Aims: Evaluation of nucleic acids in plasma exosomes is a noninvasive method that can be used to detect different types of cancer. The aim of this study was to determine the value of exosomal long noncoding RNAs (lncRNAs) in detecting lung squamous cell carcinoma (LSCC). Materials and Methods: A total of 75 LSCC patients and 79 negative control subjects were enrolled in the study. Twenty differentially expressed lncRNAs were evaluated as potential candidates. Exosomes were isolated by ultracentrifugation, and lncRNA levels in exosomes were determined using real-time polymerase chain reaction. Receiver Operating Characteristic (ROC) curves were used to determine specificity and sensitivity. Results: Exosomal SOX2-OT was significantly upregulated in LSCC patients and showed the strongest power in detecting LSCC. The area under the ROC curve was 0.815, and the sensitivity and specificity were 76% and 73.17%, respectively. Moreover, exosomal SOX2-OT levels were significantly correlated with tumor size, TNM stage, and lymph node metastasis. Exosomal SOX2-OT levels were significantly decreased in the postoperative plasma of LSCC patients. Conclusion: SOX2-OT may serve as a promising noninvasive plasma-based tumor biomarker for LSCC.

Colorectal cancer (CRC) is one of the most prevalent tumors worldwide. Recently, long noncoding RNAs (lncRNAs) have been recognized as key regulators in postgenomic biology. Numerous lncRNAs have been identified as diagnostic biomarkers and therapeutic targets. However, the molecular mechanisms underlying the role of lncRNAs in CRC progression are not fully understood. Differentially expressed lncRNAs and messenger RNAs were investigated using a microarray approach in five paired primary CRC tumor tissues and the corresponding nontumor tissues and confirmed in an additional 116 paired tissues and 21 inflammatory bowel disease tissues and 15 adjacent normal tissues by a quantitative real‐time polymerase chain reaction. We also performed comprehensive transcriptome profiling analysis on Gene Expression Omnibus and The Cancer Genome Atlas datasets. We identified LINC02595 and evaluated its clinical significance as a plasma biomarker. The function of LINC02595 was evaluated using a panel of in vivo and vitro assays, including cell counting kit‐8, colony formation, cell cycle, apoptosis, RNA fluorescence in situ hybridization, luciferase reporter, immunohistochemistry, and CRC xenografts. We found that LINC02595 is upregulated in tumor tissues and blood samples of patients with CRC and CRC cell lines. Functional research found that LINC02595 promotes CRC cell growth, influences the cell cycle, and reduces apoptosis in vitro and vivo. Mechanistically, LINC02595 promoted BCL2‐like 1 (BCL2L1) expression through miR‐203b‐3p sponging. Our research demonstrated that LINC02595 is an oncogene in CRC and established the presence of a LINC02595‐miR‐203b‐BCL2L1 axis in CRC, which might provide a new diagnostic biomarker and therapeutic targets for the treatment of this disease.

Gene polymorphisms impact greatly on a person’s susceptibility to pulmonary tuberculosis (PTB). Macrophage receptor with collagenous structure (MARCO) and CD36 are two scavenger receptors (SRs) that can recognize Mycobacterium tuberculosis (Mtb) and play a key role in tuberculosis infection. Gene polymorphisms of MARCO and CD36 may contribute to tuberculosis risk. To investigate whether genetic polymorphisms of MARCO and CD36 are associated with susceptibility to PTB, genomic DNA samples from patients (n = 202) and healthy controls (n = 216) were collected and analyzed by polymerase chain reaction with high-resolution melting analysis. We studied two single nucleotide polymorphisms (SNPs) in MARCO (rs12998782 and rs17009726) and three SNPs in CD36 (rs1194182, rs3211956 and rs10499859). Rs12998782 (P = 0.018) might be associated with susceptibility to PTB. Rs1194182 (P < 0.01) and rs10499859 (P < 0.001) might be associated with resistance to PTB. Rs17009726 and rs3211956 were not associated with susceptibility/resistance to PTB. These data showed that MARCO rs12998782 may increase PTB risk while two SNPs of CD36, rs1194182 and rs10499859 may reduce the risk, indicating MARCO and CD36 as important receptors in response to PTB.

Prostate cancer, recognized as a “cold” tumor, has an immunosuppressive microenvironment in which regulatory T cells (Tregs) usually play a major role. Therefore, identifying a prognostic signature of Tregs has promising benefits of improving survival of prostate cancer patients. However, the traditional methods of Treg quantification usually suffer from bias and variability. Transcriptional characteristics have recently been found to have a predictive power for the infiltration of Tregs. Thus, a novel machine learning-based computational framework has been presented using Tregs and 19 other immune cell types using 42 purified immune cell datasets from GEO to identify Treg-specific mRNAs, and a prognostic signature of Tregs (named “TILTregSig”) consisting of five mRNAs (SOCS2, EGR1, RRM2, TPP1, and C11orf54) was developed and validated to monitor the prognosis of prostate cancer using the TCGA and ICGC datasets. The TILTregSig showed a stronger predictive power for tumor immunity compared with tumor mutation burden and glycolytic activity, which have been reported as immune predictors. Further analyses indicate that the TILTregSig might influence tumor immunity mainly by mediating tumor-infiltrating Tregs and could be a powerful predictor for Tregs in prostate cancer. Moreover, the TILTregSig showed a promising potential for predicting cancer immunotherapy (CIT) response in five CIT response datasets and therapeutic resistance in the GSCALite dataset in multiple cancers. Our TILTregSig derived from PBMCs makes it possible to achieve a straightforward, noninvasive, and inexpensive detection assay for prostate cancer compared with the current histopathological examination that requires invasive tissue puncture, which lays the foundation for the future development of a panel of different molecules in peripheral blood comprising a biomarker of prostate cancer.