy in cancer immunotherapy, while their application on mesothelioma is still largely uncharacterized. This study aimed to identify potential antigens in mesothelioma for anti-mesothelioma mRNA vaccine development, and further determine the immune subtypes of mesothelioma for selection of suitable candidates from an extremely heterogeneous population. Gene expression data and corresponding clinicopathological information were obtained from the TCGA and gene expression omnibus, respectively. Then, the genetic alterations were compared and visualized using cBioPortal, and differentially expressed genes and their prognostic signatures were identified by GEPIA. The relationship between tumor-infiltrating immune cells and the expression of tumor antigens was systematically evaluated by TIMER online. Finally, the immune subtypes and immune landscape of mesothelioma were separately analyzed using consensus cluster and graph learning-based dimensional reduction. A total of five potential tumor antigens correlated with prognosis and infiltration of antigen-presenting cells, including AUNIP, FANCI, LASP1, PSMD8, and XPO5 were identified. Based on the expression of immune-related genes, patients with mesothelioma were divided into two immune subtypes (IS1 and IS2). Each subtype exhibited differential molecular, cellular and clinical properties. Patients with the IS1 subtype were characterized by an immune “cold” phenotype, displaying superior survival outcomes, whereas those with the IS2 subtype were characterized by an immune “hot” and immunosuppressive phenotype. Furthermore, immune checkpoints and immunogenic cell death modulators were differentially expressed between the IS1 and IS2 immune subtype tumors. The immunogenomic landscape of mesothelioma revealed a complex tumor immune microenvironment between individual patients. AUNIP, FANCI, LASP1, PSMD8, and XPO5 are putative antigens for the development of anti-mesothelioma mRNA vaccine and patients with the IS1 subtype may be considered for vaccination.

The “writers” of four types of adenosine (A)-related RNA modifications (N6-methyladenosine, N1-methyladenosine, alternative polyadenylation, as well as A-to-inosine RNA editing) are closely related to the tumorigenesis and progression of many cancer types, including skin cutaneous melanoma (SKCM). However, the potential roles of the crosstalk between these RNA modification “writers” in the tumor microenvironment (TME) remain unclear. The RNA modification patterns were identified using an unsupervised clustering method. Subsequently, based on differentially expressed genes responsible for the aforementioned RNA modification patterns, an RNA modification “writer” scoring model (W_Score) was constructed to quantify the RNA modification-associated subtypes in individual patients. Moreover, a correlation analysis for W_Score and the TME characteristics, clinical features, molecular subtypes, drug sensitivities, immune responses, and prognosis was performed. We identified three RNA modification patterns, corresponding to distinct tumor immune microenvironment characteristics and survival outcomes. Based on the W_Score score, which was extracted from the RNA modification-related signature genes, patients with SKCM were divided into high- and low-W_Score groups. The low-W_Score group was characterized by better survival outcomes and strengthened immunocyte infiltration. Further analysis showed that the low-W_Score group was positively associated with higher tumor mutation burden and PD-L1 expression. Of note, two immunotherapy cohorts demonstrated that patients with low W_Score exhibited long-term clinical benefits and an enhanced immune response. This study is the first to systematically analyze four types of A-related RNA modifications in SKCM, revealing that these “writers” essentially contribute to TME complexity and diversity. We quantitatively evaluated the RNA modification patterns in individual tumors, which could aid in developing personalized immunotherapy strategies for patients.

Four types of A-related RNA modification regulators interact with each other and even the crosstalk between the regulators could characterize the tumor immune microenvironment infiltration patterns, chemosensitivity, and cancer prognosis in patients with pan-cancer.