Lung cancer and pulmonary tuberculosis are two significant public health problems that continue to take millions of lives each year. They may have similar symptoms and, in some cases, are diagnosed simultaneously or may have a causal relationship. In tuberculosis disease, the chronic inflammation, different produced molecules, genomic changes, and fibrosis are believed to be important factors that may promote carcinogenesis. As a reverse reaction, the development of carcinogenesis and the treatment may induce the reactivation of latent tuberculosis infection. Moreover, the recently used checkpoint inhibitors are a debatable subject since they help treat lung cancer but may lead to the reactivation of pulmonary tuberculosis and checkpoint-induced pneumonitis. Pulmonary rehabilitation is an effective intervention in post-tuberculosis patients and lung cancer patients and should be recommended to improve outcomes in these pathologies.

Background/Objectives: Immunotherapy has changed the therapeutic approach for various solid tumors, especially lung tumors, malignant melanoma, renal and urogenital carcinomas, demonstrating significant antitumor activity, with tolerable safety profiles and durable responses. However, not all patients benefit from immunotherapy, underscoring the need for predictive biomarkers that can identify those most likely to respond to treatment. Methods: The integration of predictive biomarkers into clinical practice for immune checkpoint inhibitors (ICI) holds great promise for personalized cancer treatment. Programmed death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), microsatellite instability (MSI), gene expression profiles and circulating tumor DNA (ctDNA) have shown potential in predicting ICI responses across various cancers. Results: Challenges such as standardization, validation, regulatory approval, and cost-effectiveness must be addressed to realize their full potential. Predictive biomarkers are crucial for optimizing the clinical use of ICIs in cancer therapy. Conclusions: While significant progress has been made, further research and collaboration among clinicians, researchers, and regulatory institutes are essential to overcome the challenges of clinical implementation. However, little is known about the relationship between local and systemic immune responses and the correlation with response to oncological therapies and patient survival.

Melanoma is a common and aggressive tumor originating from melanocytes. The increasing incidence of cutaneous melanoma in recent last decades highlights the need for predictive biomarkers studies. Melanoma development is a complex process, involving the interplay of genetic, epigenetic, and environmental factors. Genetic aberrations include BRAF, NRAS, NF1, MAP2K1/MAP2K2, KIT, GNAQ, GNA11, CDKN2A, TERT mutations, and translocations of kinases. Epigenetic alterations involve microRNAs, non-coding RNAs, histones modifications, and abnormal DNA methylations. Genetic aberrations and epigenetic marks are important as biomarkers for the diagnosis, prognosis, and prediction of disease recurrence, and for therapeutic targets. This review summarizes our current knowledge of the genomic and epigenetic changes in melanoma and discusses the latest scientific information.

Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota’s composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.

Breast cancer remains a leading cause of cancer-related mortality worldwide, with treatment resistance and tumor heterogeneity posing major clinical challenges. MicroRNAs (miRNAs), small non-coding RNAs regulating gene expression, have emerged as key players in breast cancer biology, influencing tumor initiation, progression, and therapy resistance. This narrative review synthesizes recent evidence on the involvement of miRNAs in breast cancer subtypes and their impact on treatment response. Notably, miR-155, miR-503, and miR-21 have shown potential as non-invasive biomarkers and modulators of pathways such as PI3K-Akt, MAPK, and TNF signaling. Additionally, exosomal miRNAs may reflect chemoresistance profiles and predict pathological response to neoadjuvant therapy. Emerging data also support the use of specific miRNAs to sensitize tumors to radiotherapy or modulate immune checkpoints like PD-L1 in triple-negative breast cancer. However, challenges persist regarding standardization, sample types, and study heterogeneity. Further translational research is needed to validate miRNA signatures and their utility in guiding personalized treatment. By highlighting mechanistic insights and potential clinical applications, this review aims to contribute to the ongoing efforts of integrating miRNAs into precision oncology for breast cancer.