An immune cell infiltration-based immune score model predicts prognosis and chemotherapy effects in breast cancer.

Background: Immune cells have essential auxiliary functions and influence clinical outcomes in cancer, with high immune infiltration being associated with improved clinical outcomes and better response to treatment in breast cancer (BC). However, studies to date have not fully considered the tumor-infiltrating immune cell (TIIC) landscape in tumors. This study investigated potential biomarkers based on TIICs to improve prognosis and treatment effect in BC. Results: We enrolled 5112 patients for analysis and used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT), a new computational algorithm, to quantify 22 TIICs in primary BC. From the results of univariate Cox regression, 12 immune cells were determined to be significantly related to the overall survival (OS) of BC patients. Furthermore, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to construct an immune prognostic model based on six potential biomarkers. By dividing patients into low- and high-risk groups, a significant distinction in OS was found in the training cohort, with 20-year survival rates of 42.6% and 26.3%, respectively. Applying a similar protocol to validation and test cohorts, we found that OS was significantly shorter in the high-risk group than in the low-risk group, regardless of the molecular subtype of BC. Using the immune score model to predict the effect of BC patients to chemotherapy, the survival advantage for the low-risk group was evident among those who received chemotherapy, regardless of the chemotherapy regimen. In evaluating the predictive value of the nomogram, a decision curve showed better predictive accuracy than the standard tumor-node-metastasis (TNM) staging system. Conclusion: The immune cell infiltration-based immune score model can be effectively and efficiently used to predict the prognosis of BC patients as well as the effect of chemotherapy.

Sui S ，An X ，Xu C ，Li Z ，Hua Y ，Huang G ，Sui S ，Long Q ，Sui Y ，Xiong Y ，Ntim M ，Guo W ，Chen M ，Deng W ，Xiao X ，Li M ... -  《Theranostics》

Predicting the immune landscape of invasive breast carcinoma based on the novel signature of immune-related lncRNA.

The composition of the population of immune-related long non-coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma. The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA-Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5-year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut-off point to establish the optimal model for distinguishing high-risk or low-risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut-off point, re-evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy. After re-assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome. The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti-tumor immunotherapy.

Shen S ，Chen X ，Hu X ，Huo J ，Luo L ，Zhou X ... -  《Cancer Medicine》

A stromal and immune cell infiltration-based score model predicts prognosis and chemotherapy effect in colorectal cancer.

The stromal and immune cells crosstalk with cancer cells in tumor microenvironment, but few studies have fully considered the overall landscape of the infiltrating stromal and immune cells in colorectal cancer. We enrolled 1836 colorectal cancer patients and divided them into the training, validation and test cohorts. 64 stromal and immune cells were quantified in each primary colorectal cancer tissue by estimating gene expression data using xCell algorithm. Univariate, LASSO and multivariate Cox regression analyses were subsequently employed to establish a stromal and immune score prognostic model based on 13 potential cell biomarkers. Patients of the three cohorts were divided into the high- and low-risk groups according to the cutoff value. Compared with the low-risk group, high-risk group showed significant shorter survival, worse clinicopathologic outcomings, higher cancer-related expressions and more active epithelial-mesenchymal transformation. 5-Fu and FUFOL chemotherapy regimens made the low-risk patients gain significant survival advantage, while none chemotherapy regimens benefited the high-risk group, which may benefit from immune checkpoint inhibitors. The nomogram combining the stromal and immune score with standard TNM staging system showed better predictive accuracy than TNM stage alone. The stromal and immune cell infiltration-based score model can effectively and efficiently predict the prognosis and chemotherapy effect in colorectal cancer.

Luo Q ，He F ，Cao J 《-》

Identification of the prognostic value of ferroptosis-related gene signature in breast cancer patients.

Wang D ，Wei G ，Ma J ，Cheng S ，Jia L ，Song X ，Zhang M ，Ju M ，Wang L ，Zhao L ，Xin S ... -  《BMC CANCER》

A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer.

Li J ，Li X ，Zhang C ，Zhang C ，Wang H ... -  《-》